Fleet management of unmanned aerial vehicles

Abstract

Fleet management of unmanned aerial vehicles (UAVs) is disclosed. Fleet management systems and methods may be implemented within UAVs, or systems utilizing UAVs, utilized in a delivery system configured to pick up a payload or a package at a shipping location and deliver a payload or package to a delivery location. The fleet management systems and methods may facilitate system operations of a UAV fleet, including, without limitation, positioning or deploying UAVs to meet or fill demand, prepositioning UAVs to meet a demand prediction, association delivery' orders to UAVs, order fulfillment, fleet maintenance management during operations, and flight path planning or generation.

Description

FLEET MANAGEMENT OF UNMANNED AERIAL VEHICLESCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 647,951. filed May 15. 2024, titled “Fleet Management of Unmanned Aerial Vehicles,” and to U.S. Provisional Patent Application No. 63 / 767,706, filed March 6, 2025, titled “Fleet Management of Unmanned Aerial Vehicles,” each of which is incorporated by reference herein, in the entirety and for all purposes.FIELD

[0002] The described embodiments relate generally to an unmanned aerial vehicle (UAV), such as one that may be used to deliver payloads (e.g., packages).BACKGROUND

[0003] Aerial vehicles, such as airplanes and unmanned vehicles (e.g. drones, UAVs, etc.), have many uses. Recently, aerial vehicles are becoming a viable option for package delivery vehicles. Such aerial vehicles can take many forms, such as. but not limited to, rotorcraft (e.g., helicopters, quadrotors, and so on) as well as fixed-wing aircraft. During delivery, all or portion of the flight commands to the aerial vehicle may be autonomously generated or executed.

[0004] As aerial vehicles are used more frequently for package deliveries, there is a need for improved overall systems that allow reliable delivery of a package to a deliverylocation. Further, there is a need for a system that provide an ability to safely cany a diverse array of goods in a wide range of environments to precise locations using autonomous delivery- systems that create a smooth and pleasant delivery- experience for a recipient.SUMMARY

[0005] In one example, a method for deploying one or more UAVs may include determining a demand profile by analyzing delivery- demand characteristics. The demand profile may include demand time and a geographic area for a payload characteristic. The delivery demand characteristics may include at least one of demographic information, recipient operational schedules, shipper operational schedules, social media data, weatherdata, news data, or event data. The method may include deploying the one or more UAVs to the geographic area within a threshold of the demand time for delivery of a payload including the payload characteristics. The one or more UAVs may be deployed based on a match between one or more UAV characteristics and one or more features of the demand profile.

[0006] In examples, the method may include readying the one or more UAVs for flight based on the demand time and geographic area. Readying the one or more UAVs may include at least one of charging a power source for the UAVs or preloading the payload into a UAV based on expected ordering demand. Deploying the one or more UAVs may include locating the one or more UAVs to charging or dock locations within a threshold distance of the geographic area. The locating the one or more UAVs may include flying the one or more UAVs to the charging or dock locations or transporting the UAVs via a vehicle transport to the charging or dock locations. The weather data may include weather event data and seasonal data. The event data may include a sporting event or large community event. Deploying the one of more UAVs may include assessing a fleet of UAVs based on anticipated deliver}' characteristics and an ability to meet the delivery characteristics, and based on the ability7to meet the delivery characteristics, selecting at least one of the one or more UAVs for deployment to the geographic area. The ability to meet the delivery characteristics may include at least one of a state of charge, a cargo capacity, a cargo type, environmental characteristics, or a current location. The environmental characteristics may include one or more weather conditions and one or more airspace restrictions. The delivery' demand characteristics may be analyzed by one or more machine learned models or clustering algorithms to identify demand patterns based on at least one of the geographic area, an industry, a demographic selection, a product type, or a business type. The demand profile may include a demand classification including one of a safety' critical, an urgent, and a nonurgent.

[0007] In one example, a method of managing a fleet of UAVs may include predicting a delivery demand volume for a geographic region within a demand window. The method may include positioning a plurality of UAVs in one or more locations within, or within a threshold distance from, the geographic region ahead of the demand window. Positioning the plurality of UAVs may include moving the UAVs from locations outside of the geographic region.

[0008] In examples, positioning the plurality of UAVs within the threshold distance may include positioning one or more UAVs at a UAV dock location or at a shipper location within the threshold distance. The method may include determining the plurality of UAVs for positioning from a fleet of UAVs, wherein the determination is based on cargo characteristics, power characteristics, prior location, or payload delivery characteristics. The power characteristics may include a charge time or a battery level. The method may include generating an incentive to delivery customers within the geographic region ahead of the demand window, and coordinating deliveries of payloads to the delivery' customers ahead of the demand window to arrange the positioning of the plurality of UAVs in the one or more locations. The incentive may include at least one of a cost reduction for a payload, a discount for one or more payloads, a giveaway, or a timing benefit.

[0009] In one example, a method of managing a fleet of UAVs may include predicting a demand location for deliveries by UAVs for a demand window. The method may include generating incentives for deliveries in the demand location ahead of the demand window. The method may include positioning UAVs in the demand location ahead of the demand window by transporting deliveries based on the generated incentives.

[0010] In examples, incentives may be targeted at both delivery' customers and payload providers.

[0011] In one example, a method of managing a fleet of UAVs may include detecting a capacity deficit in one or more UAVs within a geographic area based on an expected demand in the geographic area. The method may include transporting the one or more UAVs outside of the geographic area to rebalance a distribution of the fleet to offset the capacity deficit.

[0012] In one example, a method of fleet management for a fleet of UAV s may include determining a battery status for a plurality of UAVs. The method may include selecting a UAV from the plurality' of UAVs for a mission based on mission characteristics including flight distance, environmental factors, and the battery status.

[0013] In examples, the battery status may include a state of charge of a battery, a temperature of the battery, and a state of health of the battery.

[0014] In one example, a method of positioning charging stations for UAVs may include analyzing environmental characteristics and a delivery demand prediction for a geographic area. The method may include optimizing a location algorithm based on theanalysis. The method may include positioning the charging stations based on the optimization.

[0015] In examples, a home base charging station of the charging stations may be assigned to every UAV. The environmental characteristics may include at least two or more of air space limitations, ground obstacles, noise regulations, and weather conditions including one or more of fog, wind, precipitation, or temperature.

[0016] In one example, a system for management of a fleet of UAV s may include a plurality of observatory sources for receiving real time information regarding status and position information for UAVs within the fleet of UAVs. The system may include a dispatch module in communication with the plurality of observatory sources and a delivery database to dispatch selected UAVs to respective locations. The system may include path planning module in communication with the plurality of observatory sources and the dispatch module to generate flight paths for the selected UAVs.

[0017] In examples, the UAVs may communicate both between different UAVs directly and through one or more cellular communication pathways. The real time information may be received by the observatory sources via direct vehicle to vehicle communications between UAVs and through cellular communication between the UAVs and other observatory sources. The system may include a mapping module in communication with the path planning module to generate real time trajectories using the real time information from the observatory sources. The system may include a visualization module to generate a visual representation of the fleet of UAVs based on the plurality of observatory sources. The UAVs may transmit information to the plurality7of observatory sources based on a trigger, wherein the trigger includes a state change. The path planning module may include balancing a path risk with a path cost, wherein the path risk is weighted more than the path cost. The path risk may include an analysis of a risk to other UAVs or a ground risk. The ground risk may include a risk of flying over selected ground areas. The path planning module may receive real time weather information and airspace information. The path planning module may be further in communication with a traffic control module to request airspace for use by the UAVs. The traffic control module may be configured to create an assigned relationship between a dock or bay and each UAV of the selected UAVs. The assigned relationship may include a home base dock or bay and a contingency dock or bay. The assigned dock or bay may be a virtual bay7. The path planning module may be configured to determine one or more locations for a flight mode transition of the selected UAVs along the flight path.The path planning module may be configured to generate the flight paths for the selected UAVs based on a cost matrix. The path planning module may be configured to generate the flight paths in or on the cloud, wherein the selected UAVs are configured to make real time flight decisions based on the flight paths. The UAVs may autonomously navigate along the generated flight paths. The dispatch module may be configured to receive a mission request for a delivery order originating from a first location to be delivered to a second location, analyze fleet characteristics for a plurality of UAVs within a threshold of the first location, select a UAV from the plurality of UAVs to complete the mission request, receive a first flight path from a current location of the UAV to the first location, receive a second flight path from the first location to the second location, and dispatch, to the UAV, a mission including the first flight path and the second flight path. The plurality of observatory sources may receive fleet information from a plurality of docking locations, wherein the fleet information may include docked UAV charging information and dock throughput information. The observatory sources may include one or more docks that transmit weather information from their respective locations. The docks may transmit dock status information including one or more of a maintenance issue or a dock availability. The observatory sources may include one or more UAVs that transmit weather information as detected by the one or more UAVs. The path planning module may analyze airspace, risk factors for the airspace, distance, battery status, UAV degradation, and location to generate a flight path.

[0018] In one example, a system for management of a fleet of UAVs may include a path planning module configured to generate flight paths for the UAVs, wherein the path planning module is configured to generate the flight paths based on a cost function comprising different weighted costs.

[0019] In one example, a system for management of a fleet of UAVs may include a traffic control module configured to create an assigned relationship between a dock or bay and a UAV, and provide or define release policies associated with the assigned relationship. The UAV may be configured to release the assigned relationship based on the release policies.

[0020] In one example, a method of associating a payload with a delivery vehicle may include receiving a queue of orders for delivery'. The method may include detecting order characteristics for a completed order of the queue of orders. The method may include detecting delivery vehicle characteristics for the delivery vehicle receiving the completed order. The method may include associating the delivery' vehicle with the completedorder. The method may include updating a delivery time for the completed order based on flight characteristics of the delivery vehicle.

[0021] In examples, the order characteristics may be detected by capturing order information from a package of the completed order and associating the captured order information with order information in a fleet database. The order characteristics may be captured via a scanner or a camera. The delivery vehicle characteristics may be determined by capturing a vehicle identifier from the delivery vehicle before or as the delivery vehicle is associated with the completed order. The method may include transmitting a delivery' location to the delivery' vehicle after the delivery' vehicle is associated with the completed order. The method may include determining that the order characteristics do not match cargo characteristics for the delivery vehicle, and receiving a second completed order, wherein the second completed order is associated with the delivery vehicle and the delivery time is for the second completed order. The order characteristics may include a weight and the cargo characteristics may include a weight maximum for cargo.

[0022] In one example, a method of assigning a UAV to a delivery order may include determining one or more order characteristics for the delivery order. The method may include comparing the one or more order characteristics to UAV characteristics for a plurality of UAVs. The method may include selecting, from the plurality of UAVS, a UAV for the delivery order based on the UAV characteristics most matching the order characteristics.

[0023] In examples, the one or more order characteristics may include at least one of a delivery location, a delivery distance, an order temperature, an order ty pe, a pickup location, or a delivery window. The one or more order characteristics may include a demand profile, wherein the demand profile may include a location demand.

[0024] In one example, a method of arranging delivery orders may include receiving order characteristics for a delivery' order. The method may include associating the delivery order with a delivery vehicle. The method may include analyzing vehicle characteristics and delivery characteristics with respect to the delivery order to determine a match for delivery' criteria. When there is a match, the method may include transmitting the delivery' order to a delivery' location via the delivery' vehicle.

[0025] In examples, the vehicle characteristics may include cargo characteristics including insulation, prior cargo, and capacity’. The delivery characteristics may include optimal transportation temperature, product type, and priority.

[0026] In one example, a method of validating a delivery payload for delivery by a UAV may include receiving payload characteristics as the delivery payload is prepared for delivery. The method may include assigning the delivery payload to a first UAV. The method may include comparing first cargo characteristics of the first UAV with the payload characteristics. The method may include determining that the first cargo characteristics are not validated with the payload characteristics. The method may include assigning the delivery payload to a second UAV.

[0027] In examples, the first cargo characteristics may include at least one of insulation properties, cooling properties, hazmat characteristics, or food characteristics.

[0028] In one example, a method of managing a fleet of UAVs may include positioning UAVs in different geographic locations based on an estimated delivery demand. The method may include dispatching the UAVs to payload pickup locations. The payload pickup locations may partially overlap with the geographic locations. The UAVs may navigate between a respective geographic location and a respective payload pickup location autonomously. The method may include tracking the UAVs to the payload pickup locations. The method may include assigning the UAVs to delivery locations based on payload delivery characteristics.

[0029] In examples, tracking the UAVs may include receiving tracking information from the UAVs and from docks for receiving the UAVs, wherein the docks are positioned in the geographic locations and the payload pickup locations. The method may include generating paths for the UAVs from the payload pickup locations to the delivery locations, wherein the paths are based in part on available air space, weather, vehicle capabilities, known obstacles, and payload delivery characteristics. The paths may be generated based on an optimization of battery’ status, distance, air space characteristics, and route risk. The method may include dispatching a set of UAVs to updated geographic locations based on a rebalance assessment determined based on current positions of the UAVs.

[0030] In one example, a method for receiving a delivery’ location for a payload may include receiving a delivery request for a delivery’ area for the payload. The method may’ include analyzing a viability of the delivery’ request. The method may include displaying one or more delivery' location options to a delivery customer. The method may include receiving a preference for a delivery' location option of the one or more delivery location options. The method may include setting the delivery location option as the delivery location for the payload.

[0031] In examples, displaying the one or more delivery location options may include displaying an image corresponding to the delivery area, and receiving a user identification of the one or more delivery location options within the delivery area. The user identification may include pixel information on the image. The user identification may include a textual input regarding the image. Displaying the one or more delivery7location options may include displaying a textual description of the one or more delivery7location options relative to the delivery area. The textual description may include at least one of front yard, back yard, side yard, deck, or patio. Receiving the preference for the delivery location option may include receiving a textual input from the user describing the delivery location option.

[0032] In one example, a method of tracking a delivery order may include receiving delivery order characteristics including a shipping location and a delivery location. The method may include determining that the delivery order is deployed to a delivery vehicle. The method may include estimating a flight time for the delivery' vehicle from the shipping location to the delivery location based on known flight conditions including at least estimated speed, environmental characteristics, vehicle capabilities, and payload characteristics. The method may include displaying the estimated flight time on a user device associated with the delivery' order.

[0033] In examples, the method may include displaying a three dimensional orientation of the delivery vehicle as the delivery vehicle flies from the shipping location to the delivery location. The method may include displaying a flight speed on the user device. The method may include determining there is an unexpected flight impairment along a route between the shipping location and the delivery location, and updating the flight time based on the unexpected flight impairment. The unexpected flight impairment may include a flying obstacle, a change in environmental characteristics, or a change in vehicle capabilities. The knoyvn flight conditions may include a battery' state of charge, estimated weather, and keep out zones within an air space. Determining that the delivery' order is deployed to a delivery’ vehicle may include receiving packing data associating the delivery order with the delivery vehicle, wherein the packing data is detected as the delivery order is coupled to the delivery vehicle. The packing data may be received by capturing information of the delivery' vehicle and information of the delivery' order as the delivery order is attached to the delivery' vehicle.

[0034] In one example, a method of generating a delivery time estimate for an order may include determining a shipper throughput capacity at a shipping location for theorder. The shipper throughput capacity may be based on a dock utilization, an expected charge time for UAVs at the shipping location, a fleet throughput in the region, an expected order preparation time for the shipping location, and an expected loading time for loading orders into a UAV. The method may include determining order characteristics of the order. The method may include estimating a delivery time based on the shipper throughput capacity and the order characteristics.

[0035] In examples, the order characteristics may include a delivery location. The order characteristics may include a priority value for the order.

[0036] In one example, a method for fleet management of a fleet of UAVs may include receiving a queue of payloads for delivery' via the fleet of UAVs. The method may include assessing payload characteristics of the pay loads within the queue to determine an order priority for the payloads. The method may include assigning UAVs from the fleet of UAVs to a pickup location based on the order priority7.

[0037] In examples, the payload characteristics may include at least one of a payload type or a payload delivery promise.

[0038] In one example, a method for fleet management of a fleet of UAV s may include receiving a queue of payloads for delivery. The method may include deploying one or more UAVs to a pickup location based on the queue of payloads. The method may include determining that a UAV has received a payload based on packing information. The method may include assigning a mission to the UAV to deliver the payload to a customer based on payload characteristics received within the packing information.

[0039] In examples, the UAV may be configured to receive any pay load of the queue of pay loads and is capable of completing a mission for any payload of the queue of payloads. The one or more UAVs deployed to the pickup location may be agnostic with respect to receiving any payload from the queue of payloads. The packing information may be received based on payload information transmitted from a shipping device as or after the payload has been coupled to the UAV.

[0040] In one example, a method for managing a delivery service may include determining a demand window for delivery of a payload. The method may include transmitting at least one notification to at least one user regarding an incentive to modify a delivery time of the pay load from within the demand window to outside of the demand window. The method may include updating the delivery time based on a receipt of acceptance of the incentive.

[0041] In examples, the method may include updating a fleet deployment schedule for one or more delivery’ vehicles based on the demand window and a number of accepted incentives across multiple orders. The demand window may be based on a predicted demand or a current demand. The predicted demand may be determined by analyzing delivery demand characteristics comprising at least one of social media data, weather data, news data, and event data to determine a demand time and a geographic area for a payload characteristic. The incentive may be a price reduction for a shipping cost of the payload, and wherein the at least one user is a payload supplier. The at least one user may be a delivery recipient, and wherein the incentive is a discounted price for the payload or a future payload.

[0042] In one example, a path planning module for a UAV fleet may be configured to analyze a plurality of fleet characteristics, order characteristics, and airspace characteristics to generate a flight trajectory' for a flight mission. The fleet characteristics may include real time data from at least a subset of the UAVs within the fleet. The path planning module may be configured to transmit the flight trajectory to a UAV.

[0043] In examples, a cost function may be used to produce the flight trajectory.

[0044] In one example, a method for fleet management of a fleet of UAVs may include determining a flight path for a UAV based on a battery' state of charge of the UAV and a delivery location for a payload carried by the UAV. The flight path may include a charging waypoint between a shipping location and the delivery location. The method may include activating the UAV on the flight path based on a delivery window for the payload is sufficient to enable a charge at the charging waypoint.

[0045] In one example, a method for determining a dock location may include analyzing a plurality of delivery routes and a utilization of the plurality of the delivery routes. The method may include determining an average position along highly utilized delivery routes. The method may include positioning a dock in the average position.

[0046] In examples, the method may include positioning the dock based on a number of delivery vehicles in the area and vehicle capabilities of the delivery vehicles.

[0047] In one example, a method for managing a fleet of UAVs may’ include receiving flight history' and maintenance information from UAVs. The method may include determining a subset of the UAVs are above a maintenance threshold. The method may include relocating the subset of the UAVs to a maintenance bay based on the determination.

[0048] In addition to the exemplary aspects and embodiments described above, further aspects and embodiments will become apparent by reference to the drawings and by study of the following description.BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The disclosure will be readily understood by the following detailed description in conjunction with the accompanying drawings, wherein like reference numerals designate like structural elements, and in which:

[0050] FIG. 1 illustrates an example UAV.

[0051] FIG. 2 illustrates an example UAV including a first aerial vehicle and a second aerial vehicle deployed from the first aerial vehicle.

[0052] FIG. 3 illustrates an example docking assembly for a UAV.

[0053] FIG. 4 illustrates a schematic diagram of an example aerial vehicle system.

[0054] FIG. 5 illustrates a flowchart for an example method of deploying a UAV based on a demand prediction.

[0055] FIG. 6 illustrates a flowchart for an example method of managing a fleet of UAVs.

[0056] FIG. 7 illustrates a flowchart for another example method of managing a fleet of UAVs.

[0057] FIG. 8 illustrates a flowchart for another example method of managing a fleet of UAVs.

[0058] FIG. 9 illustrates a flow chart for another example method of managing a fleet of UAVs.

[0059] FIG. 10 illustrates a flowchart for an example method of positioning charging stations for UAVs.

[0060] FIG. 11 illustrates a flowchart for an example method of managing a delivery service.

[0061] FIG. 12 illustrates a flowchart for an example method of associating a payload with a delivery' vehicle.

[0062] FIG. 13 illustrates a flowchart for an example method of assigning a UAV to a delivery order.

[0063] FIG. 14 illustrates a flowchart for an example method of arranging deliveryorders.

[0064] FIG. 15 illustrates a flowchart for an example method of validating a delivery payload for delivery by a UAV.

[0065] FIG. 16 illustrates a flowchart for an example method of managing a fleet of UAVs.

[0066] FIG. 17 illustrates a flowchart for an example method of receiving a deliver}' location for a payload.

[0067] FIG. 18 illustrates a flowchart for an example method of tracking a delivery order.

[0068] FIG. 19 illustrates a flowchart for an example method of generating a deliver ' time estimate for an order.

[0069] FIG. 20 illustrates a flowchart for an example method for fleet management of a fleet of UAVs.

[0070] FIG. 21 illustrates a flowchart for another example method for fleet management of a fleet of UAVs.

[0071] FIG. 22 illustrates a flowchart for another example method for fleet management for a fleet of UAVs.

[0072] FIG. 23 illustrates a flowchart for an example method for determining a dock location.

[0073] FIG. 24 illustrates a flowchart for an example method of managing a fleet of UAVs.

[0074] FIG. 25 illustrates a schematic diagram of an example fleet management system for implementing various embodiments in the examples described herein.

[0075] FIG. 26 illustrates a schematic diagram of an example computer system for implementing various embodiments in the examples described herein.DETAILED DESCRIPTION

[0076] The description that follows includes sample systems, methods, and apparatuses that embody various elements of the present disclosure. However, it should be understood that the described disclosure may be practiced in a variety of forms in addition to those described herein.

[0077] The examples described herein are generally directed to methods and systems that enable fleet management of UAVs, e g., vehicle movements, payload pickups and deliveries, charging positioning and timing, and other related functions to control and deploy a plurality of UAVs to operate a logistics platform. The examples disclosedherein facilitate system operations of a UAV fleet. Such system operations include, without limitation, positioning or deploying UAVs to meet or fdl demand, prepositioning UAVs to meet a demand prediction, association delivery orders to UAVs, order fulfdlment, fleet maintenance management during operations, and flight path planning or generation, such operations help to ensure efficient and timely operation of the fleet, as well as satisfying customer expectations regarding timeliness, as well as predict and manage demand to ensure capacity and the like.

[0078] The fleet management systems and methods may be implemented within UAVs and / or systems utilizing UAVs (e.g., an aerial system). The UAVs may be utilized in a delivery system configured to pick up a payload or a package at a shipping location (e.g., warehouse, restaurant, retail, or other location) and deliver a payload or package to a delivery location (e.g., customer location, residential area, or the like). It should be noted that while various features and components are discussed with respect to UAVs or UAV systems, the features and components can be used separately from the UAV and / or in various combinations with each other. As such the discussion of any particular implementation is meant as illustrative only. Further, various methods may be implemented in other logistic and delivery platforms, such as those employing other autonomous vehicles, such as automobiles and / or driver or operator dependent or partially dependent systems, such as standard automobiles or the like. As such, the discussion of any particular embodiment is meant as illustrative only.

[0079] The fleet management system may include one or more modules distributed over a network (e.g., in or on the cloud). For example, a fleet management module in the cloud may communicate with and control one or more UAVs, such as to manage a fleet of UAVs (e.g., vehicle movements, payload pickups and deliveries, charging positioning and timing, and other related functions to control and deploy a plurality of UAVs to operate a logistics platform). The fleet may communicate with each other and the fleet management module over a network, such as to coordinate distribution of UAVs in a location or area, position UAVs as needed to address current and anticipated demand, coordinate pickup and delivery of orders, generate flight paths, associate or assign orders to particular UAVs, set delivery locations, prioritize order fulfillment, coordinate UAV charging and other maintenance during operations, etc.

[0080] An example aerial system may include a first aerial vehicle (e.g.. a main or carrier aerial vehicle) and a second aerial vehicle (e.g., a dependent aerial vehicle). In these instances, the first aerial vehicle may act to support and / or transport the secondaerial vehicle to a location and the second aerial vehicle can be deployed from the first aerial vehicle, e.g., the second aerial vehicle can be deployed from a first height and descend to a second height or location, such as to deliver a package. In some embodiments, the first aerial vehicle may be able to remain at a high location, such as by hovering, and may reduce the noise, disruption, and safety7risks experienced by humans and animals on the ground or delivery location as the second vehicle may be quieter than the first aerial vehicle. In some examples, the first aerial vehicle can be optimized for longer flight paths and the second aerial vehicle can be optimized for an enhanced delivery experience to a human on the ground or delivery location. The payload may be a good or package including consumer goods, food, medical supplies, or other items. Additional variations of the first aerial vehicle and / or the second aerial vehicle may include types of rotorcraft (e.g., helicopters, quadrotors, and so on) or similar vehicles that generate thrust for movement through air, as well as fixed-wing aerial vehicles. In other examples, the system may include a payload that is directly delivered by a main or first aerial vehicle, e.g., deployed from a first height, released at the ground without the use of a dependent second vehicle, etc.

[0081] The locations may include a retail, restaurant, wholesale, industrial, mail carrier, or other site in which payloads and packages are processed for delivery7to a customer. The delivery location may include a package location designated as a specific portion of a building or area, such as a door, a window, a deck, a roof, a parking area, or other locations accessible by a delivery recipient.

[0082] The first aerial vehicle may include flight assemblies enabling different types of flight. For example, the first aerial vehicle may include fixed wings and a cruise propeller configured to forward or cruise flight motion and may also include one or more propeller assemblies configured for hover or similar motion. The cruise propeller may be articulable (e.g. rotatable) between a cruise flight and hover flight position.

[0083] In many embodiments, the second aerial vehicle may be coupled to the first aerial vehicle, e.g., by a tether, cable, or the like. In some embodiments, the second aerial vehicle may be stowed within a portion of the first aerial vehicle, such as a cavity or bay, for a first portion of the mission and deployed and retracted for a second portion of the mission.

[0084] The second aerial vehicle may have separate drive abilities, allowing the second aerial vehicle to steer itself without or to supplement steering by the first aerial vehicle. For example, the second aerial vehicle may include a propulsion assemblyallowing the second aerial vehicle to move relative to the first aerial vehicle. The propulsion assembly may include thrusters to provide active control or enable generation of thrust irrespective of the position of the second aerial vehicle. Alternatively, the second aerial vehicle may be dependent completely on the main vehicle or first aerial vehicle.

[0085] The body of the second aerial vehicle may include a volume or feature to house the payload, e.g., a payload bay. In some examples, the payload bay may be accessible through one or more apertures by one or more selectively openable assemblies, such as a lid or doors, to place a payload in the bay or to remove the payload from the bay at the delivery location. In examples without the second aerial vehicle, the main or first aerial vehicle may include such or similar features to house, receive, or otherwise hold the payload and for removal or delivery of the payload at the delivery location.

[0086] The aerial vehicles may include one or more sensors that collect data to assist in the operation of the aerial vehicle. For example, the aerial vehicles may include a plurality of sensor assemblies such as audio or sound sensors, visual sensors such as cameras, or the like. The various sensors may be used in combination with processing elements or other systems to navigate. For example, the aerial vehicle may be operated remotely or completely autonomously. The sensors may detect information regarding the environment, such as obstacles, weather information, and the like, and may include a computer and / or be in communication with a processor, to allow the aerial vehicles to make decisions regarding flight, docking, and landing.

[0087] The various computers or processors in communication with the sensors may be associated with machine learned models, databases, or the like to assist in categorizing or understanding sensed information. For example, a machine learned model may be trained to identify objects in images captured by the sensors and the various computing elements may identify flight paths or maneuvers based on the identified objects, or positions of the objects relative to the aerial vehicles. The first aerial vehicle and second aerial vehicle may be in operative communication with each other, such as through wireless networks, cell networks, radio frequencies, wired, or other communication methods.

[0088] Reference will now be made to the accompanying drawings, which assist in illustrating various features of the present disclosure. The following description is presented for purposes of illustration and description. Furthermore, the description is not intended to limit the inventive aspects to the forms disclosed herein. Consequently,variations and modifications commensurate with the following teachings, and skill and knowledge of the relevant art. are within the scope of the present inventive aspects.

[0089] Any of the features, components, and / or systems, including the arrangements and configurations thereof, shown in or described with reference to any one respective figure or set of figures can be included, either alone or in any combination, in any of the other examples shown in or described with reference to another figure or set of figures. Thus, any and all features described herein may be implemented alone or in any combination, irrespective of the figure or process in which the feature is described. AERIAL VEHICLE SYSTEM OVERVIEW

[0090] FIGS. 1-2 illustrate an example UAV 100 or aerial vehicle delivery' system. The UAV 100 includes various components and systems enabling aerial delivery of a payload, such as packages, food, or other items, to a delivery location. For example, the UAV 100 may be configured for flights over a distance, such as from a pickup location to the delivery location, while carrying or retaining a payload for delivery7. At a desired location, such as at the delivery location, the UAV 100 may deliver the pay load, such as through deployment of a portion of the UAV 100, as described herein.

[0091] The UAV 100 may include a first or primary aerial vehicle 102 (hereinafter “first aerial vehicle’'). The first aerial vehicle 102 may include a fuselage 106 that defines a housing or body for the first aerial vehicle 102. The fuselage 106 may include a nose 110 portion forming a front end of the first aerial vehicle 102 and a tail 112 portion forming a rear end of the first aerial vehicle 102. The fuselage 106 may generally taper as it extends towards both the tail 112 and the nose 110, although in other configurations, the fuselage 106 may be differently configured. The fuselage 106 may be configured to store various components of the first aerial vehicle 102, such as a payload and / or a second or secondary aerial vehicle 120 (hereinafter “second aerial vehicle”). For example, a vehicle compartment 124 may be defined as a cavity within the fuselage 106, such as on a bottom surface of the fuselage 106, to receive the second aerial vehicle 120 therein. The fuselage 106 (and / or other components of the first aerial vehicle 102) may be configured to include aesthetically pleasing features and elements.

[0092] The first aerial vehicle 102 may' include one or more sensors that collect data to assist in the operation of the first aerial vehicle 102. For example, the first aerial vehicle 102 may be operated remotely or completely autonomously by detecting information regarding the environment, such as obstacles, weather information, and the like, and may include a computer and / or be in communication with a processor, to allow the first aerialvehicle 102 to make decisions regarding flight, docking, and landing. With respect to docking, the first aerial vehicle 102 may include a docking assembly 130. The docking assembly 130 may include various structure (e.g., a fin 134 or other structure) to be received or partially inserted within a dock for the first aerial vehicle 102, as described below.

[0093] The first aerial vehicle 102 may include a wing assembly 140 and a tail wing assembly 142. The wing assembly 140 may include multiple wings coupled to the fuselage 106 (e g., a pair of wings extending from opposite sides of the fuselage 106). The wings may be fixed in position relative to the fuselage 106 and configured to enable a cruise or forward flight motion of the first aerial vehicle 102. The tail wing assembly 142 may extend from or be otherwise coupled to the tail 112. The tail wing assembly 142 may function as a stabilizer for the first aerial vehicle 102 to help stabilize the first aerial vehicle 102 during flight. In one example, the tail wing assembly 142 may be arranged in a V-structure to provide both horizontal and vertical stabilization, but in other embodiments may be differently configured.

[0094] The first aerial vehicle 102 may include one or more propulsion systems (e.g.. propeller assemblies) to both propel the first aerial vehicle 102 in a first flight motion (e.g., forward flight) as well as a second flight motion, such as a hover position and / or multi-dimensional flight. In one example, the first aerial vehicle 102 may include a main propeller assembly 150. The main propeller assembly 150 may include multiple propeller assemblies, with the propeller assemblies coupled to booms or otherwise configured to be positioned spaced apart from the fuselage 106. A rear or tail propeller assembly 152 may be coupled to the fuselage 106, such as to the tail 112. The tail propeller assembly 152 may propel the first aerial vehicle 102 in forward flight and / or hover or multidimensional flight. For example, the tail propeller assembly 152 may be articulable or movable based on the desired flight for the first aerial vehicle 102.

[0095] Other examples of the first aerial vehicle 102 may be found in International Patent Application No. PCT / US2024 / 014632, filed on February 28, 2024, and titled “Aerial Vehicle and Aerial Vehicle Systems,’7the disclosure of which is hereby incorporated by reference in its entirety.

[0096] The second aerial vehicle 120 may be selectively attached to and / or stored within the first aerial vehicle 102 during flight to or from the delivery' location. In such examples, the first aerial vehicle 102 provides propulsion for flights over a distance, such as from a pickup location to the delivery location. During such flight, the second aerialvehicle 120 may be atached to, partially inside, nested, or otherwise stored within the first aerial vehicle 102 in a retracted or stowed position. At a desired location, such as at the delivery location, the second aerial vehicle 120 may be deployed from the first aerial vehicle 102 to deliver a pay load.

[0097] At the delivery location, the propulsion system of the first aerial vehicle 102 may enable hovering flight over the delivery location at a first altitude or height above the delivery location (e.g.. may include cruise and hover propellers). The second aerial vehicle 120 may be deployed from the first aerial vehicle 102 at the first altitude to the delivery location. For example, the second aerial vehicle 120 may be released and allowed (e.g., using gravitational force) to descend downwards from the first aerial vehicle 102. After delivery, the second aerial vehicle 120 may be retracted back into the first aerial vehicle 102. Both the descent and the ascent may be controlled by a retraction assembly (e.g., a tether 160) or another mechanism.

[0098] The second aerial vehicle 120 may nest in the vehicle compartment 124. The nesting may result in a more secure stowing of the second aerial vehicle 120. When the second aerial vehicle 120 is stowed in the first aerial vehicle 102, the botom surface of the second aerial vehicle 120 may align with the bottom surface of the first aerial vehicle 102 to define a single botom surface. When stowed, the second aerial vehicle 120 may provide additional stability or assist in balancing the first aerial vehicle 102 during forward flight. For example, the vehicle compartment 124 may be defined around a center of gravity of the first aerial vehicle 102. When stowed, a center of gravity of the second aerial vehicle 120 may be positioned to correspond with the center of gravity' of the first aerial vehicle 102.

[0099] The second aerial vehicle 120 may include one or more propulsion assemblies 600 to maneuver the second aerial vehicle 120 relative to the first aerial vehicle 102. In one example, the propulsion assemblies 600 are arranged to provide forward and / or side- to-side movement of the second aerial vehicle 120, or rotation of the second aerial vehicle 120 about the tether 160.

[0100] Either or both of the first aerial vehicle 102 or the second aerial vehicle 120 may include one or more processing elements and / or sensors to load / deliver a package and / or to traverse a flight path from the pickup location to the delivery location, to the pickup location or a service station, or to a designated or predetermined site. The first aerial vehicle 102 or the second aerial vehicle 120 may be autonomous, partially autonomous, or navigated by a user from a controlling location. The UAV 100 mayoperate in rural or urban locations. Accordingly, the first aerial vehicle 102 may deploy the second aerial vehicle 120 in a variety of locations and environmental conditions.

[0101] Either or both of the pickup location or the deliver}’ location may be a warehouse, restaurant, retail store, service center, residential building or a similar location where delivery services may be utilized. A delivery’ recipient may designate the delivery location as a specific portion of a building or area, such as a door, a window, a deck, a roof, a parking area, or other locations accessible by a delivery recipient.

[0102] Other examples of the second aerial vehicle 120 may be found in International Patent Application No. PCT / US2024 / 018347, filed on March 4, 2024, and titled "Autonomous Delivery Vehicle and System,"’ the disclosure of which is hereby incorporated by reference in its entirety.

[0103] FIG. 3 illustrates an example docking system 300 for one or more aerial vehicles (e.g., the UAV 100). The docking system 300 may include at least one dock 302 or more than one dock, such as docks 302 and 304 that mechanically retain UAVs 306 and 308. respectively (e g., secure the UAVs in a docked configuration). In examples, the docks 302 and 304 may provide electrical connections (e.g., power and / or data) to the UAVs 306 and 308 to, for example, charge batteries of the UAVs 306 and 308 and / or provide mission information or other useful data to the UAVs 306 and 308. Each of UAV 306 and UAV 308 may be similar to the UAV 100. described above. To secure each UAV 306 or 308 to a dock 302 or 304, the docking assembly 130 of the first aerial vehicle 102 may be latched to the dock 302 or 304.

[0104] The docks 302 and 304 may be mounted on or otherwise held in place by a support 310. The support 310 may include a vertical tower 312 and arms 314 and 316. When held in place by the support 310, the docks 302 and 304 are generally positioned to receive UAVs 306 and 308. Although FIG. 3 is discussed with respect to two docks, in other instances, fewer (e.g., a single dock) or more than two docks may be coupled to the tower 312 or other support structure that supports the docks relative to the ground or other support surface and / or building.

[0105] In various examples, each dock 302 and 304 may be configured to charge a UAV and / or may be configured to allow a UAV to unload and / or receive payload via a loading assembly 318. For example, dock 302 may be a charging dock, such that UAV 306 may receive electrical power (e.g., to charge batteries) via the dock 302. Dock 304 may be a loading dock, which may or may not provide charging capabilities to UAV 308. However, the dock 304 is generally placed relative to the loading assembly 318such that the UAV 308 may utilize the loading assembly 318 to receive and / or deliver payload. For example, the second aerial vehicle 120 may descend from the first aerial vehicle 102 and pass through a chute 320 (or passage, tube, ramp) of the loading assembly 318 to deliver payload to, and / or receive payload from, the inside of the building adjacent to the docking system 300. In examples, the docking system 300 may include a barrier 324. The barrier 324 may be a platform or net placed around an opening to the chute 320.

[0106] In various examples, docking assemblies may include different numbers of charging and / or loading docks, multiple towers, and the like. Other examples of the docking system 300 may be found in International Patent Application No.PCT / US2024 / 016087. filed on February 16, 2024 and titled "‘Docking Configurations for Aerial Vehicles,’’ the disclosure of which is hereby incorporated by reference in its entirety. In general the docks or other storage and / or charging locations may act as a housing location for the aerial vehicles, provide power thereto, and / or connect the aerial vehicles with a payload loading assembly and the examples described herein

[0107] FIG. 4 illustrates a schematic diagram of an example aerial vehicle system 400. The aerial vehicle system 400 may include the UAV 100, such as the first aerial vehicle 102 and the second aerial vehicle 120. The aerial vehicle system 400 may include a network 410 through which the one or more devices of the aerial vehicle system 400 may communicate. The devices or components of the aerial vehicle system 400 may be communicatively or operatively coupled with each other, or one or more computing systems to enable autonomous operation or navigation of the first aerial vehicle 102 and / or the second aerial vehicle 120.

[0108] The aerial vehicle system 400 may include a fleet management system 414 or fleet management module in operative communication with the UAV 100, such as the first aerial vehicle 102 and / or the second aerial vehicle 120. In examples, the fleet management system 414 or fleet management module may organize and / or control the distribution of UAVs in a location. For example, the fleet management system 414 may receive delivery requests and identify one or more UAVs to fulfill the delivery requests, as well as positioning UAVs as needed to address anticipated demand, maintenance schedules, and the like. Accordingly, the fleet management system 414 may issue commands to the first aerial vehicle 102 and / or the second aerial vehicle 120 (either directly or indirectly), or other devices of aerial vehicle system 400, to begin operation.

[0109] The aerial vehicle system 400 may include a flight controller 416. The flight controller 416 may generate or update flight paths for the UAV 100. For example, the flight controller 416 may determine flight paths between two or more locations, such as between a shipper or retail location and a delivery location. In examples, the flight controller 416 may determine all aspects of the flight path. In other examples, the flight controller 416 may determine portions of the flight path, such as the start and end points or corridors open for travel by the UAV 100. In such examples, the UAV 100 (e.g., the first aerial vehicle 102 and / or the second aerial vehicle 120) may determine the remaining portions of the flight path. In some examples, the flight controller 416 may determine a portion of the maneuvers for the UAV 100 to navigate a flight path.

[0110] The aerial vehicle system 400 may include a server 418 including one or more databases or for executing one or more computing operations. The server 418 may be a computing system including one or more processing elements for storing information or executing one or more operations of the aerial vehicle system 400. For example, the server 418 may include a database of relevant information for flight, such as weather, map data, information on a delivery environment or delivery location, or the like. In examples, as described herein, machine learned models may be used by the system for autonomous or partially autonomous navigation of the UAV 100, including the first aerial vehicle 102 and / or the second aerial vehicle 120. The machine learned models or the training data for the models may be located at or executed by the server 418.[OH l] The aerial vehicle system 400 may include or be in communication with a remote device 422. The remote device 422 may be a user device or an operator device. As an operator device, the remote device 422 may receive information from the aerial vehicle system 400 or inputs and commands for communication to the aerial vehicle system 400. For example, an operator may use the remote device 422 to resolve conflicts in the aerial vehicle system 400, correct or update flight paths, or the like. As a user device, the remote device 422 may generate delivery requests to the aerial vehicle system 400 or provide other inputs to the aerial vehicle system 400. The delivery requests may identify payloads for delivery, or locations to receive and / or deliver payloads. The delivery requests may also include information relating to the delivery, such as time to deliver, pickup locations, delivery' locations, or the like.

[0112] While reference is made to single and separate devices herein, it is appreciated that multiples of the same device may be included, such as multiple aerial vehicles, multiple servers 418, or multiple flight controllers 416, and the like. Similarly, thevarious devices, such as the flight controller 416, remote device 422, server 418. or fleet management system 414 may be a single device or unit or may be multiple devices which may be distributed at one or more physical or virtual locations.

[0113] The first aerial vehicle 102 may include propulsion and flight control systems 430. The propulsion and flight control systems 430 may include the propulsion systems / assemblies described above, such as the main propeller assembly 150 and the tail propeller assembly 152. or other features related to the flight or physical control of the first aerial vehicle 102. The first aerial vehicle 102 may include a retraction assembly 434 for selectively deploying and retracting the second aerial vehicle 120. The retraction assembly 434 may include tether 160 and a selective locking mechanism, or other features related to coupling or controlling the second aerial vehicle 120 by the first aerial vehicle 102, as described above or in International Patent Application No.PCT / US2024 / 014632 or International Patent Application No. PCT / US2024 / 018347, incorporated by reference herein.

[0114] The second aerial vehicle 120 may include propulsion and flight control systems 438 for independent movement of the second aerial vehicle 120 relative to the first aerial vehicle 102, such as to navigate the second aerial vehicle 120 to a payload location. The propulsion and flight control systems 438 may include the propulsion systems / assemblies described above, such as the propulsion assemblies 600, or other features related to the flight or physical control of the second aerial vehicle 120. The second aerial vehicle 120 may include payload release systems 442 providing the selective storage and release of pay loads for deliveries.

[0115] Either or both of the first aerial vehicle 102 and the second aerial vehicle 120 may include communication systems 446, 450 for transferring information between the first aerial vehicle 102 and the second aerial vehicle 120, and / or to one or more of the devices of aerial vehicle system 400. The communication systems 446, 450 may be wired and / or wireless systems. For example, the first and second aerial vehicles 102, 120 may communicate over short or long ranges. In some examples, the first and second aerial vehicles 102, 120 may communicate by Bluetooth. Wi-Fi, cellular communication, radio communication, satellite, or the like, without intent to limit.

[0116] Either or both of the first aerial vehicle 102 and the second aerial vehicle 120 may include sensors or sensor assemblies 454, 458. such as those described herein. The sensors 454. 458 may include audio sensors and / or visual sensors. The sensors 454, 458 may also include various sensors that detect or determine force, orientation, acceleration,location, elevation, temperature, and / or similar characteristics of the first aerial vehicle 102, the second aerial vehicle 120, and / or the delivery environment.

[0117] Either or both of the first aerial vehicle 102 and the second aerial vehicle 120 may include navigation systems 462, 466. The navigations systems 462, 466 may determine locations, orientations, or movements of the first aerial vehicle 102 and / or the second aerial vehicle 120. For example, navigation system 462 may be in communication with sensors 454 and / or 458 to determine the location or heading of the first aerial vehicle 102 and / or the second aerial vehicle 120. Similarly, navigation system 466 may be in communication with sensors 454 and / or 458 to determine the location or heading of the second aerial vehicle 120 and / or the first aerial vehicle 102. The navigations systems 462, 466 may be in communication with global satellite systems (e.g. GPS or GNSS) to determine delivery locations, conformity with flight paths, or the like.

[0118] Either or both of the first aerial vehicle 102 and the second aerial vehicle 120 may include computing elements or resources 470, 474. The computing elements 470. 474 may include one or more processors configured to receive information (such as from on-board and / or off-board sensors) and make decisions based on the information, such as directing the first aerial vehicle 102 and / or the second aerial vehicle 120 to fly to certain locations or the like. The computing element may be operatively or communicatively coupled with the communication systems 446, 450. which may allow the computing elements 470, 474 to receive information from off-board sources, such as the flight controller 416, fleet management system 414, server 418, remote device 422, or other devices of aerial vehicle system 400. In examples, the computing elements 470, 474 may be operatively or communicatively coupled with the sensors 454, 458. The computing elements 470, 474 may interpret information gathered by the sensors 454, 458, such as to identify objects, features, or other characteristics of the surroundings. In examples, the computing elements 470, 474 may interpret the information gathered by the sensors 454, 458 to determine a flight path of the first aerial vehicle 102 and / or the second aerial vehicle 120.UAV POSITIONING

[0119] One or more UAVs 100 may be utilized to pick up and deliver pay loads, such as packages including consumer goods, food, medical supplies, and other items. To improve system efficiency, such as to reduce delivery’ time, or reduce a time to pick up, among other inefficiencies, one or more UAVs 100 may be deployed to geographic areas(e.g., near a shipping location, near a pickup location, near areas of high utilization, etc.) based on a demand prediction. For example, certain geographic areas may exhibit higher utilization or demand for UAV package delivery, such as during certain times of day (e.g., evening, lunchtime, etc.), days of the week (e.g., weekends, Mondays, bad or good weather days, etc.), days of the year (e.g., holidays, sporting events, large community7events, etc.), or otherwise. The predicted demand may be determined through analysis of various data sources, including third party sources (e.g., social media data, weather data, news data, event data), intrinsic sources (e g., historical demand), as well retailer or shipper information (e.g., product orders, anticipated shipments, etc.), and other sources, such as to determine a demand time and a geographic area for the demand.

[0120] To reduce operational inefficiencies, such as those mentioned above, one or more UAVs 100 may be deployed to the geographic areas to meet anticipated or predicted demand. For example, one or more UAVs 100 may be positioned before shippers request the UAVs 100. In examples, a fleet of UAVs 100 may be prepositioned to anticipate demand. For example, one or multiple UAVs 100 may be positioned within or near a geographic region ahead of a demand window for the geographic region. For example, ahead of or based on a large sporting event (e.g., ahead of the event’s starting time, halftime or another intermission, conclusion, etc.) UAVs 100 may be prepositioned, such as by select restaurants, to meet anticipated demand (e.g., near restaurants with chicken wings that are typically ordered during sporting events, etc.) and with ty pical or associated fare for such deliveries.

[0121] A demand profile may be determined for a geographic area or location. The demand profile may be determined based on factors that impact whether a shipper is requesting or needing a UAV 100, and / or whether a customer is requesting or needing a delivery (e.g., based on shipper order history, anticipated or observed demand spikes, etc.). The demand profile may include a time (e.g., a window or range of times) for the estimated demand as well as a volume or capacity7demand. For example, the demand profile may include information about when, where, and the amount of delivery capacity7that may be needed.

[0122] The demand profile may be matched with one or more UAVs 100, and the UAVs 100 may be arranged for the demand. For example, the demand profile may include types of delivery7and select UAVs 100 with those characteristics may be matched with the demand. As another example, the demand matching may include selecting a number of UAVs 100 that can be positioned in the demand area at or duringthe demand window (e.g., are close by or within a threshold distance). The UAVs 100 may be proactively moved from areas of lower demand to areas of higher demand, such as based on anticipated demand. The UAVs 100 may be moved (e.g., proactively) to account for vehicle deficiencies for an expected demand. For example, a fleet of UAVs 100 may be rebalanced, such that UAVs 100 in each geographic area or region can fulfill expected demand. In examples, UAV(s) 100 may be selected for a mission based on mission characteristics, including flight distance, environmental factors, and battery status, among other features.

[0123] In examples, incentives may be generated (e.g., to delivery customers and / or payload providers, such as through discounts or awards), such as through push notifications, in-app notifications, text messages, etc. (e.g.. geographically targeted notification or message in a target area). In one example, incentives may be generated to drive positioning of a fleet of UAVs 100 to a geographic region about to experience high demand (e.g., ahead of a demand window for the geographic region / are), but without having non-payload carrying flights (e.g.. empty UAVs). In another example, one or multiples UAVs 100 may be prepositioned in an area before the incentives are sent for driving or creating demand in the area. In such examples, the system may ensure anticipated demand can be filled before generating incentives (e.g., asking people to place an order). As a result, the generated incentives may scale with actual capacity in the area. In some examples, incentives may be generated to maintain or adjust operation of one or more UAVs 100 in a particular region, such as to maintain operation of UAVs 100 (e.g., fill the time) in a particular area when demand is low adjust delivery times based on demand, etc. For example, incentives may be generated and transmitted to users / delivery recipients to modify a delivery time of their order, such as to steer UAV utilization or modify' a demand curvature. The incentives may include economic incentives (e.g., cost reductions, discounted prices, rewards, giveaways, etc.), environmental incentives (e.g., reduced carbon footprint, etc.), or recognition incentives (e.g., awards, marketing, etc.), among others.

[0124] Along these lines. FIGS. 5-10 illustrate example methods of positioning or deploying one or more UAVs 100 to meet anticipated demand. For example, FIG. 5 illustrates one example method 500 of deploying one or more UAVs 100 based on demand prediction. In block 510, a demand profile is determined by analyzing deliverydemand characteristics. The demand profile may include a demand time for a payload characteristic. For example, delivery customers may request certain payloads or delivery'orders during certain times (e.g., evening, lunchtime, weekends, holidays, around sporting events, etc.). The demand profile may include a geographic area for a payload characteristic, e.g., city, neighborhood, address, or the like and often may include a distance threshold around the identified area as well. For example, delivery requests may be concentrated to set geographic regions or locations, such as tied to sporting events, large community events, etc. The demand profile may include a demand classification including one of a safety critical, an urgent, and a non-urgent.

[0125] The delivery demand characteristics may include at least one of demographic information, recipient operational schedules, shipper operational schedules, shipper order history, social media data, weather data, news data, or event data. The weather data may include weather event data and seasonal data. The event data may include a sporting event or large community event. The delivery demand characteristics may be analyzed by one or more machine learned models or clustering algorithms to identify demand patterns based on at least one of the geographic area, an industry, a demographic selection, a product type, or a business type.

[0126] In block 520, one or more UAVs 100 are readied for flight. The UAV(s) 100 may be readied based on the demand time and geographic area. For example, additional UAVs 100 may be prepared for flight in anticipation of an upcoming demand in a certain geographic area (e.g., dinner time in the suburbs, lunch rush in a commercial area, etc.). Block 520 may include at least one of charging a power source of the UAVs 100 or preloading the payload into a UAV 100 based on expected ordering demand. For example, items that are typically or expected to ordered during a certain demand window may be preloaded into one or more UAVs 100 to meet the anticipated demand.

[0127] In block 530, the one or more UAVs 100 are deployed to the geographic area based on the demand profile. Block 530 may include deploying the one or more UAVs 100 to the geographic area within a threshold of the demand time for delivery of a payload including the payload characteristics. The threshold may account for flight time of the UAVs 100 from their current locations to the geographic area, such that the UAVs 100 arrive at the geographic area ahead of the demand time or as the demand time begins. In other examples, the threshold may be a determined window of time relative to the demand time, such as within a certain amount of minutes, seconds, or other measurements of time relative to the start of the demand time (e.g., + / - 5 minutes of the start of the demand time, before the start of the demand time, etc.). The one or moreUAVs 100 may be deployed based on a match between one or more UAV characteristicsand one or more features of the demand profile. For example, only UAVs 100 having characteristics that match (or are within a threshold of similarity to) the demand profile may be deployed to the geographic area. For instance, UAVs 100 positioned near the area, having the required payload capacity (individually or collectively), having the required battery capacity' or state of charge, or having other characteristics to satisfy the demand profile may be matched and deployed to the geographic area. Block 530 may include locating the one or more UAVs 100 to charging or dock locations within a threshold distance of the geographic area. The threshold distance may a determined distance from the geographic area, such as to allow the UAVs 100 to sendee the geographic area within the demand window or time. Locating the UAVs 100 may include directing the UAVs 100 to fly or navigating them to the charging or dock locations within the geographic area (or sufficiently close to enable service of the geographic area). Locating the UAVs 100 may include transporting the UAVs 100 via a vehicle transport to the charging or dock locations.

[0128] In examples, block 530 may include assessing a fleet of UAVs 100 based on anticipated delivery characteristics and an ability to meet the delivery characteristics. The ability to meet the delivery characteristics may include at least one of a state of charge, a cargo capacity7, a cargo ty pe, environmental characteristics, or a current location of the UAV fleet. The environmental characteristics may include one or more weather conditions and one or more airspace restrictions. Block 530 may further include selecting at least one of the one or more UAVs 100 for deployment to the geographic area based on the ability' to meet the delivery' characteristics. As a result, the UAV(s) 100 deployed to the geographic area may be able to fulfill the required deliveries. As one example, UAVs 100 may be prepositioned near restaurants (e.g.. near anticipated customers) to handle food rushes, such as a food demand at lunch or dinner time. In another example, UAVs 100 may be repositioned near a medical facility, such as based on historical ordering data for a medical laboratory, to handle orders in and out of the laboratory' during peak hours (e.g., between 2:00PM and 5:00PM). In yet another example, UAVs 100 may be deployed to handle requests around large sporting events (e.g., to meet a typical demand for chicken wings, party dips, chips, or other food items during the Super Bowl).

[0129] FIG. 6 illustrates an example method 600 of managing a fleet of UAVs 100. In block 610. a delivery demand volume for a geographic region within a demand window may be predicted, such as in a manner as described above with reference to FIG. 5. Forexample, a demand window for deliveries within a geographic region may be predicted based on an analysis of past deliveries, operational schedules, social media data, weather data, news data, event data, or other information. The delivery demand volume may be a predicted volume of deliveries to be made during the demand window, such as based on intrinsic or extrinsic data sources or history.

[0130] In block 620, a plurality of UAVs 100 for positioning from a fleet are determined. For example, multiple UAVs 100 may be selected based on a prediction that the selected UAVs 100 will be able to fulfill or otherwise meet or fill the delivery demand volume. The determination may be based on cargo characteristics, power characteristics, prior location, or payload delivery' characteristics of the UAVs 100. For example, individual UAVs 100 with capacity to cany’ and deliver the expected orders may be determined or selected, with the number of selected UAVs 100 able to fill the delivery demand volume (e.g., a sufficient number of UAVs 100 is selected to fulfill all expected orders within an expected delivery' window). The power characteristics may include a charge time or a battery level.

[0131] In block 630, the plurality of UAVs 100 are positioned in or near the geographic region ahead of the demand window. For example, the plurality of UAVs 100 may be positioned in one or more locations within, or within a threshold distance from, the geographic region ahead of the demand window . Block 630 may include moving one or more UAVs 100 from locations outside of the geographic region. For example, one or multiple UAVs 100 located outside of the geographic region may be moved into or near the geographic region ahead of the demand window. Block 630 may include positioning one or more UAVs 100 at a UAV dock location (e.g., dock 302 or 304) or at a shipper location within the threshold distance. In examples, if fleet capacity is at or approaching capacity, one or multiple UAVs 100 may be brought into the area for the demand.

[0132] In block 640, an incentive to delivery^ customers within the geographic region is generated ahead of the demand window'. The incentive may include at least one of a cost reduction for a payload, a discount for one or more payloads, a giveaway, or a timing benefit, among other incentives described herein. For example, a quicker delivery time may be provided or offered to delivery' customers located at waypoints along a flight path. The incentive may be generated through an application, such as via an in-app notification, a text message, or the like. The incentive may be provided to the delivery customer, such as to encourage orders, or the incentive may be provided to the shipper, such as to encourage shipments or for the shipper to pass through the incentive to theircustomers. For example, customers in a suburb area may receive an alert or other notification that they can receive a discount on orders placed for delivery’ within a small window (e.g., within the next hour, within the next two hours, etc.), such as to take advantage of the UAVs 100 positioned in the area (e.g., to use the UAVs 100 to deliver pay loads in certain areas ahead of the anticipated demand).

[0133] In block 650, deliveries of pay loads may be coordinated to the delivery’ customers within the geographic region ahead of the demand window. The deliveries may be coordinated to arrange the positioning of the plurality' of UAVs 100 in the one or more locations within, or within a threshold distance from, the geographic region ahead of the demand window. Such examples may drive positioning of multiple UAVs 100 to the geographic region, such that the UAVs 100 are prepositioned for or ahead of the demand window.

[0134] FIG. 7 illustrates another example method 700 of managing a fleet of UAVs 100. In block 710, a demand location is predicted for deliveries by UAVs 100 for or within a demand window, such as in a manner as described herein. For example, a demand window for deliveries within or at a demand location (e.g., a geographic area, a geographic region, a certain location, etc.) may be predicted based on an analysis of past deliveries, operational schedules, social media data, weather data, news data, event data, or other information, as described herein.

[0135] In block 720, incentives are generated for deliveries in the demand location ahead of the demand window. The incentives may be targeted at both delivery customers and payload providers. The incentives can include cost discounts, giveaways, or time savings, among other incentives described herein. In examples, the incentives can be provided through push notifications, in-app notifications, text messages, etc. (e.g., geographically targeted notification or message in a target area to increase delivery requests in the target area). In examples, an order queue may' be evaluated to determine if scheduled orders can be delivered early or if unscheduled orders can be opportunistically fulfilled. For example, unscheduled orders can be used to fill the time when other demand is low.

[0136] In block 730, UAVs 100 are positioned in the demand location ahead of the demand window based on the generated incentives. For example, UAVs 100 may be positioned in the demand location ahead of the demand window by transporting deliveries resulting from the generated incentives. As a result, UAVs 100 may be steered to the demand location or area while opportunistically fulfilling additional orders.

[0137] FIG. 8 illustrates an example method 800 of managing a delivery' service. In block 810. a demand window is determined for delivery of a payload. The demand window may be based on a predicted demand or a current demand. The predicted demand may be determined by analyzing delivery demand characteristics. The delivery demand characteristics may include at least one of social media data, weather data, news data, and event data to determine a demand time and a geographic area for a payload characteristic. For example, intrinsic and extrinsic sources may be analyzed to determine predicted rain storms or other weather, start and stop times of sporting or community events, locations of mass social media posts and live streaming, video feed locations, or the like. This and other information may be used to predict demand windows and volume. For example, predicted storms may indicate an increased demand for home deliveries and for certain products (e.g., comfort foods and items, weather appropriate clothing, etc.) in the area of the storm. Increased social media attention to a particular event or product may also indicate or cause an increased demand at the event or for the particular product.

[0138] In block 820, at least one notification is transmitted to at least one user regarding an incentive to modify a delivery time of the payload, such as in a manner as described herein. For example, the notification may be a push notification, an in-app notification, a text message, or the like. The incentive may be associated with modifying the delivery time from within the demand window to outside of the demand window. The incentive may be a price reduction for a shipping cost of the payload. In such example, the user may be a payload supplier. In other examples, the user may be a delivery recipient, and in such examples, the incentive may be a discounted price for the payload or a future payload.

[0139] In block 830, the delivery time is updated based on an acceptance of the incentive. For example, the delivery time may be updated (e.g., in the system, an app, etc.) based on a receipt of acceptance of the incentive by the at least one user.

[0140] In block 840, a fleet deployment schedule for one or more delivery vehicles is updated. The fleet deployment schedule may hst or contain the deliveries (e.g., all deliveries) to be completed by the delivery vehicles in the fleet, such as within a period of time (e.g., within the hour, for the day, etc.). The fleet deployment schedule may be updated based on the demand window- and a number of accepted incentives across multiple orders.

[0141] FIG. 9 illustrates another example method 900 of managing a fleet of UAVs 100. In block 910. a capacity deficit is detected in one or more UAVs 100 within a geographic area. The detected capacity deficit may be based on an expected demand in the geographic area. Example capacity deficits may include a lack of cargo volume, a lack of cargo carrying capacity, or a lack of vehicle capability to meet the characteristics of the expected demand, among other deficits. For example, delivery orders or payloads from the expected demand may include characteristics disqualifying a UAV 100 or multiple UAVs 100 within the geographic area, such that the UAV(s) 100 cannot fulfill the delivery orders or payloads (e.g., are not capable of delivering the orders or payloads).

[0142] In block 920, the deficient UAVs 100 are transported outside of the geographic area. The UAVs 100 may be transported outside the geographic area to rebalance a distribution of the fleet to offset the capacity deficit. For example, the deficient UAVs 100 may be transported to areas in which they can fulfill orders. In addition, transporting the deficient UAVs 100 outside of the geographic area may allow capable UAVs 100 to replace the deficient UAVs 100 in the geographic area, such as to meet the expected demand.

[0143] FIG. 10 illustrates another example method 1000 of managing a fleet of UAVs 100. In block 1010, a battery status is determined for a plurality of UAVs 100. The battery status may include a state of charge of a battery, a temperature of the battery, and a state of health of each battery of the UAVs 100. The battery status may be determined based on a battery7management system (BMS), historical data, predictive analysis, etc.

[0144] In block 1020, a UAV 100 is selected for a mission based on mission characteristics. The mission characteristics may include at least the battery status. For example, the selected UAV 100 may have a battery state of charge or health sufficient or necessary for the mission. In examples, the mission characteristics include flight distance, environmental factors, and the battery status. In such examples, the battery state of charge or health may be sufficient to support operations of the UAV 100 during the entire flight, and with expected environmental factors (e.g., expected wind loads, temperatures, etc ).UAV - PAYLOAD PAIRING

[0145] In examples, a payload, such as an order for delivery, may be paired to or with one or more delivery vehicles. For example, an order may be assigned, coupled, or otherwise associated to one or more UAVs 100 for delivery. The assignment orassociation of an order to a UAV 100 or other delivery vehicle may be based on characteristics of the order (“order characteristics’' or “payload characteristics’"). Example characteristics of the order may include weight, volume, temperature requirements, handling requirements, and delivery standards or speed, among other characteristics of the order itself, or any combination thereof. In examples, the assignment or association of an order to a delivery vehicle may be based on characteristics of the delivery vehicle (“delivery vehicle characteristics”; “UAV characteristics”; or “vehicle characteristics”). Example characteristics of the delivery vehicle may include cargo characteristics / limits (e.g., weight limits or maximums, volume limits, etc ), special purpose configurations or capabilities (e g., cold delivery capable, hot delivery capable, insulation properties, cooling properties, hazmat characteristics, food limitations or characteristics, etc.), battery state of charge, vehicle status or capability, or maintenance schedule, among other characteristics of the delivery vehicle itself, or any combination thereof.

[0146] In such examples, a UAV 100 having vehicle characteristics matching or most closely matching the characteristics of the order may be selected or associated to the particular order. Conversely, an order may not be associated with a UAV 100 that cannot fulfill the order, such as due to vehicle deficiencies, a lack of vehicle capability, or the vehicle characteristics not matching the order characteristics (e.g. non-hazmat capable). As a result, a particular UAV 100 may be validated to or with the order or payload. Once the orders are associated to capable UAVs 100 (e.g., validated), the UAVs 100 may transmit or deliver the orders to their respective delivery locations. In some embodiments, the order deliveries may be tracked, such as to update delivery' times or otherwise verify the deliveries are completed. In this manner, the delivery of orders maybe arranged or fulfilled.

[0147] Along these lines, FIGS. 11-15 illustrate example methods of delivery order and flight association (e.g., UAV - payload pairing). For example, FIG. 11 illustrates an example method 1100 of associating a payload with a delivery vehicle. In block 1110. a queue of orders for delivery is received. The queue may include a list of delivery' requests for a geographic area, for an order delivery service (e.g., system 400), or the like. The queue may be received over a network, via direct communication, through a delivery service, or the like.

[0148] In block 1120. order characteristics are detected for a completed order of the queue of orders. The order characteristics may be detected by capturing orderinformation from a package of the completed order and associating the captured order information with order information in a fleet database. For example, the order characteristics may be captured via a scanner or a camera. The order characteristics may include information related to the order and / or the items of the order (e.g., based on item or order level), including delivery time, payload characteristics, priority7status, and other order characteristics described herein. For example, the order characteristics may include a weight, volume, temperature, etc.

[0149] In examples, the order characteristics may be determined, such as based on a database of items within the order and / or input by the shipper. In examples, certain characteristics of the order (e.g., item weight, item volume, item dimensions, first information, etc.) may be retrieved from a first database, whereas other characteristics of the order (e.g., shipping temperature, handling requirements, second information, etc.) may be retrieved from a second database. In examples, the first information may be provided by shippers or retailers, and the second information may be gathered or collected by the fleet management system, such as over time based on experience. In examples, the payload may be determined or defined based on order characteristics. For example, the payload may be limited to items of similar temperature or type, such as limiting the pay load to only hot items of the order, cold items of the order, perishable items of the order, non-perishable items of the order, etc. In other examples, the system may limit the items that can be ordered together. For example, the system (e.g., an application) may allow only hot items to be ordered together, or only cold items to be ordered together, etc. In such examples, the assessment or analysis of the individual items may be limited (e.g., to improve efficiency, reduce computational overhead, etc.), such that only the total volume or weight of the order is of concern.

[0150] In block 1130, delivery vehicle characteristics are detected for the deliver}7vehicle receiving the completed order. The delivery vehicle characteristics may be characteristics of the delivery vehicle itself, such as the vehicle’s capabilities, configuration, limits, and status, among others described herein. The delivery vehicle characteristics may be determined by capturing a vehicle identifier (ID) from the delivery vehicle before or as the deliver}7vehicle is associated with the completed order. For example, the order characteristics may be captured via a scanner or a camera. In examples, the delivery vehicle characteristics may be determined based on a database (e.g., a manufacturer’s database, a database of characteristics, etc.), such as the system querying the database for the vehicle ID

[0151] In block 1140, the delivery' vehicle is associated with the completed order. For example, the delivery vehicle and completed order may be paired, assigned, or otherwise tied together in an order fulfillment system, a logistics system, or a fleet management system. The order may be associated with the delivery vehicle at loading. In such examples, characteristics of the order may be verified. For instance, the actual weight of the packaged order may be determined, such as by the user packing the order or by the delivery vehicle itself (e.g., using the retraction assembly or winch to estimate or determine weight based on force). The actual characteristics of the order (e.g., actual weight) may be input into the system, such as automatically by the delivery vehicle when the actual weight is determined by the retraction assembly or other means. Such information may be used to configure the delivery vehicle, such as the actual weight used to tune motors and perform other functions. In examples, a notification may be generated to indicate the delivery vehicle is associated with the completed order. For instance, a notification may provide that a specific payload was loaded into a specific UAV 100, such as through a barcode being scanned and associated with the vehicle ID. The notification may be generated through an application, such as via an in-app notification, a text message, or the like. As a result of the association, delivery of the completed order may be provided by the associated delivery' vehicle only.

[0152] In block 1150. a delivery location is transmitted to the delivery vehicle. The delivery location may be transmitted to the delivery’ vehicle after the delivery vehicle is associated with the completed order. The delivery' location may be transmitted to the delivery' vehicle with the order (e.g., the order contains delivery' location information). The delivery location may be the customer's address or a geographic area designated by the customer (e.g., front yard, back yard, side yard, patio, driveway, or a location thereof, etc.), such as in a manner as described herein.

[0153] In block 1160, a delivery time for the completed order is updated based on flight characteristics of the delivery vehicle. For example, the delivery time may be updated as a result of vehicle / battery degradation, unexpected flight impairments, operation anomalies of the delivery vehicle, such as in a manner as described herein. In some examples, the delivery' time may be updated based on a difference between predicted and actual weights of the order. For instance, the delivery' time may be updated if the actual weight of the order is more or less than a predicted weight when the order was requested.

[0154] Block 1170 includes determining that the order characteristics do not match cargo characteristics for the delivery vehicle. The cargo characteristics may be related to cargo capacities, limitations, or capabilities of the delivery vehicle. For example, the cargo characteristics may include a weight maximum for cargo, a volume maximum for cargo, or a cargo type limitation, among other characteristics described herein, or any combination thereof. The order characteristics may be compared to the cargo characteristics, such as to determine whether the delivery vehicle can deliver or carry the completed order based on the cargo characteristics. If the delivery vehicle’s cargo characteristics do not match the order characteristics, the completed order may be assigned to a different delivery vehicle to complete the order. For example, if the completed order contains hazardous materials (“hazmaf ’) and the delivery vehicle is not capable of carrying hazmat of (e.g., non-hazmat capable), then the completed order may be assigned to a different deliver}' vehicle that is hazmat capable. As another example, if the completed order contains temperature dependent items (e.g., hot items, cold items, etc.) and the delivery vehicle is not capable of providing the needed temperature, then the completed order may be assigned to a different delivery vehicle having the needed capabilities (e.g., cold delivery capable, hot delivery capable, etc.). As yet another example, if the delivery' vehicle is not large or powerful enough for the completed order (e.g., the completed order is too large or heavy for the delivery vehicle), then the completed order may be assigned to a different vehicle having the needed capabilities. As yet another example, block 1 170 may consider perception capabilities of the delivery' vehicle. For example, the completed order may be assigned to a vehicle with adequate perception capabilities to fly in certain locations / risk areas (e.g., in fog, under low light conditions, etc.).

[0155] In block 1180, a second completed order is received. If the delivery vehicle cannot deliver the completed order, such as due to the cargo characteristics of the delivery vehicle not matching the order characteristics of the completed order, the delivery vehicle may be assigned a different order. For example, the second completed order may be associated with the delivery vehicle. In such examples, the provided delivery time may be for the second completed order.

[0156] FIG. 12 illustrates an example method 1200 of assigning a UAV 100 to a delivery order. In block 1210, one or more order characteristics are determined for the delivery order, such as in a manner as described herein. For example, an order characteristic may be detected or captured from a package, or determined based on thedelivery order itself. The order characteristics may include deliver}' time, payload characteristics (e.g., size and weight), priority status, and other order characteristics described herein.

[0157] In block 1220, the one or more order characteristics are compared to UAV characteristics for a plurality of UAVs 100. The order characteristics may include at least one of a delivery’ location, a delivery distance, an order temperature, an order type, a pickup location, or a delivery window, among other characteristics described herein. In examples, the order characteristics may include a demand profile. The demand profile may be similar to that described above, including at least one of a location demand, a demand time or window, or a demand classification. The UAV characteristics may be characteristics of the UAV 100 itself, such as the UAV’s capabilities, configuration, limits, and status, among others described herein.

[0158] In block 1230, a UAV 100 is selected from the plurality of UAVs 100 for the delivery order based on the UAV characteristics matching or most closely matching the order characteristics. The selection may be based on best fit, a perfect match, or other selection criteria. As a result, the selected UAV 100 may be the one most suited for the mission or job.

[0159] FIG. 13 illustrates an example method 1300 of arranging deliver}' orders. In block 1310, order characteristics are received for a deliver ' order, such as in a manner as described herein. For example, order characteristics may be detected, captured, or provided by a shipper or fulfillment system. The order characteristics may include delivery time, payload characteristics (e.g., size and weight), priority status, and other order characteristics described herein.

[0160] In block 1320. the delivery order is associated with a delivery vehicle, such as in a manner as described herein. For example, the delivery order may be paired, assigned, or otherwise tied to a deliver}' vehicle in an order fulfillment system, a logistics system, or a fleet management system.

[0161] In block 1330. vehicle characteristics and delivery characteristics are analyzed with respect to the delivery order to determine a match for delivery criteria. The vehicle characteristics may include cargo characteristics including insulation, prior cargo, and capacity. The deliver}' characteristics may include optimal transportation temperature, product type, and priority. The vehicle characteristics may be compared to the deliver}' characteristics, such as to determine whether the delivery vehicle can deliver, carry, or otherwise fulfill the deliver}' order. If the vehicle characteristics do not match thedelivery characteristics, the delivery order may be assigned to a different delivery vehicle to complete the order, such as in a manner as described herein.

[0162] When a match is determined, block 1340 includes transmitting or transiting the delivery order to a delivery location via the delivery vehicle. For example, the UAV 100 may fly from the pickup or shipping location to the delivery7location, such as autonomously or semi-autonomously. In examples, imagery may be used to confirm safe flight conditions prior to launch or takeoff. For example, an image from dock or UAV 100 may be used to confirm that the airspace around the UAV 100 is clear. At the delivery location, the order may be delivered by the UAV 100. For example, the second aerial vehicle 120 may be deployed from the first aerial vehicle 102 to deliver the order, such as in a manner as described herein. For instance, the first aerial vehicle 102 may hover over the delivery location, and the second aerial vehicle 120 may be lowered from the first aerial vehicle 102 to deliver the order. In examples, imagery7may be used to confirm delivery or the delivery area. For example, if the second aerial vehicle 120 needs assistance in determining whether it is safe to delivery its payload, an image of the delivery area may be sent to a fleet management system. The image and other information about the delivery7site (e.g., mapping information, satellite imagery, previously collected imagery7, etc.) may be reviewed (e.g., by a human operator). In examples, an operator may select on the image where the package should be delivered, such as based on the image and whatever information is provided by the customer (e.g., previous customer feedback or specific intent). In examples, such human operator data may be used to train a model, such as for reinforcement learning, for future deliveries. In examples, the imagery of the delivery site may be used as survey data to compile an image of the operating area. In examples, an image of the delivered package may be provided to the customer as proof of delivery. In examples, the customer may validate that they7are receiving delivery, such as associating customer device location data with location data of the second aerial vehicle 120. After delivery, the second aerial vehicle 120 may be retracted back into the first aerial vehicle 102. After delivery, the UAV 100 may return to the same shipping location, a different shipping location, or a dock.

[0163] FIG. 14 illustrates an example method 1400 of validating a delivery payload for delivery7by a UAV 100. In block 1410, payload characteristics are received as the delivery payload is prepared for delivery. The payload characteristics may include features of the delivery payload (e.g., a delivery7order) itself, such as volume, weight, ortemperature, among other order characteristics described herein, or any combination thereof.

[0164] In block 1420, the delivery payload is assigned to a first UAV (e.g., a first UAV 100). For example, the delivery payload may be paired, assigned, tied, or otherwise associated to the first UAV in an order fulfillment system, a logistics system, or a fleet management system.

[0165] In block 1430. cargo characteristics of the first UAV are compared with the payload characteristics. The cargo characteristics may include at least one of insulation properties, cooling properties, hazmat characteristics, or food characteristics. Comparing the cargo characteristics with the payload characteristics may determine whether the first UAV can deliver or carry the delivery payload.

[0166] In block 1440, the cargo characteristics are validated with the payload characteristics. For example, block 1440 may include determining that the cargo characteristics are not validated with the payload characteristics, such as based on the cargo characteristics not matching the payload characteristics. If the cargo characteristics of the first UAV do not match the pay load characteristics, block 1440 may determine that the cargo characteristics are not validated with the payload characteristics.

[0167] In block 1450, the delivery payload is assigned to a second UAV (e.g., a second UAV 100), such as based on the determination that the cargo characteristics of the first UAV are not validated with the payload characteristics. In such examples, the delivery payload may be assigned to the second UAV to complete delivery of the delivery payload.

[0168] FIG. 15 illustrates an example method 1500 of managing a fleet of UAVs 100. In block 1510. UAVs 100 are positioned in different geographic locations based on estimated delivery demand, such as in a manner as described herein. For example, one or more UAVs 100 may be selectively positioned (e.g., in a targeted manner) to meet anticipated demand, such as to ensure predicted delivery requests or orders can be fulfilled. In examples, one or more UAVs 100 may be prepositioned for the estimated delivery demand, such as ahead of a delivery demand window, as described herein.

[0169] In block 1520, the UAVs 100 are dispatched to payload pickup locations. The payload pickup locations may be shipping locations, shipper locations, or other areas for the UAVs 100 to pick up or receive a payload (e.g., dock 302 or 304 of docking assembly 300). The payload pickup locations may partially overlap with the geographic locations. For example, the payload pickup locations may be at or located near thegeographic locations at which the UAVs 100 are positioned to anticipate demand. In examples, the UAVs 100 may navigate between a respective geographic location and a respective payload pickup location autonomously. For example, a delivery request may trigger a UAV 100 to autonomously navigate from its current location to the payload pickup location associated with the request.

[0170] In block 1530. the UAVs 100 are tracked to the payload pickup locations. For example, the positions of the UAVs 100 may be tracked by satellite GPS (e.g.. using navigations systems 462, 466). In examples, block 1530 may include receiving tracking information from the UAVs 100 and from docks (e.g., dock 302 or 304) for receiving the UAVs 100. The docks may be positioned in the geographic locations and the payload pickup locations.

[0171] In block 1540, the UAVs 100 are assigned to delivery locations based on payload delivery characteristics, such as in a manner as described herein. For example, the pay load delivery characteristics may include one or more characteristics of the payload, including a delivery address and a delivery time, among others described herein. The UAVs 100 may be assigned locations for delivering their payload, including the delivery address and an area at the delivery address for placing the payload (e g., front yard, back yard, driveway, etc.).

[0172] In block 1550. paths (e.g.. flight paths) are generated for the UAVs 100 from the payload pickup locations to the delivery locations. The paths may be based in part on available air space, weather, vehicle capabilities, known obstacles, and payload delivery characteristics. The vehicle capabilities may include a reduced charge or a degradation of general flight performance. The paths may be generated based on an optimization of battery status, distance, air space characteristics, and route risk.

[0173] In block 1560, a set of UAVs 100 is dispatched to updated geographic locations based on a rebalance assessment. For example, one or more UAVs 100 may be deployed or sent to other areas experiencing or about to experience increased demand. The rebalance assessment may be determined based on current positions of the UAVs 100. For example, the nearest UAVs 100 may be dispatched to the updated geographic locations. Additionally, or alternatively, the current positions of the UAVs 100 may indicate an excess number of UAVs 100 in a particular area or location. In such examples, the excess UAVs 100 may be sent to different locations to rebalance the fleet.ORDER FULFILLMENT

[0174] In examples, data related to delivery requests and fulfillment may be managed by a fleet management system (“FMS”) or fleet core service, such that the FMS is able to track and manage things in the real world. For example, the FMS may receive information from a customer platform, such as orders to be fulfilled, pickup information, payload information, delivery information, etc. The FMS may use this information to make delivery decisions, such as commanding delivery vehicles (e.g., UAVs 100) where to go to pick up and deliver payloads. The customer platform may be capable of “listening” to, or requesting, the information in the FMS (e.g., delivery' updates broadcast within the FMS) to provide updates to the customer, such as to update delivery' times, vehicle / system status, etc.

[0175] In examples, one or more delivery' characteristics of a payload / order may be set by a user (e.g., a delivery customer) and / or the delivery' customer may be kept apprised of order fulfillment. For example, delivery' location options may be provided to a delivery customer, and the delivery customer may identify a preference for delivery (e.g., front yard, back yard, side yard. deck, patio, porch, etc.). The delivery location or request may be validated, such as to verify that the specific UAV f OO can deliver the specific order or payload to the delivery' location (e.g., based on vehicle capabilities, payload characteristics, environmental characteristics, etc ).

[0176] A delivery time may be estimated and provided to the delivery customer, such as upon order placement. The delivery time estimate may be based on system capacity and capabilities (e.g., number of available and operational UAVs 100, order preparation and loading times, UAV flight speed, etc.) and / or order priority' (e.g., high priority orders will be delivered before low priority’ orders, orders that can be fulfilled will be prioritized over those that cannot with the existing UAV fleet, etc.). To facilitate delivery of orders irrespective of their payload characteristics, the UAVs 100 deployed to pickup locations may be agnostic to receive any payload from a queue of orders, such that each UAV 100 at a pickup location is capable of delivering any order in the queue. The assignment of one or more UAVs 100 to a pickup location may be based on order priority (e.g., UAVs 100 are first sent to pickup locations having orders of high priority).

[0177] The order delivery may be tracked, and any updates or changes to the delivery' time may be provided to the delivery customer. For example, an order may be tracked from order receipt, to packing of the order in a UAV 100, and to delivery of the packed order to the customer. The estimated delivery time may be updated or revised (e.g..dynamically in real time or near real time) based on a detection of an unexpected flight impairment that slows order delivery. The unexpected flight impairment may be the result of anomalies in UAV operation or external conditions, as noted below.

[0178] Along these lines, FIGS. 16-20 illustrate example methods of order fulfillment. For example, FIG. 16 illustrates an example method 1600 of receiving a delivery location for a payload. In block 1610, a delivery request is received for a delivery area for the payload. For example, an order may be received, with the order to be delivered to a delivery area (e.g., the home, business, or other designated location of a customer). In examples, the delivery area may be an area designated for the delivery7, such as the front yard, back yard, side yard, or another area of the customer's home. In examples, the delivery area (e.g., an index of addresses or pay load area for pay loads to be delivered) may be managed within a maps system or module, which may be updated or improved based on deliveries made (e.g., continuously over time). In such examples, personal identifiable information may be mapped to the delivery area, such as an address for delivery (e.g., as provided by a customer) mapped to an index of addresses within the managed delivery area (e.g., a maps database). In examples, information may be provided about where to deliver the payload (e.g., a payload delivery7location), such as the customer identifying where to deliver the pay load (e.g., front yard, back yard, side yard, porch, table, etc.). For instance, after entering an address for delivery, the user / customer may select an area to deliver the payload (e.g.. via a satellite view image or other method, including via text input, visual intent, or augmented reality to mark or select), although user designation of the delivery area may not mandatory. In examples, the system may be intelligent to identify7the delivery area itself, such as based on delivery history (e.g., for same customer or for customers at the same address), default locations, address features, maps data, or other factors. In examples, the customer may provide the delivery request (e.g., delivery address and payload delivery location) through a customer-facing application.

[0179] Depending on the application, the delivery request may be received by the delivery system owner or through one or more partners. In examples, a baseline for delivery requests received by a partner may support freeform text input (e.g., a delivery instructions textbox). In some examples, UI elements or features of the delivery system owner may be imbedded into the partner’s application. In some examples, delivery requests through a partner may be directed to the application of the delivery system owner. In some examples, a notification may be delivered to a customer that an order isready for delivery (e.g., a notification that a prescription is ready for pick up), with the notification including a link to schedule delivery (e.g., via a partner’s application or the system owner’s application).

[0180] In block 1620, a viability of the delivery request is analyzed. For example, the delivery of the pay load / order may be verified, such as verifying that the payload / order can be delivered to the delivery area. The viability of the delivery’ may be based on vehicle capabilities, payload characteristics, or environmental characteristics, such as in a manner as described herein. For example, the viability may be based on the cargo carrying ability of a UAV 100, the size / dimensions of the payload / order, prevailing weather conditions at the delivery area, etc. For instance, the delivery' request may not be viable when a rainstorm or other weather condition (e.g.. high winds, extreme temperatures, etc.) is predicted during the delivery window. In contrast, the delivery request may be viable when clear or mild weather conditions are predicted during the delivery window.

[0181] In block 1630. one or more delivery location options are displayed to a delivery customer. In examples, an image corresponding to the delivery area may be displayed, such as on a user device (e g., remote device 422). A user identification of the one or more delivery location options within the delivery’ area may be received, such as via the user device. The user identification may include pixel information on the image. For example, a user may highlight an area within the image corresponding to the one or more delivery location options. The user identification may include a textual input regarding the image. For example, a user may enter textual input within a prompt on the user device. In examples, block 1730 may include displaying a textual description of the one or more delivery location options relative to the delivery area. The textual description may include at least one of front yard, back yard, side yard, deck, or patio.

[0182] In block 1640, a preference is received for a delivery location option of the one or more delivery location options. For example, a textual input may be received from the user (e.g., via remote device 422) describing the preferred delivery location option. The user may enter textual input within a prompt displayed on the user device. In other examples, the preferred delivery location option may be received through audio input, such that the user may speak the preferred delivery location option.

[0183] In block 1650. the preferred delivery location option is set as the delivery' location for the payload. Once set, the payload may be delivered to the preferred delivery location, such as in a manner as described herein.

[0184] FIG. 17 illustrates an example method 1700 of tracking a delivery order. In block 1710, delivery order characteristics are received. The delivery order characteristics may include a shipping location and a delivery location. Such are examples only, and the delivery order characteristics may include other information, including delivery time, payload characteristics (e.g., size and weight), priority7status, and other order characteristics described herein.

[0185] Block 1720 includes determining that the delivery order is deployed to a delivery vehicle. Block 1720 may include receiving packing data associating the delivery order with the delivery' vehicle. The packing data may be detected as the delivery' order is coupled to the delivery7vehicle. The packing data may be received by capturing information of the delivery vehicle and information of the delivery order as the delivery order is attached to the delivery vehicle. For example, a notification may be generated to indicate the delivery7vehicle has received the delivery order, such as a notification providing that the delivery7order was loaded into the delivery vehicle (e.g., barcode of both the delivery7order and the delivery vehicle being scanned). The notification may be generated through an application, such as via an in-app notification, a system notification, a text message, or the like.

[0186] In block 1730, a flight time for the delivery7vehicle is estimated from the shipping location to the delivery location. The flight time may be estimated based on known flight conditions. The known flight conditions may include at least estimated speed, environmental characteristics, vehicle capabilities, and payload characteristics, or any combination thereof. In other examples, the known flight conditions may include at least one of a battery7state of charge, estimated weather, or keep out zones within an air space.

[0187] In examples, various information may be provided to a delivery customer. For example, in block 1740, the estimated flight time is displayed on a user device (e.g., remote device 422, an app, etc.) associated with the delivery order. In block 1750, a flight speed is displayed on the user device. In block 1760, a three dimensional (3D) orientation of the delivery vehicle is displayed. The 3D orientation may be displayed on the user device as the delivery7vehicle flies or otherwise transits from the shipping location to the delivery' location.

[0188] Block 1770 includes determining there is an unexpected flight impairment along a route between the shipping location and the delivery location. The unexpected flight impairment may include a flying obstacle, a change in environmentalcharacteristics, or a change in vehicle capabilities, among other unexpected conditions that affect UAV flight. In block 1780. the flight time is updated based on the unexpected flight impairment. As a result, the delivery customer or another user may be apprised of when the order will be delivered, such as dynamically as conditions change.

[0189] FIG. 18 illustrates an example method 1800 of generating a delivery time estimate for an order. In block 1810, a shipper throughput capacity at a shipping location is determined for the order. The shipper throughput capacity may be the minimum, maximum, or average rate at which orders can be processed at the sipping location (e.g., from order receipt to packing of the order for delivery). The shipper throughput capacity may be based on a dock utilization, an expected charge time for UAVs 100 at the shipping location, a fleet throughput in the region, and expected order preparation time for the shipping location, and an expected loading time for loading orders into a UAV 100, or any combination thereof.

[0190] In block 1820, one or more order characteristics of the order are determined. The order characteristics may include one or more characteristics described herein. For example, the order characteristics may include a delivery location. In examples, the order characteristics may include a priority value for the order (e g., high priority vs. low priority).

[0191] In block 1830. a delivery time is estimated. The delivery time may be estimated based on the shipper throughput capacity and the order characteristics. For example, the delivery time may be estimated based on predicted packing time of the order at the shipping location, the distance from the shipping location to the delivery location, a speed of the delivery' vehicle, and the like. The delivery' time may be estimated based on charging, repositioning, dock utilization, order loading, weather, etc. For example, based upon payload characteristics (e.g., size, weight, etc.), an average time to prepare and pick up an order at the shipping location may be determined. The time estimate may also factor in the time to get a delivery vehicle to the shipping location and / or any time needed to charge the delivery vehicle (e.g., at the shipping location or along the route from the shipping location to delivery). The delivery time may also take into account real time or predicted weather conditions when estimating flight time (e g., to the shipping location, from the shipping location to delivery7, etc.). In examples, the estimated delivery time may be based on system capacity. For instance, if the system is tracking more orders than the system has capacity to fulfill, the delivery time estimate may be bumpedout accordingly. In such examples, the delivery demand may be managed, such as waiving a delivery fee or providing another incentive in exchange for a later delivery.

[0192] FIG. 19 illustrates an example method 1900 for fleet management of a fleet of UAVs 100. Block 1910 includes receiving a queue of payloads for delivery via the fleet of UAVs 100, such as in a manner as described herein. For example, the queue may include a list of delivery requests for a geographic area, for an order delivery service (e.g., system 400). or the like.

[0193] In block 1920, payload characteristics of the pay loads within the queue are assessed to determine an order priority for the payloads. The payload characteristics may include at least one of a pay load ty pe or a payload delivery promise. The payload type may include the class of goods to be delivered (e.g., consumer goods, perishable food, nonperishable food, medical supplies, etc.). Certain payload types may be prioritized over others. For example, perishable food and medical supplies may be prioritized over consumer goods and nonperishable food. The payload delivery promise may include the promised delivery speed or time of the order (e.g., promised delivery time, within the hour, same day. next day. etc.). In such examples, the orders may be prioritized based on promised delivery.

[0194] In block 1930, UAVs 100 from the fleet are assigned to a pickup location based on the order priority. For example, a greater number of UAVs 100 may be assigned to pickup locations with high priority orders, such as to ensure or facilitate deliverypromises and standards are met.

[0195] FIG. 20 illustrates another example method 2000 for fleet management of a fleet of UAVs 100. In block 2010, a queue of payloads for delivery- is received, such as in a manner as described herein. For example, the queue may include a list of delivery requests for a geographic area, for an order delivery service (e.g., system 400), or the like.

[0196] In block 2020, one or more UAVs 100 are deployed to a pickup location based on the queue of payloads. For example, a UAV 100 or multiple UAVs 100 may be deployed to a pickup location based on order priority, the number of orders in the queue, etc. The one or more UAVs 100 deployed to the pickup location may be agnostic with respect to receiving any payload from the queue of payloads. For example, each UAV 100 may be configured to receive any payload of the queue of pay loads and may be capable of completing a mission for any payload of the queue of pay loads.

[0197] Block 2030 includes determining that a UAV 100 has received a payload. The determination may be based on packing information. For example, the determination may be based on confirmation of the payload packed within the UAV 100 (e.g., confirmation by packer that payload is loaded into the UAV 100. a notification associated with the payload loaded into the UAV 100 (e.g., via barcode scanning, a sensor detecting the payload within UAV 100), etc.). In examples, the packing information may be received based on payload information transmitted from a shipping device as or after the payload has been coupled to the UAV 100.

[0198] In block 2040, a mission is assigned to the UAV 100 to deliver the payload to a customer. The assignment of the mission may be based on pay load characteristics received within the packing information. For example, the packing information may contain a delivery location, a delivery window or promise, or other delivery information. FLEET MAINTENANCE MANAGEMENT

[0199] In examples, UAV operation, such as UAV fleet operation, may account for required or scheduled maintenance. As one example, a determined flight path or trajectory of a UAV 100 may be based on the UAV 100 receiving necessary maintenance along the flight path. For instance, a UAV 100 may be activated on a flight path (e.g., between a shipping location and a delivery location) based on a delivery7window7for its payload enabling the UAV 100 to receive a necessary charge at a charging w aypoint or dock (e.g.. dock 302 or 304). If the delivery window does not allow for the UAV 100 to charge (or receive other required maintenance) at a dock or other location along the flight path from a shipping location to a delivery7location, the payload may be associated with a different or secondary UAV 100, such as to accommodate maintenance of the primary UAV 100 and fulfill the order within the delivery window.

[0200] In examples, a dock (e.g., docking assembly 300) may be positioned based on typical delivery routes or flight paths. For example, one or more docks may be positioned along highly utilized delivery7routes, such as to increase system efficiencies by reducing travel time for maintenance. Based on flight history and maintenance information, one or more UAVs 100 may be relocated to a maintenance bay (e.g.. dock 302 or 304 of docking assembly 300), such as UAVs 100 that are due for maintenance.

[0201] Along these lines, FIGS. 21-24 illustrate example methods of fleet maintenance management during operations. For example, FIG. 21 illustrates an example method 2100 for fleet management for a fleet of UAVs 100. In block 2110, a flight path is determined for a UAV 100 of the fleet. The flight path may be determined based on abatery state of charge of the UAV 100. For example, the batery state of charge may limit how far and in what conditions the UAV 100 can fly. The flight path may be determined based on a delivery location for a payload carried by the UAV 100. For example, the determined flight path may include the delivery location. The flight path may include a charging waypoint between a shipping location and the delivery location.

[0202] In block 2120. the UAV 100 is activated on the flight path. The UAV 100 may be activated on the flight path based on a delivery window for the payload is sufficient to enable a charge at the charging waypoint, such as in a manner as described herein. If the delivery window is not sufficient to allow charging of the UAV 100 at the charging waypoint, the UAV 100 may not be activated on the flight path. In such examples, a different UAV 100 may be activated to fulfill the order within the delivery window.

[0203] FIG. 22 illustrates an example method 2200 for determining a dock location. In block 2210, a plurality of delivery routes and a utilization of the plurality of delivery routes may be analyzed. For example, historical deliveries and / or future delivery projections may be analyzed to determine or narrow down delivery routes that are highly utilized. Highly utilized routes may include those that are used frequently (e.g., daily) or those along which a threshold of UAVs 100 utilize (e.g., utilized by a threshold percentage of the fleet, account for a threshold percentage of total fleet mileage, etc.). The threshold percentage may be based on the available number of docks to position or other factors. For example, the threshold percentage may be lower if a large number of docks are available to position, and vice versa.

[0204] In block 2220, an average position along highly utilized delivery' routes may be determined. The average position may be determined using various methods. For example, the average position may be determined based on an overlap of multiple delivery routes. In some examples, the average position may be based on an average time spent at a particular location.

[0205] In block 2230, a dock (e.g., docking assembly 300) is positioned in the average position. The dock may be positioned based on a number of delivery vehicles in the area and vehicle capabilities of the delivery vehicles. For example, the dock may be positioned in an area having a high number of delivery vehicles, as noted above. In examples, the dock may be positioned to account for delivery' vehicle deficiencies in the area (e.g., low batery levels or capacity).

[0206] FIG. 23 illustrates an example method 2300 of positioning charging stations for UAVs 100. In block 2310, environmental characteristics and a delivery demandprediction are analyzed for a geographic area. The environmental characteristics may include at least two or more of air space limitations, ground obstacles, noise regulations, and weather conditions. The weather conditions may include one or more of fog, wind, precipitation, or temperature, among other ambient conditions. The delivery demand prediction may be determined in a manner as described herein. For example, delivery' histories, shipper operational schedules, social media data, weather data, news data, or event data, among other sources, may be analyzed to predict a delivery demand.

[0207] In block 2320, a location algorithm is optimized based on the analysis. For example, the analysis of block 2310 may indicate locations of high utilization (e.g., shipping locations, event centers, concentrated delivery corridors, etc.) and highly utilized delivery routes within the geographic area. The location algorithm may be optimized to determine average positions of delivery vehicles, and the typical service needs or statuses of the delivery' vehicles at the average positions, among other characteristics or factors. For example, the location algorithm may determine hot zones of activity and need for a fleet of UAVs 100.

[0208] In block 2330. charging stations are positioned based on the optimization. For example, charging stations (e.g., dock 302 or 304, docking assembly' 300, etc.) may be positioned along the highly utilized delivery' routes, at the locations of high utilization, or otherwise in the hot zones determined by the location algorithm. In examples, a home base charging station may be assigned to every UAV 100. In this manner, each UAV 100 may have a go to charging station, such as when back up charging stations are unavailable, the UAV 100 loses communication, or when the UAV 100 needs to be grounded for any reason.

[0209] FIG. 24 illustrates an example method 2400 of managing a fleet of UAVs 100. In block 2410, flight history and maintenance information is received from UAVs 100. The flight history may include the number of flights and recorded flight distances, flight conditions, and / or flight paths of the UAVs 100 (e.g., a fleet of UAVs 100), among other information. The maintenance information may include maintenance schedules and the actual maintenance performed on the UAVs 100.

[0210] In block 2420, a subset of the UAVs 100 is determined above a maintenance threshold. For example, the maintenance information and flight history may be used to identify one or more UAVs 100 in need of upcoming maintenance and / or having past due maintenance or service.

[0211] In block 2430, based on the determination in block 2420, the subset of UAVs 100 is relocated to a maintenance bay. For example, the subset of UAVs 100 may be relocated to dock 302 or 304 of docking assembly 300, such that the UAVs 100 receive needed maintenance or sendee.FUEET MANAGEMENT SYSTEM

[0212] FIG. 25 illustrates a schematic diagram of a system 2500 for management of a fleet of UAVs 100 (e.g.. a fleet management system). The system 2500 may implement various embodiments of the examples described herein. For example, the system 2500 may be used to implement one or more components described herein, such as the UAV 100, the aerial vehicle system 400, etc. For example, the first aerial vehicle 102, the second aerial vehicle 120. the fleet management system 414. the flight controller 416, and / or the server 418 may include one or more of the components of the system 2500. The system 2500 may be used to implement or execute one or more of the components or operations disclosed in FIGS. 1-24, described above. The system 2500 may implement the methods or processes of FIGS. 5-24, described above. The system 2500 may include one or more (e.g.. a plurality of) observatory sources 2510. a dispatch module 2520. a path planning module 2530, a mapping module 2540, a visualization module 2550, and a traffic control module 2560, or any combination thereof. Each of the various components may be in communication with one another through one or more buses or communication networks, such as wired or wireless networks.

[0213] The observatory sources 2510 may receive information (e g., real time or near real time information) regarding status and position information for UAVs 100, such as UAVs 100 within a fleet of UAVs 100. The observatory sources 2510 may receive fleet information from one or more (e.g.. a plurality of) docking locations. The fleet information may include docked UAV charging information and dock throughput information. In examples, the observatory sources 2510 may include one or more docks (e.g., docks 302, 304). The dock(s) may transmit weather information from their respective locations. Additionally, or alternatively, the dock(s) may transmit dock status information, including, for example, one or more of a maintenance issue or a dock availability. In examples, the observatory sources 2510 may include one or more UAVs 100. The UAV(s) 100 may transmit detected weather information, dock status, UAV status, or the like.

[0214] The UAVs 100 may communicate both between different UAVs 100 directly and through one or more cellular communication pathways. The information may bereceived by the observatory sources 2510 via direct vehicle communications between UAVs 100 and through cellular communication between the UAVs 100 and other observatory sources 2510. The UAVs 100 may transmit information to the observatory sources 2510 based on a trigger. The trigger may include a state change (e.g., a state change of the UAV 100, the UAV fleet, etc.).

[0215] The dispatch module 2520 may dispatch selected UAVs 100 to respective locations. For example, the dispatch module 2520 may dispatch UAVs 100 to one or more shipping locations, delivery locations, or maintenance locations (e.g., docking system 300), such as in a manner as described above. The dispatch module 2520 may be in communication with the observatory sources 2510 and a database 2570 to dispatch the selected UAVs 100 to their respective locations, such as in a manner as described above. For example, the dispatch module 2520 may receive a mission request for a delivery order originating from a first location to be delivered to a second location (e.g., from a shipping location to a delivery' location). The dispatch module 2520 may analyze fleet characteristics for one or more (e.g., a plurality of) UAVs 100 within a threshold of the first location, such as within a threshold distance or time from the first location. The dispatch module 2520 may select a UAV 100 from multiple UAVs 100 to complete the mission request. From the path planning module 2530, the dispatch module 2520 may receive a first flight path from a current location of the UAV 100 to the first location, and a second flight path from the first location to the second location. The dispatch module 2520 may dispatch a mission to the selected UAV 100, including the first flight path and the second flight path.

[0216] The dispatch module 2520 may control UAV resource decisions. In one example, dispatch module 2520 may support or interact with different users or applications requesting one or more UAVs (e.g., a reservation system). For example, multiple users, applications, or third-party' systems may each request a certain number of UAVs 100 (e.g., in respective areas), such as to satisfy their own delivery7requirements or demand. In such examples, the different users or applications may run on top of or otherwise interact with the system 2500 to request UAV resources. The dispatch module 2520, or another module of system 2500, may handle the various requests to pull resources. For example, the dispatch module 2520 may define leases balancing the multiple needs. The leases may establish clear ownership (e.g., reservations) on the number of UAVs 100. UAV time, or services (e.g.. docks, pickup, delivery, etc.) allocated to each user or application. In such examples, the different users or applicationsmay interact with a system interface (e.g., a portal application) to request or reserve needed resources, with the underlining system 2500 (e.g., dispatch module 2520) controlling UAV distribution and use based on the requests. In one example, the reservation system may be agnostic to the specific UAV requested. For example, the reservation system or dispatch module 2520 may allocate UAVs 100 based on need or demand (e.g., 5 UAVs allocated to area A, 10 UAVs allocated to area B), irrespective of the payload to be delivered, user, or application.

[0217] The path planning module 2530 may generate flight paths for the selected UAVs 100. The path planning module 2530 may analyze one or more features, factors, or characteristics to generate a flight path. For example, the path planning module 2530 may analyze airspace, risk factors for the airspace and / or the ground space below, distance, battery status, UAV degradation, airspace traffic, ground traffic, road and / or building positions, weather, and location, among others, or any combination thereof, to generate a flight path. The path planning module 2530 may be in communication with the observatory sources 2510 and the dispatch module 2520 to generate the flight paths. For example, the path planning module 2530 may generate the flight paths based on observed conditions of the UAVs 100 and / or the environment.

[0218] The path planning module 2530 may balance a path risk with a path cost in generating the flight paths. For example, the path planning module 2530 may optimize the flight paths to minimize a path risk or another risk. The path risk may include an analysis of a risk to other UAVs 100 and / or a ground risk. The ground risk may include a risk of flying over selected ground areas (e.g., safety corridors or zones, no fly zones or areas, areas with people on the ground, etc.). For example, the generated flight paths maycut perpendicular or generally perpendicular to roads or waterways, avoid flying over stadiums or highly populated areas, consider payload characteristics, or follow historical aircraft patterns, among others, to limit UAV exposure and / or reduce one or more risks to objects below the flight paths. The path cost may include the cost (e.g., of the fleet operator) for flying the given path, the actual use of the path risk in the path planning, etc.

[0219] In examples, the flight paths may be generated based on a balancing of factors. For example, a longer flight path may create or expose the UAVs 100 to more risks overall. In some examples, the path planning module 2530 may reference a cost matrix. The cost matrix may assign weights to each risk or factor or to a group of risks or factors. For example, instead of a single weight assigned to each risk or factor, a basket of costsor risks may be grouped together and carried through path planning. In such examples, a soft constraint may be provided by the grouped costs or risks until one of the grouped costs or risks reaches a threshold, whereupon the single cost or risk may provide a hard constraint on flight path. The path risk may be weighted more than the path cost, or vice versa. In other examples, certain risks may be set to define hard constraints on the flight path, whereas others may be set to define soft constraints. In one example, hard constraints may highly impact the path planning and soft constraints may lightly impact the path planning. For example, a hard constraint may define an absolute or clear no fly zone without exception, and a soft constraint may define a preferred no fly zone absent extenuating circumstances. Other risks may include the flight mode. For example, certain risks may be associated with a hover mode, a forward flight mode, a reverse flight mode, etc. In some examples, the risks or factors may be weighted appropriately such that hard constraints are not included. Instead, the path planning module 2530 may be tuned sufficiently, with the flight paths generated trusting the algorithm.

[0220] The weights or risks may be used to solve a cost function. For example, the cost function may be optimized based on the weights to define a flight path for the UAV 100. For example, a flight path over roads may cost more than a flight path over empty fields. Highly populated or busy areas or flight corridors may cost more than less populated areas or flight corridors. The cost function may be solved based on hard and soft constraints. In one example, the cost function may account for transitions of soft constraints to hard constraints. For example, the battery energy level may be a soft constraint until a threshold amount (e.g., less than 25% power remaining), at which point energy consumption may become a hard constraint. In some examples, the cost function may continue to optimize based on the mission and new data. In another example, the cost function may be used to determine the flight paths without use of hard constraints. For example, very high costs may be associated with certain risks, effectively defining a hard constraint but with improved cost decision making.

[0221] In examples, the path planning module 2530 may generate the flight paths prior to dispatching the UAVs 100 (e.g., preplanned flight paths). Additionally, or independently, the path planning module 2530 may generate or modify the flight paths inflight. For example, the path planning module 2530 may create or adjust the flight paths inflight based on changing conditions, mission criteria, etc. In examples, the path planning module 2530 may receive weather information and airspace information, suchas in real time or near real time. The path planning module 2530 may generate the flight paths based on the weather and airspace information.

[0222] In one example, the path planning module 2530 may generate the flight paths in or on the cloud, making strategic decisions regarding the mission for initial paths. In such examples, the UAV 100 may make safety or real time flight decisions based on the paths. For example, the path planning module 2530 may make high level strategy decisions in or on the cloud, and the UAV 100 may make real time decisions based on real world conditions. Should a conflict arise between the high level or initial flight paths generated by the path planning module 2530 and the real time decisions of the UAV 100, the UAV 100 decisions may override based on accurate sensor information from the UAV 100. In another example, the UAV 100 may complete a mission without the path planning module 2530 in or on the cloud. For example, the UAV 100 may be preloaded with mission information. In some examples, the path planning module 2530 (e.g., in or on the cloud) may be source of truth for a mission, telling the UAV 100 what to do generally. The UAV 100 may be agnostic to the reasoning behind a mission. For example, the UAV 100 may make a payload pickup and delivery, regardless of the actual payload.

[0223] In addition to planning spatially, the path planning module 2530 may account for flight regimes in generating the flight paths. For example, the path planning module 2530 may determine one or more locations for a flight mode transition of the UAVs 100 along the flight paths (e.g., locations where to transition between UAV flight modes along the flight paths). Specifically, the path planning module 2530 may determine where to transition the UAVs 100 between hover and forward flight, when to drop the second aerial vehicle 120 from the first aerial vehicle 102, and where to hover the UAVs 100 along the flight paths. Additionally, or independently, the path planning module 2530 may generate the flight paths based on one or more flight constraints of the different flight modes. For example, the generated flight paths may be based on turning radius, time to deploy, time to transition, or other flight mode constraint or characteristic. In examples, the UAVs 100 may autonomously navigate along the generated flight paths, such as in a manner as described above.

[0224] In this manner, the path planning module 2530 may be configured to analyze a plurality of fleet characteristics, order characteristics, ground space characteristics, and airspace characteristics, or any combination thereof, to generate a flight trajectory for a flight mission, and transmit the flight trajectory to a UAV 100. The fleet characteristicsmay include data (e.g., real time or near real time data) from at least a subset of the UAVs 100 within the fleet. As noted above, a cost function may be used to produce the flight trajectory.

[0225] The mapping module 2540 may generate trajectories (e.g., real time or near real time trajectories) using the information from the observatory sources 2510. The mapping module 2540 may be in communication with the path planning module 2530 to generate the trajectories. For example, the mapping module 2540 may communicate with the path planning module 2530 to generate paths using map data. For example, the map data may include roadway, waterway, and / or structure information or location used by the path planning module 2530 to generate the flight paths, such as to avoid populated areas or gathering sports and / or cross roadways or waterways in a perpendicular or generally perpendicular manner, as described above. The map data may be received from a separate system or vendor, or the map data may be collected via the fleet of UAVs 100. For example, the fleet of UAVs 100 may be utilized to generate a map of the area, based on which flight paths may be generated by the path planning module 2530.

[0226] The visualization module 2550 may generate a visual representation of the UAV fleet. The visual representation may be generated based on the observatory sources 2510 (e.g., the status and position information received from the observatory sources 2510). The visual representation may be presented or displayed on remote device 422.

[0227] The path planning module 2530 may be in communication with the traffic control module 2560. For example, the path planning module 2530 may communicate with the traffic control module 2560 to request airspace for use by the UAVs 100. In examples, the traffic control module 2560 may request the airspace to be used by the UAVs 100. In this manner, the path planning module 2530 may be in communication with traffic control to make reservations and / or clear airspace for use by one or more UAVs 100.

[0228] In some examples, the system may include dock or bay reservations and in some instances the traffic control module 2560 may manage bay or dock reservations for the UAVs 100. For example, the traffic control module 2560 may assign a UAV 100 to a bay. In one example, the bay may be dock 302 or 304. In another example, the bay may be virtual, such as a point in space (e.g., as defined via GPS) where the UAV 100 can hover in position or an area about which the UAV 100 can circle around. For example, the virtual bay may be a staging area for a UAV 100 to wait until a loading dock is clear. In another example, the virtual bay may be an area where a UAV 100 can wait until adock is available. Thus, a virtual bay may be assigned for efficient system throughput, for example throughput loading for fulfilling orders. For example, the virtual bay allows a UAV to queue ahead of being able to access a physical bay, reducing the time needed for transitioning between UAVs (e.g., as one UAV is loaded with a payload and departs the physical bay, the next one can immediately deploy from the virtual location to the physical location). Without the virtual bays, UAVs may end up deploying on shorter missions, heading to other physical bays, or the like, which can increase the transition time between loading UAVs and / or require more physical bays, which require increased space and expense.

[0229] In one example, multiple UAVs 100 may be assigned to the same dock or bay, whether during the same time or during partially overlapping times (e.g., a non-exclusive time window). In such examples, the multiple UAVs 100 may deconflict with each other for positioning at the dock or bay. For example, the UAVs 100 themselves may decide which UAV will go first, second, etc. such as based on time of arrival, mission criteria, or UAV status, among other criteria. In this manner, the UAVs 100 may be agnostic to the dock (e.g.. a shared tenancy to the dock) such that fleet and individual requirements and needs are balanced through UAV throughput. In one example, multiple UAVs 100 may request or reserve the same dock or bay up to a certain density. In this manner, there may be “categories’' of UAVs that may be assigned to certain docks, bays, or other pickup locations, e.g.. 5 food UAVs may be assigned to dock A around 2pm and 10 medical grade UAVs may be assigned to dock B around 3pm, etc. In this manner, the volume can be increased as the payloads can be loaded more quickly into the first available category of UAV that is arrived at the dock, rather than waiting for the specifically assigned UAV.

[0230] In examples, the traffic control module 2560 (or other module within the system) may define a dock assignment system (e.g., a dock buddy system) that assigns a dock or bay to a particular UAV 100, or vice-versa. The assigned UAV 100 may be associated with the dock or bay for however long the assigned relationship is needed. For example, the dock or bay may be reserved for the UAV 100 until the UAV 100 determines the dock or bay is no longer necessary. That is, the UAV 100 defines the reservation length even if the reservation is set from a central processing element. In this manner, the UAV 100 itself may release the assigned relationship. For example, theUAV 100 may release the assigned relationship if another dock is preferred or needed, for example based on mission criteria. As noted above, the assigned dock or bay may beactual or virtual. In such examples, should the UAV 100 have an issue preventing departure from the dock or bay (e.g., loading error, mechanical failures, etc.), the selfreleasing feature may prevent the dock or bay from being double booked.

[0231] In examples, the traffic control module 2560 may provide or define release policies that provide guidance to the UAVs 100 on when to release assigned relationships. In some examples, the release policies may be stored and updated on the cloud and / or in database 2570. In one example, the assigned relationship may be largely up to the UAV 100. In another example, the release policies may be based on dock or bay purposes. For example, when the assigned dock is used for loading or charging purposes, the assigned UAV 100 may release the dock once loaded or charged to allow another UAV 100 to load or charge. When the dock is assigned as a home base, however, the assigned UAV 100 may not release the assigned relationship until the UAV 100 is reassigned to a different home base. As a result, a home base may be permanently assigned or associated with each UAV 100. In some examples, the home base may not be static and may be reassigned based on mission status and UAV position. When coupled with the demand prediction concepts described above, reassigning the home bases may cause the UAV fleet to rebalance towards the updated home bases. The home base may be a set dock or bay to which the UAV 100 is assigned. For example, the home base may be a default location to which the UAV 100 may travel or dock for charging, maintenance, downtime, etc. In another example, the release policies may favor volume push through, such as to maximize deliveries, UAV throughput, etc.

[0232] In examples, the release policies may provide guidance on what behavior is preferred. In one example, a never release guidance may prevent the UAV 100 from releasing the assigned relationship. The never release guidance may apply when a dock is assigned as a home base. Under the never release guidance, the dock or bay may remain with the assigned UAV 100 even if the UAV 100 erroneously releases it (i.e., the dock or bay will be immediately reassigned to the UAV 100). In another example, an aggressive release guidance may instruct the UAV 100 to release the assigned relationship quickly. The aggressive release guidance may apply when a dock is assigned as a loading or charging dock. In another example, a conservative release guidance may provide the UAV 100 leeway to decide when to release the assigned relationship. The conservative release guidance may apply when no other UAVs 100 are in the area, when the dock will be used again in the future, when there is no rush to release the dock or bay, etc.

[0233] In some examples, the dock assignment system may assign contingency bays to the UAVs 100. The contingency bays may be areas, fields, spaces, structures, non-docks, or docks where the UAVs 100 can go during emergencies and / or when its home base is unavailable, based on location and UAV capabilities. In some examples, multiple contingency bays may be assigned to a single UAV 100, or the same contingency bay may be assigned to multiple UAVs 100. In such examples, the multiple bays assigned to a single UAV 100 may have an order of priority, such as home base first, then contingency bays based on location. Additionally, the multiple UAVs 100 assigned to a single bay may have an order of priority, such as based on mission status and / or UAV capability, state, use, or type.

[0234] FIG. 26 illustrates a schematic diagram of an example computer system 2600 for implementing various embodiments in the examples described herein. The computer system 2600 may be used to implement one or more components described herein, such as the UAV 100, the aerial vehicle system 400, the system 2500, etc. For example, the computing elements 470, 474, the system 2500, or any module of system 2500 may include one or more of the components of the computer system 2600. The computer system 2600 may be used to implement or execute one or more of the components or operations disclosed in FIGS. 1-25, described above. The computer system 2600 may implement the methods or processes of FIGS. 5-24, described above. The computer system 2600 may include one or more processing elements 2610, an input / output interface 2620, a display 2630, one or more memory components 2640, a network interface 2650, and one or more external devices 2660. Each of the various components may be in communication with one another through one or more buses, communication networks, such as wired or wireless networks.

[0235] The processing element 2610 may be any type of electronic device capable of processing, receiving, and / or transmitting instructions. For example, the processing element 2610 may be a central processing unit, microprocessor, processor, or microcontroller. Additionally, it should be noted that some components of the computer system 2600 may be controlled by a first processor and other components may be controlled by a second processor, where the first and second processors may or may not be in communication with each other.

[0236] The memory components 2640 are used by the computer system 2600 to store instructions for the processing element 2610, as well as store data, such as the trained ML models 1140 and the like. The memory components 2640 may be, for example,magneto-optical storage, read-only memory, random access memory, erasable programmable memory, flash memory, or a combination of one or more types of memory components.

[0237] The display 2630 provides visual feedback to a user. Optionally, the display 2630 may act as an input element. The display 2630 may be a liquid crystal display, plasma display, organic light-emitting diode display, and / or other suitable display. In examples where the display 2630 is used as an input, the display may include one or more touch or input sensors, such as capacitive touch sensors, a resistive grid, or the like.

[0238] The I / O interface 2620 allows a user to enter data into the computer system 2600, as well as provides an input / output for the computer system 2600 to communicate with other devices or services. For example, an I / O interface 2620 may allow a user to provide data, such as for use in navigation of the UAV 100. The I / O interface 2620 can include one or more input buttons, touch pads, and so on.

[0239] The network interface 2650 provides communication to and from the computer system 2600. For example, the network interface 2650 may allow communication between the first aerial vehicle 102 and the second aerial vehicle 120, and / or between other devices of the aerial vehicle system 400. The network interface 2650 may include one or more communication protocols, such as, but not limited to WiFi, Ethernet, Bluetooth, and so on. The network interface 2650 may also include one or more hardwired components, such as a Universal Serial Bus (USB) cable, or the like. The configuration of the network interface 2650 depends on the types of communication desired and may be modified to communicate via WiFi, Bluetooth, and so on.

[0240] The external devices 2660 may be one or more devices that can be used to provide various inputs to the computer system 2600, e.g., mouse, microphone, keyboard, trackpad, or the like. The external devices 2660 may be local or remote and may vary as desired. In some examples, the external devices 2660 may also include one or more additional sensors.

[0241] The technology described herein may be implemented as logical operations and / or modules in one or more systems. The logical operations may be implemented as a sequence of processor implemented steps directed by software programs executing in one or more computer systems and as interconnected machine or circuit modules within one or more computer systems, or as a combination of both. Likewise, the descriptions of various component modules may be provided in terms of operations executed or effected by the modules. The resulting implementation is a matter of choice, dependent on theperformance requirements of the underlying system implementing the described technology. Accordingly, the logical operations making up the embodiments of the technology described herein are referred to variously as operations, steps, objects, or modules. Furthermore, it should be understood that logical operations may be performed in any order, unless explicitly claimed otherwise or a specific order is inherently- necessitated by the claim language.

[0242] In some implementations, articles of manufacture are provided as computer program products that cause the instantiation of operations on a computer system to implement the procedural operations. One implementation of a computer program product provides a non-transitory computer program storage medium readable by a computer system and encoding a computer program. It should further be understood that the described technology may be employed in special purpose devices independent of a personal computer.

[0243] The above specification, examples and data provide a complete description of the structure and use of exemplary- embodiments of the invention as defined in the claims. Although various embodiments of the claimed invention have been described above with a certain degree of particularity, or with reference to one or more individual embodiments, it is appreciated that numerous alterations to the disclosed embodiments without departing from the spirit or scope of the claimed invention may be possible. Other embodiments are therefore contemplated. It is intended that all matter contained in the above description and shown in the accompanying drawings shall be interpreted as illustrative only of particular embodiments and not limiting. Changes in detail or structure may be made without departing from the basic elements of the invention as defined in the following claims.

[0244] Other examples and implementations are within the scope and spirit of the disclosure and appended claims. For example, features implementing functions may also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations. Also, as used herein, including in the claims, ”or“ as used in a list of items prefaced by “at least one of indicates a disjunctive list such that, for example, a list of “at least one of A, B, or C” means A or B or C or AB or AC or BC or ABC (i.e., A and B and C). Further, the term “exemplary” does not mean that the described example is preferred or better than other examples.

[0245] The foregoing description, for purposes of explanation, uses specific nomenclature to provide a thorough understanding of the described embodiments. However, it will be apparent to one skilled in the art that the specific details are not required in order to practice the described embodiments. Thus, the foregoing descriptions of the specific embodiments described herein are presented for purposes of illustration and description. They are not targeted to be exhaustive or to limit the embodiments to the precise forms disclosed. It will be apparent to one of ordinary skill in the art that many modifications and variations are possible in view of the above teachings.

Claims

CLAIMSWhat is claimed is:

1. A method for deploying one or more unmanned aerial vehicles (UAVs) comprising: determining a demand profile by analyzing delivery demand characteristics, wherein the demand profile comprises demand time and a geographic area for a payload characteristic, wherein the delivery demand characteristics comprise at least one of demographic information, recipient operational schedules, shipper operational schedules, social media data, weather data, news data, or event data; and deploying the one or more UAVs to the geographic area within a threshold of the demand time for delivery of a payload comprising the payload characteristics, wherein the one or more UAVs are deployed based on a match between one or more UAV characteristics and one or more features of the demand profile.

2. The method of claim 1, further comprising readying the one or more UAVs for flight based on the demand time and geographic area.

3. The method of claim 2, wherein readying the one or more UAVs comprises at least one of charging a power source for the UAVs or preloading the pay load into a UAV based on expected ordering demand.

4. The method of claim 1, wherein deploying the one or more UAVs comprises locating the one or more UAVs to charging or dock locations within a threshold distance of the geographic area.

5. The method of claim 4, wherein the locating the one or more UAVs comprises flying the one or more UAVs to the charging or dock locations or transporting the UAVs via a vehicle transport to the charging or dock locations.

6. The method of claim 1, wherein the weather data comprises weather event data and seasonal data.

7. The method of claim 1, wherein the event data comprises a sporting event or large community- event.

8. The method of claim 1, wherein deploying the one of more UAVs comprises: assessing a fleet of UAVs based on anticipated delivery characteristics and an ability to meet the delivery characteristics; and based on the ability to meet the delivery characteristics, selecting at least one of the one or more UAVs for deployment to the geographic area.

9. The method of claim 8, wherein the ability- to meet the delivery characteristics comprises at least one of a state of charge, a cargo capacity, a cargo type, environmental characteristics, or a current location.

10. The method of claim 9, wherein the environmental characteristics comprise one or more weather conditions and one or more airspace restrictions.11 . The method of claim 1, wherein the delivery demand characteristics are analyzed by one or more machine learned models or clustering algorithms to identify demand patterns based on at least one of the geographic area, an industry, a demographic selection, a product type, or a business type.

12. The method of claim 1, wherein the demand profile further comprises a demand classification including one of a safety- critical, an urgent, and a non-urgent.

13. A method of managing a fleet of unmanned aerial vehicles (UAVs) comprising: predicting a delivery- demand volume for a geographic region within a demand window; and positioning a plurality of UAVs in one or more locations within, or within a threshold distance from, the geographic region ahead of the demand window, wherein positioning the plurality of UAV s comprises moving the plurality of UAV s from locations outside of the geographic region.

14. The method of claim 13. wherein positioning the plurality of UAVs within the threshold distance comprises positioning one or more UAVs at a UAV dock location or at a shipper location within the threshold distance.

15. The method of claim 13. further comprising determining the plurality of UAVs for positioning from a fleet of UAVs, wherein the determination is based on cargo characteristics, power characteristics, prior location, or payload delivery characteristics.

16. The method of claim 15, wherein the power characteristics comprise a charge time or a battery level.

17. The method of claim 13, further comprising: generating an incentive to delivery customers within the geographic region ahead of the demand window; and coordinating deliveries of pay loads to the delivery’ customers ahead of the demand window to arrange the positioning of the plurality of UAVs in the one or more locations.

18. The method of claim 17, wherein the incentive comprises at least one of a cost reduction for a payload, a discount for one or more payloads, a giveaway, or a timing benefit.

19. A method of managing a fleet of unmanned aerial vehicles (UAVs) comprising: predicting a demand location for deliveries by UAVs for a demand window; generating incentives for deliveries in the demand location ahead of the demand window; and positioning UAVs in the demand location ahead of the demand window bytransporting deliveries based on the generated incentives.

20. The method of claim 19, wherein incentives are targeted at both delivery customers and payload providers.

21. A method of managing a fleet of unmanned aerial vehicles (UAVs) comprising: detecting a capacity deficit in one or more UAVs within a geographic area basedon an expected demand in the geographic area; and transporting the one or more UAVs outside of the geographic area to rebalance a distribution of the fleet to offset the capacity deficit.

22. A method of fleet management for a fleet of UAVs comprising: determining a battery status for a plurality of UAVs; selecting a UAV from the plurality of UAVs for a mission based on mission characteristics including flight distance, environmental factors, and the battery status.

23. The method of claim 22, wherein the battery status comprises a state of charge of a battery, a temperature of the battery, and a state of health of the battery.

24. A method of positioning charging stations for unmanned aerial vehicles (UAVs) comprising: analyzing environmental characteristics and a delivery demand prediction for a geographic area: and optimizing a location algorithm based on the analysis; and positioning the charging stations based on the optimization.

25. The method of claim 24, wherein a home base charging station of the charging stations is assigned to every UAV.

26. The method of claim 24. wherein the environmental characteristics comprise at least two or more of air space limitations, ground obstacles, noise regulations, and weather conditions including one or more of fog, wind, precipitation, or temperature.

27. A system for management of a fleet of unmanned aerial vehicles (UAVs) comprising: a plurality of observatory sources for receiving real time information regarding status and position information for UAVs within the fleet of UAVs; a dispatch module in communication with the plurality of observatory sources and a delivery database to dispatch selected UAVs to respective locations; and a path planning module in communication with the plurality of observatory sources and the dispatch module to generate flight paths for the selected UAVs.

28. The system of claim 27, wherein the UAVs communicate both between different UAVs directly and through one or more cellular communication pathways.

29. The system of claim 27, wherein the real time information is received by the observatory sources via direct vehicle to vehicle communications between UAVs and through cellular communication between the UAVs and other observatory sources.

30. The system of claim 27, further comprising a mapping module in communication with the path planning module to generate real time trajectories using the real time information from the observatory sources.

31. The system of claim 27, further comprising a visualization module to generate a visual representation of the fleet of UAVs based on the plurality of observatory sources.

32. The system of claim 27. wherein the UAVs transmit information to the plurality of observatory sources based on a trigger, wherein the trigger includes a state change.

33. The system of claim 27, wherein the path planning module further comprises balancing a path risk with a path cost, wherein the path risk is weighted more than the path cost.

34. The system of claim 33, wherein the path risk comprises an analysis of a risk to other UAVs or a ground risk.

35. The system of claim 34, wherein the ground risk comprises a risk of flying over selected ground areas.

36. The system of claim 27. wherein the path planning module further receives real time weather information and airspace information.

37. The system of claim 36, wherein the path planning module is further in communication with a traffic control module to request airspace for use by the UAVs.

38. The system of claim 37, wherein the traffic control module is configured to create an assigned relationship between a dock or bay and each UAV of the selected UAVs.

39. The system of claim 38, wherein the selected UAVs are configured to release the assigned relationship.

40. The system of claim 38. wherein the assigned relationship comprises a home base dock or bay and a contingency dock or bay.

41. The system of claim 38, wherein the assigned dock or bay is a virtual bay.

42. The system of claim 27, wherein the path planning module is configured to determine one or more locations for a flight mode transition of the selected UAVs along the flight path.

43. The system of claim 27. wherein the path planning module is configured to generate the flight paths for the selected UAVs based on a cost matrix.

44. The system of claim 27, wherein the path planning module is configured to generate the flight paths in or on the cloud, and wherein the selected UAVs are configured to make real time flight decisions based on the flight paths.

45. The system of claim 27, wherein the UAVs autonomously navigate along the generated flight paths.

46. The system of claim 27, wherein the dispatch module is configured to: receive a mission request for a delivery order originating from a first location to be delivered to a second location; analyze fleet characteristics for a plurality of UAVs within a threshold of the first location; select a UAV from the plurality of UAVs to complete the mission request; receive a first flight path from a current location of the UAV to the first location; receive a second flight path from the first location to the second location; anddispatch, to the UAV, a mission including the first flight path and the second flight path.

47. The system of claim 27, wherein the plurality of observatory sources receive fleet information from a plurality of docking locations, wherein the fleet information comprises docked UAV charging information and dock throughput information.

48. The system of claim 27, wherein the observatory sources further comprise one or more docks that transmit weather information from their respective locations.

49. The system of claim 48. wherein the docks further transmit dock status information comprising one or more of a maintenance issue or a dock availability.

50. The system of claim 27, wherein the observatory sources comprise one or more UAVs that transmit weather information as detected by the one or more UAVs.51 . The system of claim 27, wherein the path planning module analyzes airspace, risk factors for the airspace, distance, battery7status, UAV degradation, and location to generate a flight path.

52. A system for management of a fleet of unmanned aerial vehicles (UAVs) comprising: a path planning module configured to generate flight paths for the UAVs; wherein the path planning module is configured to generate the flight paths based on a cost function comprising different weighted costs.

53. A system for management of a fleet of unmanned aerial vehicles (UAVs) comprising: a traffic control module configured to: create an assigned relationship between a dock or bay and a UAV, and provide or define release policies associated with the assigned relationship; wherein the UAV is configured to release the assigned relationship based on the release policies.

54. A method of associating a payload with a delivery vehicle comprising: receiving a queue of orders for delivery: detecting order characteristics for a completed order of the queue of orders; detecting delivery7vehicle characteristics for the delivery vehicle receiving the completed order; associating the delivery vehicle with the completed order; and updating a delivery time for the completed order based on flight characteristics of the delivery vehicle.

55. The method of claim 54. wherein the order characteristics are detected by capturing order information from a package of the completed order and associating the captured order information with order information in a fleet database.

56. The method of claim 55. wherein the order characteristics are captured via a scanner or a camera.

57. The method of claim 54. wherein the delivery7vehicle characteristics are determined by capturing a vehicle identifier from the delivery vehicle before or as the delivery vehicle is associated with the completed order.

58. The method of claim 54, further comprising transmitting a delivery7location to the delivery vehicle after the delivery vehicle is associated with the completed order.

59. The method of claim 54, further comprising: determining that the order characteristics do not match cargo characteristics for the delivery7vehicle; and receiving a second completed order, wherein the second completed order is associated with the delivery vehicle and the delivery time is for the second completed order.

60. The method of claim 59, wherein the order characteristics comprise a weight and the cargo characteristics comprise a weight maximum for cargo.

61. A method of assigning an unmanned aerial vehicle (UAV) to a delivery' order comprising: determining one or more order characteristics for the delivery' order; comparing the one or more order characteristics to UAV characteristics for a plurality of UAVs; and selecting, from the plurality of UAVs. a UAV for the delivery’ order based on the UAV charactenstics most matching the order characteristics.

62. The method of claim 61, wherein the one or more order characteristics comprise at least one of a delivery location, a delivery' distance, an order temperature, an order type, a pickup location, or a delivery window.

63. The method of claim 62, wherein the one or more order characteristics further comprise a demand profile, wherein the demand profile comprises a location demand.

64. A method of arranging delivery orders comprising: receiving order characteristics for a delivery' order; associating the delivery' order with a delivery' vehicle; analyzing vehicle characteristics and delivery characteristics with respect to the delivery order to determine a match for delivery criteria; and when there is a match, transmitting the delivery order to a delivery location via the delivery' vehicle.

65. The method of claim 64, wherein the vehicle characteristics comprise cargo characteristics including insulation, prior cargo, and capacity.

66. The method of claim 64, wherein the delivery characteristics comprise optimal transportation temperature, product type, and priority.

67. A method of validating a delivery pay load for delivery by an unmanned aerial vehicle (UAV) comprising: receiving payload characteristics as the delivery payload is prepared for delivery; assigning the delivery payload to a first UAV; comparing first cargo characteristics of the first UAV with the payloadcharacteristics; determining that the first cargo characteristics are not validated with the payload characteristics; and assigning the delivery payload to a second UAV.

68. The method of claim 67, wherein the first cargo characteristics comprise at least one of insulation properties, cooling properties, hazmat characteristics, or food characteristics.

69. A method of managing a fleet of unmanned aerial vehicles (UAVs) comprising: positioning UAVs in different geographic locations based on an estimated delivery demand; dispatching the UAVs to payload pickup locations, wherein the payload pickup locations partially overlap with the geographic locations, wherein the UAVs navigate between a respective geographic location and a respective payload pickup location autonomously; and tracking the UAVs to the payload pickup locations; and assigning the UAVs to delivery locations based on payload delivery characteristics.

70. The method of claim 69, wherein tracking the UAVs comprise receiving tracking information from the UAVs and from docks for receiving the UAVs, wherein the docks are positioned in the geographic locations and the payload pickup locations.

71. The method of claim 69, further comprising generating paths for the UAV s from the payload pickup locations to the delivery locations, wherein the paths are based in part on available air space, weather, vehicle capabilities, known obstacles, and payload delivery characteristics.

72. The method of claim 71, wherein the paths are generated based on an optimization of battery status, distance, air space characteristics, and route risk.

13. The method of claim 69. further comprising dispatching a set of UAVs to updated geographic locations based on a rebalance assessment determined based on current positions of the UAVs.

74. A method for receiving a delivery location for a payload comprising: receiving a delivery request for a delivery area for the payload; analyzing a viability of the delivery request; displaying one or more delivery location options to a delivery customer; receiving a preference for a deli very location option of the one or more delivery location options; and setting the delivery location option as the delivers’ location for the payload.

75. The method of claim 74, wherein displaying the one or more delivery location options comprises: displaying an image corresponding to the delivers’ area; and receiving a user identification of the one or more delivers’ location options within the delivery area.

76. The method of claim 75, wherein the user identification comprises pixel information on the image.

77. The method of claim 75, svherein the user identification comprises a textual input regarding the image.

78. The method of claim 74, wherein displaying the one or more delivery location options comprises displaying a textual description of the one or more delivery location options relative to the delivery area.

79. The method of claim 78. wherein the textual description comprises at least one of front yard, back yard, side yard, deck, or patio.

80. The method of claim 74, wherein receiving the preference for the delivery location option comprises receiving a textual input from the user describing the delivery location option.

81. A method of tracking a delivery order comprising: receiving delivery order characteristics including a shipping location and a delivery location; determining that the delivery' order is deployed to a delivery' vehicle; estimating a flight time for the delivery vehicle from the shipping location to the delivery location based on knoyvn flight conditions comprising at least estimated speed, environmental characteristics, vehicle capabilities, and payload characteristics; and displaying the estimated flight time on a user device associated yvith the delivery' order.

82. The method of claim 81, further comprising displaying a three dimensional orientation of the delivery' vehicle as the delivery vehicle flies from the shipping location to the delivery location.

83. The method claim 81. further comprising displaying a flight speed on the user device.

84. The method of claim 81, further comprising: determining there is an unexpected flight impairment along a route between the shipping location and the delivery' location; and updating the flight time based on the unexpected flight impairment.

85. The method of claim 84, wherein the unexpected flight impairment comprises a flying obstacle, a change in environmental characteristics, or a change in vehicle capabilities.

86. The method of claim 81, wherein the known flight conditions comprise a battery state of charge, estimated weather, and keep out zones within an air space.

87. The method of claim 81, yvherein determining that the delivery' order is deployed to a delivery vehicle comprises: receiving packing data associating the delivery order with the delivery vehicle.wherein the packing data is detected as the delivery order is coupled to the delivery vehicle.

88. The method of claim 87, wherein the packing data is received by capturing information of the deliver}' vehicle and information of the delivery order as the deliveryorder is attached to the delivery vehicle.

89. A method of generating a deliver}' time estimate for an order comprising: determining a shipper throughput capacity at a shipping location for the order, wherein the shipper throughput capacity is based on a dock utilization, an expected charge time for unmanned aerial vehicles (UAVs) at the shipping location, a fleet throughput in the region, an expected order preparation time for the shipping location, and an expected loading time for loading orders into a UAV ; determining order characteristics of the order; and estimating a delivery’ time based on the shipper throughput capacity and the order characteristics.

90. The method of claim 89, wherein the order characteristics include a deliver}’ location.91 . The method of claim 81 , wherein the order characteristics further include a priority value for the order.

92. A method for fleet management of a fleet of unmanned aerial vehicles (UAVs) comprising: receiving a queue of payloads for deliver}' via the fleet of UAVs; assessing pay load characteristics of the payloads within the queue to determine an order priority for the payloads; and assigning UAVs from the fleet of UAVs to a pickup location based on the order priority.

93. The method of claim 92, wherein the payload characteristics comprise at least one of a payload type or a payload delivery promise.

94. A method for fleet management of a fleet of unmanned aerial vehicles (UAVs) comprising: receiving a queue of payloads for deliver}’; deploying one or more UAVs to a pickup location based on the queue of payloads; determining that a UAV of the one or more UAVs has received a payload based on packing information; and assigning a mission to the UAV to deliver the payload to a customer based on payload characteristics received within the packing information.

95. The method of claim 94. wherein the UAV is configured to receive any payload of the queue of pay loads and is capable of completing a mission for any pay load of the queue of pay loads.

96. The method of claim 94. wherein the one or more UAVs deployed to the pickup location are agnostic with respect to receiving any payload from the queue of payloads.

97. The method of claim 94. where the packing information is received based on payload information transmitted from a shipping device as or after the payload has been coupled to the UAV.

98. A method for managing a delivery service comprising: determining a demand window for delivery of a payload; transmitting at least one notification to at least one user regarding an incentive to modify a delivery time of the payload from within the demand window to outside of the demand window; and updating the delivery' time based on a receipt of acceptance of the incentive.

99. The method of claim 98. further comprising updating a fleet deployment schedule for one or more delivery vehicles based on the demand window and a number of accepted incentives across multiple orders.

100. The method of claim 98. wherein the demand window is based on a predicted demand or a current demand.

101. The method of claim 100, wherein the predicted demand is determined by analyzing delivery demand characteristics comprising at least one of social media data, weather data, news data, and event data to determine a demand time and a geographic area for a payload characteristic.

102. The method of claim 98, wherein the incentive is a price reduction for a shipping cost of the pay load, and wherein the at least one user is a pay load supplier.

103. The method of claim 98, wherein the at least one user is a delivery recipient, and wherein the incentive is a discounted price for the payload or a future payload.

104. A path planning module for an unmanned aerial vehicle (UAV) fleet, configured to: analyze a plurality of fleet characteristics, order characteristics, and airspace characteristics to generate a flight traiector for a flight mission, wherein the fleet characteristics comprise real time data from at least a subset of the UAVs within the fleet; and transmit the flight trajectory to a UAV.

105. The path planning module of claim 104, wherein a cost function is used to produce the flight trajectory'.

106. A method for fleet management of a fleet of unmanned aerial vehicles (UAVs) comprising: determining a flight path for a UAV of the fleet of UAVs based on a battery state of charge of the UAV and a delivery' location for a payload carried by the UAV, wherein the flight path includes a charging waypoint between a shipping location and the delivery location; and activating the UAV on the flight path based on a delivery^ window for the payload is sufficient to enable a charge at the charging waypoint.

107. A method for determining a dock location comprising: analyzing a plurality of delivery routes and a utilization of the plurality of thedelivery routes; determining an average position along highly utilized delivery routes; and positioning a dock in the average position.

108. The method of claim 107, further comprising positioning the dock based on a number of delivery vehicles in the area and vehicle capabilities of the delivery vehicles.

109. A method for managing a fleet of unmanned aerial vehicles (UAVs) comprising: receiving flight history' and maintenance information from UAVs; and determining a subset of the UAVs are above a maintenance threshold; and relocating the subset of the UAVs to a maintenance bay based on the determination.

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