Multi-modal delivery system for efficient goods delivery

Abstract

The present disclosure relates to a multi-modal delivery system that coordinates the delivery of goods between ground robot, autonomous vehicle, and / or manually operated transport mechanism. The system leverages the strengths of each type of vehicle to optimize delivery efficiency, particularly in urban environments. The handoff between transport mechanisms can be performed by a human or automated system, offering flexibility in operation. This invention addresses the limitations of each transport mechanism type, such as the speed and range of ground robots and the operational challenges faced by drones in dense urban areas.

Description

P A T E N TDocket No. SROB1PCTMULTI-MODAL DELIVERY SYSTEM FOR EFFICIENT GOODS DELIVERYFIELD OF THE DISCLOSURE

[0001] The present invention relates to the field of delivery systems, and more specifically to a system and method for coordinating delivery between different types of vehicles and transport means to improve efficiency and address operational limitations.BACKGROUND

[0002] Goods delivery has always been a vital aspect of commerce — its efficiency, reliability, and adaptability shaping the consumer experience and the very nature of trade. In recent years, the surge in online retail, the demand for faster delivery times, and the constraints of traditional delivery methods have highlighted the pressing need for new solutions. With the growth of e- commerce, the landscape of goods delivery has transformed. Today’s consumers expect rapid, reliable, and sometimes even same-day delivery for everything from groceries to electronics. Traditional delivery systems, dependent on fleets of trucks and human drivers, have struggled to keep pace with these expectations, often grappling with delays, missed deliveries, and high operational costs.

[0003] One of the most persistent problems in urban goods delivery is traffic congestion. Delivery trucks and vans contribute to — and are hindered by — crowded streets. Limited parking and loading zones further complicate the timely handoff of goods, often resulting in double-parking, traffic fines, and frustrated consumers. In densely populated neighborhoods and city centers, delivery vehicles may be unable to access certain locations altogether, especially during peak hours or in pedestrian-only zones.

[0004] The final segment of product transport to the consumer remains the most expensive and logistically complex portion of goods delivery. It is at this stage that inefficiencies, delays, and errors are most likely to occur. Factors such as building security, narrow alleyways, lack of elevators, or customer absence pose additional hurdles. Traditional delivery vehicles are often ill-suited for these micro-environments, leading to compromised service and operational bottlenecks.

[0005] The proliferation of delivery vehicles in urban areas has significant environmental consequences. Fossil fuel emissions from trucks and vans contribute to air pollution, greenhouse gas accumulation, and noise. Cities worldwide are striving to reduce their carbonP A T E N T Docket No. SROB1PCT footprint, imposing restrictions on delivery vehicles, incentivizing electric fleets, and encouraging alternative forms of transportation.

[0006] Reliance on human drivers is both a strength and vulnerability of traditional delivery systems. Rising labor costs, high turnover rates, and workforce shortages are pressing concerns for logistics companies. Furthermore, delivery work can be physically demanding and hazardous, particularly in extreme weather or during health crises, such as the CO VID-19 pandemic.

[0007] Safety is a dual concern — protecting the goods in transit and ensuring the well-being of delivery personnel. Theft, vandalism, and accidents are common risks. Delicate and perishable goods require specialized handling, and delivery workers can be exposed to dangerous situations, from traffic accidents to interactions in unfamiliar neighborhoods.

[0008] Today’ s consumers demand transparency and control over their deliveries, expecting real-time tracking, flexible scheduling, and personalized service. While technology has enabled significant advancements, traditional delivery systems frequently fall short, hindered by outdated infrastructure, limited data integration, and inflexible routing.

[0009] Persistent challenges have exposed the limitations of conventional delivery methods. The present disclosure addresses these and other related needs in the art.SUMMARY

[0010] The invention provides a multi-modal delivery system that utilizes a combination of ground robots, autonomous vehicles, aerial drones and / or manually operated transport mechanisms to optimize the delivery of goods. The system leverages the strengths of each type of vehicle to overcome their respective limitations. In particular, the system enables:

[0011] Urban Deliveries: Ground robots can navigate densely populated areas where other devices cannot safely or efficiently operate, picking up goods from locations such as restaurants and delivering them to a designated handoff area.

[0012] Long-Distance Deliveries: Drones and autonomous or manually operated vehicles can transport goods over longer distances, bypassing the limitations of ground robots. The goods can be handed off from drones or autonomous vehicles or manually operated vehicles to and from ground robots. In cases such as restaurant delivery, ground robots may be used to pick-up items from dense environments, whereas in others such as parcel delivery from warehouses, ground robots may be used to complete delivery to customers in densely populated areas.

[0013] Flexible Handoff Mechanism: The handoff between robots and drones, or between robots and autonomous vehicles or manually operated vehicles, can be performed by humansP A T E N TDocket No. SROB1PCT or fully automated through robotic arms or other mechanisms. This flexibility allows for various operational scenarios without limiting the system to a specific handoff method.

[0014] In embodiments described herein, a multi-modal delivery system for performing goods delivery operations is provided comprising a goods transportation network comprised of one or more of: a ground robot configured to navigate and operate safely within pedestrian pathways in a densely populated environment to perform pick-up and drop-off of goods; an autonomous vehicle configured to navigate and operate safely within automobile pathways to perform pickup and drop-off of goods; an aerial drone configured to transport the goods and to operate safely in a public or private airspace; and / or a manually operated transport mechanism; a handoff mechanism that provides for the transfer of the goods between transport mechanisms in the goods transportation network, wherein the hand-off mechanism operates in a hand-off location and is manual, semi-automated or automated; a cloud-based centralized control system configured to control and / or actuate the operation of one or more ground robot, autonomous vehicle, aerial drone or manually operated transport mechanism within the goods transportation network; and a computing article based scheduling system operably integrated in connection with the cloud-based centralized control system, wherein the scheduling system receives details of an order for the goods and such details comprise one or more variables that guide (a) the timing of the goods delivery operation within the goods transportation network, (b) a determination of one or more of a plurality of hand-off locations, (c) prioritization of delivery routing of one or more categories of goods, (d) determination of whether a restriction is present or possible during the transport or delivery of the goods that may delay delivery timing, (e) routing and scheduling of one or more ground robot, autonomous vehicle, aerial drone or manually operated transport mechanism within the goods transportation network in a manner that provides a determination of a route for (i) performing the largest number of deliveries of a plurality of goods in a determined time period, and / or (ii) the fastest delivery of one or more of the goods within a delivery time period.

[0015] According to embodiments described herein, the handoff area is located in an environment suitable for both drone operation and robot navigation or human navigation.

[0016] Also, according to embodiments described herein, the handoff mechanism provides for the transfer of the goods between the ground robot, autonomous vehicle, aerial drone and / or manually operated transport mechanism.

[0017] Also, according to embodiments described herein, the ground robot, autonomous vehicle, or manually operated transport mechanism is selected for operation in preference to anP A T E N TDocket No. SROB1PCT aerial drone by the scheduling system within an urban environment where aerial drones face operational limitations due to obstacles, noise, and safety concerns.

[0018] Also, according to embodiments described herein, the aerial drone is selected for operation in preference to a ground robot, autonomous vehicle, or manually operated transport mechanism by the scheduling system in less densely populated environments to transport goods over long distances.

[0019] Also, according to embodiments described herein, the hand-off mechanism includes an automated mechanism configured to automatically transfer goods between the ground robot, autonomous vehicle, and / or manually operated transport mechanism.

[0020] Also, according to embodiments described herein, the hand-off between the ground robot, autonomous vehicle, or manually operated transport mechanism is performed by a human operator.

[0021] Also, according to embodiments described herein, the ground robot, aerial drone, and autonomous vehicle are equipped with communication means in operable communication with the scheduling system to coordinate the timing and location of the handoff.

[0022] Also, according to embodiments described herein, the ground robot, autonomous vehicle, or manually operated transport mechanism are equipped with communication means in operable communication with the scheduling system to coordinate the timing and location of the handoff.

[0023] Also, according to embodiments described herein, the scheduling system operates in conjunction with a machine learning system that accounts for timing, scheduling, re-scheduling and restrictions of past delivery operations and adjusts the function of the scheduling system for future deliveries to improve efficiency.

[0024] Also, according to embodiments described herein, the scheduling system accounts for equipment safety and vandalism risk and adjusts the selected transportation modalities for part of or the entire delivery route accordingly. For example, in certain areas there is or may be a known or observed increased equipment vandalism level, and / or there is or may be a known or observed safety incident occurrence along certain stretches of sidewalk, due to identified (e.g., sidewalk width, number of vehicle alleyways, blind corners, etc.). Information concerning one or both of these aspects can be integrated by the scheduling system and ML system and used to determine which modality is most appropriate. For example, in an area where there is evidence of an increased or high rate of robot vandalism, a preference or weighting for use of an autonomous or manned vehicle may be provided over a sidewalk robot.P A T E N T Docket No. SROB1PCT

[0025] Also, according to embodiments described herein, the improvement of efficiency includes reduced transit time, increase the number of deliveries in a specific time period, combine multiple goods in a single transport mechanism within the transportation network, navigate government restrictions on the operation of one or more transport mechanism in the transportation network, decrease the impact on pedestrian traffic or roadway traffic within a delivery route, and / or decrease the environmental impact of the operation of the transportation network during delivery and hand-off functions.

[0026] Also, according to embodiments described herein, the scheduling system operates in conjunction with a machine learning system that provides a ranking of suitability of each transport mechanism in the goods transportation network to ensure the integrity of the goods and optimizing the speed of the transport, wherein the ranking determines which transport mechanism in the goods transportation network is selected for use on at least a portion of the route.

[0027] Also, according to embodiments described herein, the ranking of suitability includes a consideration of one or of total distance of the delivery, target or allowable duration of the delivery, pick-up and drop-off locations, dynamic availability of each transport mechanism (e.g., availability of units of each transport mechanism which are not fully utilized) including their relative locations, cargo size and type, walkability score, traffic, time of day, weather, legal and regulatory restrictions, and environmental impact.

[0028] Also, according to embodiments described herein, the system further comprises a tracking means the goods within the goods transportation network.

[0029] Also, according to embodiments described herein, the tracking means comprises a barcode, QR code, RFID Tag, NFC Tag, BLE Beacon, UWV communication system, IR Marker, AR Marker, Magnetic Tag, and / or Acoustic Tag and an instrument for identifying and logging the barcode, QR code, RFID Tag, NFC Tag, BLE Beacon, UWV communication system, IR Marker, AR Marker, Magnetic Tag, and / or Acoustic Tag with the scheduling system.

[0030] Also, according to embodiments described herein, methods of using the systems described herein are provided to complete delivery of goods according to the methods and mode of operations of the systems described herein.

[0031] These and other embodiments, features, and advantages will become apparent to those skilled in the art when taken with reference to the following more detailed description ofP A T E N TDocket No. SROB1PCT various exemplary embodiments of the present disclosure in conjunction with the accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The skilled person in the art will understand that the drawings, described below, are for illustration purposes only.

[0033] FIG. 1 depicts an exemplary depiction of the information and instruction flow and types of information coordinated by the multi-modal delivery control system (e.g., cloud-based centralized control system).

[0034] FIG. 2A depicts an exemplary flow of a multi-modal delivery route, including the modalities used, order information, pick, drop-off and hand-off locations.

[0035] FIG. 2B depicts another exemplary flow of a multi-modal delivery route, including the modalities used, order information, pick, drop-off and hand-off locations.

[0036] FIG. 3A depicts an exemplary multi-modal hand-off, including the information exchanged / considered and exemplary modalities used in the hand-off.

[0037] FIG. 3B depicts another exemplary multi-modal hand-off, including the information exchanged / considered and exemplary modalities used in the hand-off.DETAILED DESCRIPTION

[0038] For clarity of disclosure, and not by way of limitation, the detailed description of the invention is divided into the subsections that follow.

[0039] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as is commonly understood by one of ordinary skill in the art to which this invention belongs. All patents, applications, published applications and other publications referred to herein are incorporated by reference in their entirety. If a definition set forth in this section is contrary to or otherwise inconsistent with a definition set forth in the patents, applications, published applications and other publications that are herein incorporated by reference, the definition set forth in this section prevails over the definition that is incorporated herein by reference.

[0040] As used herein, “a” or “an” means “at least one” or “one or more.”

[0041] As used herein, the term “and / or” may mean “and,” it may mean “or,” it may mean “exclusive-or,” it may mean “one,” it may mean “some, but not all,” it may mean “neither,” or it may mean “both.”P A T E N TDocket No. SROB1PCT

[0042] As used herein, “automated handoff’ refers to the transfer of goods performed entirely by robotic or mechanical systems without human intervention.

[0043] As used herein, “centralized control system” refers to a cloud-based or networked computing system responsible for coordinating, monitoring, and directing the operation of all transport mechanisms within the goods transportation network, including scheduling, routing, and handoff management.

[0044] As used herein, “consolidation (of goods)” refers to the process of grouping multiple goods or delivery orders together for transport by a single transport mechanism over a segment of the delivery route to improve efficiency.

[0045] As used herein, “delivery operation” refers to the complete process of transporting goods from a source location to a destination, including all pick-up, transfer, handoff, and drop-off actions performed by one or more transport mechanisms.

[0046] As used herein, “delivery route” refers to the sequence of locations and transport modalities selected by the scheduling system for the movement of goods from the point of origin to the final destination, potentially including one or more handoff locations.

[0047] As used herein, “customer” refers to the individual or entity designated as the final recipient of the goods delivered via the goods transportation network and / or the individual or entity that placed an order causing the transport of the goods.

[0048] As used herein, “goods,” “cargo,” “item” and “package” are used interchangeably. In certain circumstances the term “order” may refer to “goods,” “cargo,” “item” or “package” in a manner that is clear in the context of its use.

[0049] As used herein, “goods transportation network” refers to the coordinated system provided herein comprising one or more transport mechanisms — including ground robots, autonomous vehicles, aerial drones, and / or manually operated transport mechanisms — configured to collectively perform the pick-up, transfer, and delivery of goods between designated locations.

[0050] As used herein, “ground robot” refers to an autonomous ground robot or a semi- autonomous ground robot.

[0051] As used herein, “handoff location” refers to a predetermined or dynamically assigned physical site within the goods transportation network where goods are transferred from one transport mechanism to another, such as from a ground robot to a drone, or from a vehicle to a human courier.P A T E N TDocket No. SROB1PCT

[0052] As used herein, “handoff mechanism” refers to a human or a robot configured to pick or accept pick-up of goods and transfer those goods to a different location or transport mechanism.

[0053] As used herein, “machine learning system” refers to a computational subsystem that analyzes historical and real-time data from delivery operations to optimize future scheduling, routing, and transport mechanism selection, and to improve overall efficiency of the goods transportation network.

[0054] As used herein, “manual handoff’ refers to the transfer of goods between transport mechanisms performed entirely by a human operator.

[0055] As used herein, “modality” or “transport modality” refers to a specific type of transport mechanism used within the goods transportation network, such as a ground robot, autonomous vehicle, aerial drone, or manually operated transport mechanism.

[0056] As used herein, “scheduling system” refers to a computing module, operably integrated with the centralized control system provided herein, that receives delivery order details and determines optimal timing, routing, and transport mechanism selection for goods delivery operations, potentially utilizing machine learning algorithms.

[0057] As used herein, “secure holding location” refers to a designated, access-controlled site within the goods transportation network where goods may be temporarily stored between transport segments, accessible only by authorized transport mechanisms or personnel.

[0058] As used herein, “semi-automated” refers to human control of a mechanical and / or robotic handoff mechanism or robotic transport mechanism.

[0059] As used herein, “suitability ranking” refers to a calculated score or assessment generated by the scheduling or machine learning system, reflecting the appropriateness of a given transport mechanism for a specific delivery segment, based on factors such as distance, cargo type, traffic, weather, and regulatory constraints.

[0060] As used herein, “tracking means” refers to any device or system, such as a barcode, QR code, RFID tag, NFC tag, BLE beacon, GPS tracker, or similar, used to monitor and record the location and status of goods as they move through the goods transportation network.

[0061] As used herein, “transport mechanism” refers to a manually operated vehicle, an autonomous vehicle, a ground robot, a drone, a courier, or other means described or contemplated herein.

[0062] The present disclosure includes a coordinated multi-modal delivery system in which ground robots, vehicles and drones collaborate to deliver goods. As a delivery route isP A T E N TDocket No. SROB1PCT calculated for a specific good, the modalities available and possible for carrying the goods along the route are identified and ranked in terms of suitability for transport of the goods along the entire or a portion of the route, including accounting for timing of any hand-offs. One example of the process involves the following steps:1. Pick-up by Ground Robot: A delivery robot picks up goods from a source location, such as a restaurant, within a densely populated urban area (e.g., an area with limited roadway vehicle parking).2. Transport to Handoff Location: The robot navigates to a nearby designated handoff area, which is a location suitable for drone or autonomous vehicle operations (e.g., a parking lot for an autonomous vehicle).3. Hand-off to Drone or Autonomous Vehicle: The goods are transferred to a drone or an autonomous vehicle (e.g., autonomous vehicle) from the delivery robot for longdistance transport. The handoff can be automated or manually assisted.4. Long Distance Drone / Autonomous Vehicle Transport: The drone or autonomous vehicle then travels to second hand-off location near the final destination.5. Hand-off to Ground Robot for Delivery: The goods are transferred to a (e.g., second) ground robot from the drone or autonomous vehicle for transport over the comparatively shorter distance for delivery. The handoff can be automated or manually assisted.6. Completion of Delivery: Ground robot then transports the goods directly to the customer (e.g., doorstep, storefront, etc.) or a pre-designated dropbox for delivery to the customer.

[0063] In another exemplary embodiment of the present disclosure, the delivery process involves the following steps:1. Pick-up by Ground Robot or Person: A delivery robot picks up goods from a source location, such as a restaurant, within a densely populated urban area (e.g., an area with limited roadway vehicle parking).2. Transport to Handoff Location: The human or robot navigates to a nearby designated handoff area, which is a location suitable for drone, conventional vehicle or autonomous vehicle operations (e.g., a parking lot for a conventional vehicle or an autonomous vehicle).3. Hand-off to Drone, Vehicle or Courier: The goods are transferred to a drone, vehicle (e.g., autonomous vehicle or conventional vehicle) or courier (e.g., bike, motorcycle,P A T E N TDocket No. SROB1PCT moped, etc. courier) from the delivery robot or human for longer-distance transport. The handoff can be automated or manually assisted by a human.4. Longer Distance Transport: The drone, vehicle or courier then travels to second handoff location near the final destination.5. Hand-off to Ground Robot or Human for Delivery: The goods are transferred to a (e.g., second) ground robot or human from the drone, vehicle or courier for transport over the comparatively shorter distance for delivery. The handoff can be automated or manually assisted by a human.6. Completion of Delivery: Ground robot or human then transports the goods directly to the customer (e.g., doorstep, storefront, etc.) or a pre-designated dropbox for delivery to the customer.

[0064] The system includes an automated hand-off mechanism configured to automatically transfer goods between ground robots, aerial drones, and / or autonomous vehicles, thereby minimizing manual intervention and ensuring seamless delivery operations. In certain scenarios, the hand-off between the ground robot and the aerial drone or autonomous vehicle may be performed by a human operator, allowing flexible adaptation to real- world environments where full automation may not be feasible. Communication means are integrated into each robot, enabling coordination of hand-off timing and location, both through the centralized scheduling system and direct robot-to-robot communication for redundancy and robustness.

[0065] These examples are intended to be illustrative and non-limiting - there can be more or fewer steps in the process and distances can be travelled by both vehicles, couriers and drones, not necessarily only one of the listed means. This multi-modal system optimizes the delivery process by allowing each type of vehicle to operate in the environment best suited to its capabilities within the environment where it will operate. While speed is often at a premium for selecting the appropriate transport mechanism, there are a variety of other factors considered as explained herein.

[0066] Each or one or more of the manual or robotic modalities in the transportation network may be equipped with a GPS or similar tracker (e.g., a human may make use of GPS on a mobile device) such that the centralized control system and scheduling system can readily determine the location of any transportation and / or transfer modality in the transportation network at any particular moment for real time positioning and scheduling.P A T E N TDocket No. SROB1PCT

[0067] At each point at which goods are picked up, transferred, and delivered between origin and the transportation network, between modalities in the transportation network, and between a modality in the transportation network and the customer a tracking means is utilized in most embodiments to track the transfer and transportation of the transported goods. The system used to track the goods is most often a barcode, QR code, RFID Tag, NFC Tag, BLE Beacon, UWV communication system, IR Marker, AR Marker, Magnetic Tag, Acoustic Tag, or similar. While the system can make use of barcodes, QR codes, RFID Tags, NFC Tags, BLE Beacons, UWV communication systems, IR Markers, AR Markers, Magnetic Tags, or Acoustic Tags already present on the goods or packaging thereof. More frequently a specific barcode, QR code, RFID Tag, NFC Tag, BLE Beacon, UWV communication system, IR Marker, AR Marker, Magnetic Tag, or Acoustic Tag is generated and applied to the goods at the point of origination. These tracking means are accounted for in the presently described systems (e.g., centralized control system and scheduling system) to follow and schedule the passage of the goods within the robotic network and ensure integrity of the goods.

[0068] In practice, the ground robot that initially picks up the goods applies a barcode, QR code, RFID Tag, NFC Tag, BLE Beacon, UWV communication system, IR Marker, AR Marker, Magnetic Tag, or Acoustic Tag to the goods at the point of origination that is precoded or scanned into the centralized control system and scheduling system. At each point in the delivery process where the goods are transferred between transport mechanisms or from a transport mechanism to the customer, the barcode, QR code, RFID Tag, NFC Tag, BLE Beacon, UWV communication system, IR Marker, AR Marker, Magnetic Tag, or Acoustic Tag is scanned again and the associated code in the scheduling system is then updated with a code or label that indicates the action taken. For example, when the goods are handed off from the initial ground robot or human to a vehicle or drone, the ground robot, human and / or autonomous vehicle or drone scans the barcode, QR code, RFID Tag, NFC Tag, BLE Beacon, UWV communication system, IR Marker, AR Marker, Magnetic Tag, or Acoustic Tag at the time or immediately after the hand-off action is taken. If the transportation mechanism does not have a scanning system (e.g., an Uber or Lyft vehicle), then the transportation mechanism, or its operator, scans the barcode, QR code, RFID Tag, NFC Tag, BLE Beacon, UWV communication system, IR Marker, AR Marker, Magnetic Tag, or Acoustic Tag at the time the goods are transferred to the transportation mechanism lacking a tracking means to indicate when the hand-off action has been completed, at which point the scheduling system is updated with this information. In such case an identifier of the specific transport mechanism nowP A T E N TDocket No. SROB1PCT carrying the goods is often included in the information updated in the scheduling system. Additionally, the goods are or may be scanned again at the time of delivery to the customer or dropbox to indicate that the delivery process has been completed. Such information may be made accessible by the customer through an application interface, link or similar provided to the client.

[0069] Also contemplated is specific GPS based tracking of each individual item or package, where each item is labeled with a GPS tracker that can be tracked independently of the robotic network. This process flows as described above with the barcode, QR code, RFID Tag, NFC Tag, BLE Beacon, UWV communication system, IR Marker, AR Marker, Magnetic Tag, or Acoustic Tag example, but the GPS tracker is applied (to applied additionally to a barcode, QR code, RFID Tag, NFC Tag, BLE Beacon, UWV communication system, IR Marker, AR Marker, Magnetic Tag, or Acoustic Tag) to the goods at the point of origin. In certain examples a GPS tracker is only applied at a hand-off location when the goods are transferred to a 3rdparty vehicle or manually operated transport mechanism such as an Uber or Lyft vehicle, other manually operated vehicle, or courier. Such information may be made accessible by the customer through an application interface, link or similar provided to the client.

[0070] Also contemplated herein is the use of a camera on one or more of the robotic modalities in the robotic network to track / log / verify the hand-off of goods to the initial ground robot or human, between transportation modalities and to the customer. Such images or video are then transmitted to and saved in the scheduling system or separate quality control system for use in verifying each step of the delivery process. Such information may be made accessible by the customer through an application interface, link or similar provided to the client.

[0071] According to certain embodiments, the present system may often make use of secure holding locations for goods. Such locations are useful in situations where one or more deliveries are being consolidated to one or more transportation modalities for transport over longer distances for efficiency purposes, and / or situations where one or more end delivery locations are relatively close together and / or the timing for delivery is within a specific / predetermined time window. Such secure holding locations may be, or be similar to, a dropbox that is accessible only by a robotic modality in the robotic network or a human operator using an identification means. In practice, the goods are handed-off to the secure holding location from a ground robot, drone, vehicle, or human (including a separate hand-offP A T E N TDocket No. SROB1PCT mechanism, if applicable) and then held at that location for pick-up by a human, ground robot, drone or vehicle to transport the goods on the next segment of the delivery process.

[0072] As used herein, “densely populated urban area” refers to an area with limited access for the operation of one or more modalities or reduced options for hand-off locations due to the nature of the environment based on a number of factors, including state or federal limitations / regulations on the operation of autonomous vehicles or drones; areas designated officially as cities or densely populated townships or counties (i.e., by a census such as the US Census); areas with a high density of human structures, such as houses, commercial buildings, roads, bridges, and / or railways; densely settled areas consisting of 2,000 or more housing units or 5,000 or more people; urban centers having a minimum of 50,000 inhabitants plus a population density of at least 1500 people per square kilometer or density of build-up area greater than 50%; and / or areas with availability of hand-off locations (e.g., limited parking or limited options for drone take-off / landing) that number one or fewer within a 5-10 block radius.

[0073] Areas suitable for drone or autonomous vehicle operations include areas predetermined based on pre -placed static features (e.g., buildings, power lines, etc.) located where (and restricted to locations where) aerial drones can safely take-off, load, and / or land. In the case of roadway vehicles, areas suitable for drone or autonomous vehicle operations often are dynamically determined, e.g., based on active location of available autonomous roadway vehicles and / or where such vehicle can safely stop and perform a hand-off (while keeping in mind the timing involved in performing such a hand-off), and / or within a pre-determined statically or dynamically assigned geo-fence defined by the ready availability of parking (e.g., a parking lot). In certain parking areas the number of available spots may be updated in realtime, which calculation can be incorporated into the system operations such that a spot may be available at certain times and not at others. The statically or dynamically assigned geo-fence may identify a hand-off location that is available during certain times (e.g., mornings, evenings, weekends) but not available at other times (e.g., during the day on weekdays). Also included in this calculation is the consideration of the issue of whether any available modality can be utilized that does not significantly disrupt human traffic on the roadway or the sidewalk or air traffic in the area. A significant disruption in this regard may be updated and altered based on feedback in the community or a delay in a delivery caused by disruption such that an incidence of identification disruption of traffic on the roadway or the sidewalk or air traffic in the area will be incorporated into the ML system based on location and time. The sameP A T E N TDocket No. SROB1PCT features noted above may also be applied in circumstances where manual transport is provided by a human and / or a manually operated vehicle.

[0074] The hand-off to the drone or autonomous vehicle is conducted in an automated, semiautomated or manual fashion. Technological development and reliability of an available modality to properly, safely and efficiently perform the hand-off is a variable considered by the scheduling system (including the ML system) in the manner in which the hand-off is affected. In certain embodiments, depending on the nature of the goods (e.g., food items, delicate items, poorly packaged items, etc.), a human may be better suited to perform hand-off in preference to an automated modality such as a robot. In certain exemplary embodiments, a human operator may perform the act of physically transferring goods between sidewalk robots / drones and / or autonomous roadway vehicles (i.e., physically carrying the order or cargo between devices). In certain other exemplary embodiments, a robot or other mechanized transfer mechanism (e.g., dedicated pick and placement robot) will perform the transfer of goods between robots / drones and / or autonomous roadway vehicles in an automated manner. In certain other exemplary embodiments, a robotic transfer mechanism positioned on a sidewalk robot / drone or autonomous vehicle (i.e., rather than a separate dedicated transfer system) will perform the transfer of goods between sidewalk robots / drones and / or autonomous roadway vehicles in an automated manner. In certain other exemplary embodiments, a human will actuate / operate a robotic transfer mechanism (e.g., dedicated or positioned on a sidewalk robot / drone or autonomous vehicle) to perform the transfer of goods between sidewalk robots / drones and / or autonomous roadway vehicles in an automated manner. FIG. 3 depicts an exemplary multi-modal hand-off, including the information exchanged / considered and exemplary modalities used in the hand-off.

[0075] According to certain embodiments, the nature of the packaging of the goods is considered in selecting the appropriate transport and / or hand-off mechanism. For example, at the point of origin, the robot or human operator scans the goods and obtains an image that is used by the scheduling system (including the ML system) to select the route, transport means, the decision of whether the goods may be consolidated for transport with other goods, and / or hand-of mechanisms selected for transport of the goods.

[0076] As noted, the ML system is often operably integrated in the scheduling system of the cloud-based centralized control system of the present systems for resource management and robotic modality and / or route selection within the robotic network and geographical area where the goods transport occurs (modality and / or route ranking).P A T E N TDocket No. SROB1PCT

[0077] Specifically, the ML system operates to select and / or actuate the transport mechanism option best suited for transport of the goods for at least a portion (including for all portions) of a delivery route. This selection is based on a ranking of suitability of each modality to ensure the integrity of the goods and optimizing the speed of the transport. This ranking is also often assessed based on the location of the transport mechanism, the time of the need for its use and the location where the needed use is set to begin. This ranking is also often assessed per segment (or subset) of the delivery, in such instances where more than one modality is used to complete a delivery. These segments are often broken up per unit distance or per city block, as examples. The different modalities are often ranked on an assessment of many factors not limited to: total distance of the delivery, target or allowable duration of the delivery, pick-up and drop-off locations, dynamic availability of each modality (and specifically availability of units of each modality which are not fully utilized) including their relative locations, cargo size and type, walkability score, traffic, time of day, weather, legal and regulatory restrictions, and environmental impact. Some factors may be used to immediately disqualify a particular transport mechanism, such as legal and regulatory restrictions; cargo size and type (e.g. in the instance that the cargo is too large or heavy for a given modality); or weather conditions unsuitable for operation (e.g., very high winds impacting aerial drone safe operation). Other factors may not be immediately disqualifying but may carry heavy weighting. For example, heavy traffic may not be immediately disqualifying but would weigh heavily against selection of a ground vehicle (e.g., autonomous or manually operated) for short-distance delivery. In addition, certain factors may not carry heavy weighting but could be expected to change the outcome in instances where other factors net equal. As an example, if a delivery is expected to be satisfactorily completed and within similar time whether fulfilled by a sidewalk robot or an autonomous vehicle, the sidewalk robot may be preferred for its overall smaller (negative) impact on the environment. Another factor in selecting one modality over another of equal weight may be that a separate and concurrent delivery of two or more deliveries can require one of the equally weighted modalities identified for the first delivery or another of the two or more deliveries. In this case, the ML system may select the modalities for the two or more deliveries that will, for example, lower the total weight (and cost) of two or more deliveries while maintaining the delivery requirements, and / or reduce time, environmental impact, or have a positive effect on the various variables noted herein, of two or more deliveries while maintaining the delivery requirements. Other options that may be considered in this ranking include, but are not limited to, reliability of the modality, types of goods, amount of goods,P A T E N TDocket No. SROB1PCT speed of transport (top, average, etc.), remaining operable life of modality prior to the need to be recharged, and whether any special handling procedures are necessary. In cases where a manned vehicle or human courier can improve the outcome of the delivery, the ML system may add these modalities into the delivery plan. Based on an evaluation of these factors, the ML system will rank one or more ground vehicles, ground robots and / or drones to be selected for use for all or a portion of the delivery route. Based on the evaluation, the ML system may often recommend a specific delivery route or an alteration of a previously determined delivery route to optimize speed of delivery of a single item, to reduce overall time spent delivering multiple items, or reduce the overall use of resources and conservation of energy.

[0078] Also, according to embodiments described herein, the ML system accounts for equipment safety and vandalism risk and adjusts the selected transportation modalities for part of or the entire delivery route accordingly. For example, in certain areas there is or may be a known or observed increased equipment vandalism level, and / or there is or may be a known or observed safety incident occurrence along certain stretches of sidewalk, due to identified (e.g., sidewalk width, number of vehicle alley ways, blind corners, etc.). Information concerning one or both of these aspects can be integrated by the scheduling system and ML system and used to determine which modality is most appropriate. For example, in an area where there is evidence of an increased or high rate of robot vandalism, a preference or weighting for use of an autonomous or manned vehicle may be provided over a sidewalk robot.

[0079] The ML system can also be used to determine where to establish handoff locations between different modes of transport. For example, sidewalk robots in an outdoor mall can easily travel the paths and sidewalks around buildings but may not traverse parking lots as safely or effectively. When drones are used to deliver over longer distances, the ML system can be used to determine where to ideally place transfer locations where a sidewalk robot can hand off to a drone without the need to traverse a parking lot, where the drone can fly over the parking lot and collect the item from the robot without landing. The same weighting system that considers which modes of transportation to use can be utilized to determine where to optimally establish handoff locations.

[0080] In certain embodiments, the system dynamically adjusts future delivery plans to improve efficiency based on these insights. According to certain related embodiments, the delivery system will provide data from past deliveries into the ML system to evaluate the data for potential efficiency improvements, including reduced transit time, increased delivery frequency within a time period, consolidation of multiple goods into a single robot, navigationP A T E N TDocket No. SROB1PCT of governmental restrictions, reduction in pedestrian or roadway traffic disruption along delivery routes, increased security for transported goods, improved integrity of transported goods, and minimization of environmental impact during both delivery and hand-off operations.

[0081] Also according to certain embodiments, the ML system provides a ranking of the suitability of each transport mechanism (e.g., human, robot, vehicle, etc.) in the transportation network for each segment of the transport route, based on factors such as total distance, target or allowable delivery duration, pick-up and drop-off locations, dynamic availability and utilization of each modality, cargo size and type, walkability scores, current traffic, time of day, weather conditions, legal and regulatory constraints, environmental considerations, useful battery life remaining (if applicable), and planned / known future deliveries for each transport mechanism. The highest-ranked transport mechanism is often selected for each segment of the delivery route to maximize integrity and speed of goods transport.

[0082] The issue of distance often factors into the flow and control of the system, specifically including the distance from the origination of the goods to the end destination and / or the distance of the segment necessary for travel between the origination and end destination. As one example, a sidewalk robot travelling at roughly human jogging speeds could deliver up to ~2.5 miles in timely fashion (estimated to be approximately 50% of the on-demand delivery market). If this robot were (permitted) to travel in the bike line at roughly human biking speeds it could deliver up to -5 miles in timely fashion (estimated to be approximately -80% of the on-demand delivery market). The availability of goods within a close enough distance to the end destination to satisfy the on-demand delivery market will often control timing and availability of the system for the delivery of goods. Similarly, technology limitations may limit the modality selected for the delivery or portion of the delivery. When considering the efficiency of the overall operation of the system that includes multiple simultaneous deliveries of goods to different end destinations, the system may select a vehicle modality with a higher top / average speed and / or larger capacity for transporting multiple goods or multiple types of goods for the transport of goods over the exemplary 2.5 or 5 miles scenarios, e.g., to deliver more total orders or for faster order completion. Issues such as the amount of (including the expected amount of) vehicle, foot, bike, etc. traffic in the areas to be travelled by the modality, and restrictions (e.g., government restrictions, practical restrictions, etc.) on travel of certain types modalities, are considered in optimizing efficiency of the system.P A T E N TDocket No. SROB1PCT

[0083] While not intending to be bound by reference, sidewalk robots have a practical current total range of less than 5 miles per delivery. Roadway vehicles have a longer range so the timing needed for effecting goods delivery will often modulate the use of autonomous roadway vehicles, with a practical range being under 25 miles. But, when the system is utilized for the transport of goods with a longer delivery horizon (e.g., next-day, overnight or multiple days), longer distances can be covered by autonomous roadway vehicles in the present system. Aerial vehicles such as drones are often used for segments of the delivery route under 15 miles (e.g., between 10-15 miles) though this range will expand with improved technology. It is contemplated that within the present system, goods may be transferred to / from any vehicle that travels by land, air or water even though specific examples of such vehicles are not specifically discussed, but are contemplated, herein.

[0084] In certain embodiments the autonomous vehicle in the robotic network is a multipurpose vehicle such as an Uber or Lyft vehicle with the hand-off of goods to the autonomous vehicle for at least a portion of the delivery route (often while the vehicle is otherwise transporting other goods or passengers).

[0085] Deliveries using the present system can be fulfilled in a number of manners. One exemplary process is as follows 1) a sidewalk robot picks up from a merchant; 2) the sidewalk robot travels to a hand-off location; 3) the sidewalk robot (and / or a separate transfer mechanism or human) then transfers the order to a roadway vehicle or aerial drone; 4) the roadway vehicle or aerial drone then travels to second hand-off location (e.g., closer to the end destination); 5) a roadway vehicle or aerial drone then transfers (and / or a separate transfer mechanism or human) the order to a sidewalk robot; 6) the sidewalk robot then travels to a customer drop-off location (e.g., doorstep, drop box, etc.); and 7) the customer retrieves order from the sidewalk robot or human; or the sidewalk robot or human drops off the goods to a pre-designated drop-off location.

[0086] According to embodiments described herein, the coordination of the overall system and the individual modalities / units within the system is centrally controlled, i.e., not by the human customer. This multi-modal system optimizes the delivery process by allowing each type of vehicle to operate in the environment best suited to its capabilities at the moment it is needed based on the intended cargo. As noted, in one exemplary implementation a cloud-based ML system considers the following information during system coordination:- the location of the pick-up (e.g., what modalities can access the pick-up location? The decision options for this aspect are scored based on the modality better suited for theP A T E N TDocket No. SROB1PCT pick-up, with multiple modalities remaining an option, though some options may have a higher score for suitability for the pick-up):- the distance to the drop-off location;- the predetermined (e.g., as defined in a service level agreement or as by another agreement or expectation in the marketplace concerning timing) delivery duration, e.g., what is the time period provided or needed for completing the delivery before it is considered to be late;- the location of the drop-off (e.g., what modalities can access the drop-off location? The decision options for this aspect are scored based on the modality better suited for the drop-off, with multiple modalities remaining an option, though some options may have a higher score for suitability for the drop-off);- the distance to a potential hand-off location (e.g., this could be a drone loading station; or it could be where an autonomous vehicle is currently parked or could park); and / or- historical information about the routes that would be taken by each modality.

[0087] Sidewalk robots are often best suited for operation in dense urban locations and campuses. Roadway vehicles are often best suited for operation in areas with urban sprawl, suburban areas, and / or semi-rural areas. Aerial drones are often best suited for operation in areas with urban sprawl, suburban areas, and / or locations with high traffic.

[0088] Evaluation of this information by the machine learning system determines an optimal route, which may involve a single modality or multiple modalities (and may require one or more additional hand-offs between different modalities) to the destination. In operation of the system and its underlying processes any one of a variety of goals may guide the modality selection, including optimizing for overall delivery time of a single good or type of good; total deliveries fulfilled in a time period; minimizing environmental impacts (e.g., not assigning short-distance, i.e., ~2.5 miles or less, deliveries to roadway vehicles; and / or human system interruption, i.e., what modality is least disruptive to the flow of human traffic, both footpath and roadway.

[0089] A cloud-based centralized control system (e.g., the scheduling system provided herein) configured to control and / or actuate the operation of one or more transportation mechanisms within the goods transportation network is also provided. FIG. 1 depicts an exemplary depiction of the information and instruction flow and types of information coordinated by the scheduling system.P A T E N TDocket No. SROB1PCT

[0090] A computing article based scheduling system operably integrated in connection with the scheduling system is provided, which scheduling system receives details of an order for the goods and such details comprise one or more variables that guide (a) the timing of the goods delivery operation within the goods transportation network, (b) a determination of one or more of a plurality of hand-off locations, (c) prioritization of delivery routing of one or more categories of goods, (d) determination of whether a restriction is present or possible during the transport or delivery of the goods that may delay delivery timing, (e) determining if an equipment safety or vandalism risk is present along the delivery route, (f) routing and scheduling of one or more robot within the goods transportation network in a manner that provides a determination of a route for (i) performing the largest number of deliveries of a plurality of goods in a determined time period, (ii) the fastest delivery of one or more of the goods within a delivery time period, (iii) reducing vandalism risk, and / or (iv) reducing safety risks.

[0091] The scheduling system will often control the route selected and the mode used for each portion of the route. FIGS. 2A and 2B depict exemplary flow options of a multi-modal delivery route, including the modalities used, order information, pick, drop-off and hand-off locations.

[0092] FIGS. 3A and 3B depict exemplary multi-modal hand-off scenarios, including the information exchanged / considered and exemplary modalities used in the hand-off. As shown in these figures, the scheduling system operates to assign a handoff location, which prompts the transport mechanisms to travel to that location if they are not there already. At the handoff location the transport mechanism may need to prepare for handoff, for example, by unlocking or opening a door or preparing a carrier. A handoff mechanism such as a robot or human may provide for goods transfer. Often a scanning mechanism (including those mentioned herein) and / or photo / video is employed to track transfer of the goods from one transport mechanism to another. After the transfer the transport mechanism having the goods transferred from it may be told that it is no longer needed or sent to a new destination for pick-up or transfer of goods. After the transfer the transport mechanism having the goods transferred to it may be told a route or provided with route changes, it may be told to wait for additional goods, and / or it may be told to travel to a different location for delivery or pick-up of other goods, at which point it takes the indicated action. At each point in the depicted process the scheduling system may track each transport mechanism, and the handoff means and provide and / or track instructions relevant to each aspect including location, timing, process details, route, goods details, etc.P A T E N TDocket No. SROB1PCTDetailed Embodiments

[0093] The following description sets forth representative, non-limiting embodiments of the multi-modal delivery system. Unless expressly stated otherwise, any feature, element, module, component, step, or sub-combination described with respect to a particular embodiment may be combined with, substituted for, or otherwise incorporated into any other embodiment. Headings are provided solely for ease of reference and do not limit the scope of the appended claims.

[0094] Embodiment 1 - Basic Multi-Modal Network

[0095] In a first embodiment, the goods transportation network comprises:(i) at least one autonomous ground robot configured for pedestrian-path operation;(ii) at least one autonomous vehicle configured for roadway operation;(iii) at least one aerial drone;(iv) a cloud-based centralized control system in two-way data communication with each transport mechanism;(v) a scheduling system employing machine-learning algorithms to assign modalities, routes, and handoff locations; and(vi) a manual, semi-automated, or automated handoff mechanism positioned at a designated handoff location.

[0096] Any of the tracking, ranking, or secure-holding features described in Embodiments 2-7 below may be implemented in this basic embodiment.

[0097] Embodiment 2 - Machine-Learning-Driven Suitability Ranking

[0098] This embodiment augments the scheduling system of Embodiment 1 with a machinelearning (ML) engine that (a) dynamically ranks each available modality for each delivery leg based on distance, cargo type, traffic, weather, regulatory constraints, and environmental impact, and (b) self-adjusts using historical performance data. The ML engine may interoperate with any modality, handoff mechanism, or tracking means recited in Embodiments 1, 3-7.

[0099] Embodiment 3 - Secure Holding Locations and Consolidation

[0100] In this embodiment, one or more secure holding locations — e.g., lockable dropboxes or access-controlled lockers — are inserted as intermediate nodes within the transportation network. Goods may be consolidated, disaggregated, or temporarily buffered at these locations to optimize capacity utilization of downstream modalities. The secure holdingP A T E N TDocket No. SROB1PCT concept is compatible with any routing, ranking, or modality configuration disclosed in Embodiments 1, 2, 4-7.

[0101] Embodiment 4 - Expanded Tracking Means

[0102] Here, each good is tagged with at least one tracking means selected from: barcode, QR-code, RFID, NFC, BLE beacon, GPS tag, IR marker, AR marker, magnetic tag, acoustic tag, or any combination thereof. Scan events are logged to the scheduling system at pick-up, transfer, and drop-off. Camera-based verification may supplement or replace tag scanning. The expanded tracking architecture may be employed with any transport mechanism, ML engine, or secure-holding scheme of Embodiments 1-3, 5-7.

[0103] Embodiment 5 - Manual and Semi- Automated Handoffs

[0104] This embodiment focuses on human involvement: a courier, driver, or other operator physically transfers goods between modalities. A human may also actuate a robotic arm or other semi-automated mechanism to complete a handoff. Manual / semi-automated handoffs may be substituted for automated handoffs in any of Embodiments 1-4, 6, 7.

[0105] Embodiment 6 - Fully Automated Handoffs

[0106] This embodiment employs robotic transfer mechanisms — whether dedicated pick-and-place robots, robotic arms mounted on vehicles, or integrated conveyor systems — to execute handoffs without human intervention. The automated handoff system may replace or augment the manual / semi-automated handoffs of Embodiment 5 and is interoperable with any modality, ML engine, tracking means, or secure-holding location in Embodiments 1-4, 7.

[0107] Embodiment 7 - Multi-Purpose Vehicles and Third-Party Integration

[0108] In Embodiment 7, third-party roadway vehicles (e.g., ride-sharing or delivery fleets) operate as autonomous or manually driven “multi-purpose vehicles” within the network. Goods may share cabin space with passengers or cargo. Vehicle location, availability, and cargo capacity data are continuously relayed to the centralized control system for ML-driven scheduling. The multi-purpose vehicle concept may be applied to, or substituted for, the autonomous vehicle component of any of Embodiments 1-6.

[0109] Non-Limiting Cross-Reference Matrix

[0110] For clarity, the following succinct cross-references illustrate exemplary and non-limiting interchangeable features (each arrowdenotes that the referenced feature may be employed in the cited embodiment):• ML Engine (Embodiment 2) — usable in Embodiments 1, 3-7.• Secure Holding Locations (Embodiment 3) — > employable in Embodiments 1, 2, 4-7.P A T E N TDocket No. SROB1PCT• Expanded Tracking (Embodiment 4) — applicable across Embodiments 1-3, 5-7.• Manual / Semi-Automated Handoffs (Embodiment 5) Fully Automated Handoffs (Embodiment 6); either may replace the other in Embodiments 1-4, 7.• Multi-Purpose Vehicles (Embodiment 7) — > substitutable for autonomous vehicles in Embodiments 1-6.

[0111] Unless expressly stated to the contrary, all structural, functional, and procedural details described with respect to any embodiment are intended to be independently and combinatorially employable with any other disclosed embodiment. Such permutations are deemed to be expressly disclosed herein and fall within the scope of the appended claims.

[0112] Other features and advantages of the invention will be apparent from the following detailed description, and from the claims.

[0113] The above examples and embodiments are included for illustrative purposes only and are not intended to limit the scope of the invention. Many variations to those described above are possible. Since modifications and variations to the examples described above will be apparent to those of skill in this art, it is intended that this invention be limited only by the scope of the appended claims.

Claims

P A T E N TDocket No. SROB1PCTCLAIMSWe claim:

1. A multi-modal delivery system for performing goods delivery operations, comprising: a goods transportation network comprised of one or more of: a ground robot configured to navigate and operate safely within pedestrian pathways in a densely populated environment to perform pick-up and drop-off of goods; an autonomous vehicle configured to navigate and operate safely within automobile pathways to perform pick-up and drop-off of goods; an aerial drone configured to transport the goods and to operate safely in a public or private airspace; and / or a manually operated transport mechanism; a hand-off mechanism that provides for the transfer of the goods between transport mechanisms in the goods transportation network, wherein the hand-off mechanism operates in a hand-off location and is manual, semi-automated or automated; a cloud-based centralized control system configured to control and / or actuate the operation of one or more ground robot, autonomous vehicle, aerial drone or manually operated transport mechanism within the goods transportation network; and a computing article based scheduling system operably integrated in connection with the cloud-based centralized control system, wherein the scheduling system receives details of an order for the goods and such details comprise one or more variables that guide (a) the timing of the goods delivery operation within the goods transportation network, (b) a determination of one or more of a plurality of hand-off locations, (c) prioritization of delivery routing of one or more categories of goods, (d) determination of whether a restriction is present or possible during the transport or delivery of the goods that may delay delivery timing, (e) determining if an equipment safety or vandalism risk is present along the delivery route, (f) routing and scheduling of one or more ground robot, autonomous vehicle, aerial drone or manually operated transport mechanism within the goods transportation network in a manner that provides a determination of a route for (i) performing the largest number of deliveries of a plurality of goods in a determined time period, (ii) the fastest delivery of one or more of the goods within a delivery time period, (iii) reducing vandalism risk, and / or (iv) reducing safety risks.P A T E N TDocket No. SROB1PCT2. The system of claim 1, wherein the handoff area is located in an environment suitable for both drone operation and robot navigation or human navigation.

3. The system of claim 1 or 2, wherein the handoff mechanism provides for the transfer of the goods between the ground robot, autonomous vehicle, aerial drone and / or manually operated transport mechanism.

4. The system of any preceding claim, wherein the ground robot, autonomous vehicle, or manually operated transport mechanism is selected for operation in preference to an aerial drone by the scheduling system within an urban environment where aerial drones face operational limitations due to obstacles, noise, and safety concerns.

5. The system of any preceding claim, wherein the aerial drone is selected for operation in preference to a ground robot, autonomous vehicle, or manually operated transport mechanism by the scheduling system in less densely populated environments to transport goods over long distances.

6. The system of any preceding claim, wherein the hand-off mechanism includes an automated mechanism configured to automatically transfer goods between the ground robot, autonomous vehicle, and / or manually operated transport mechanism.

7. The system of any preceding claim, wherein the hand-off between the ground robot, autonomous vehicle, or manually operated transport mechanism is performed by a human operator.

8. The system of any preceding claim, wherein the ground robot, autonomous vehicle, or manually operated transport mechanism are equipped with communication means in operable communication with the scheduling system to coordinate the timing and location of the handoff.P A T E N TDocket No. SROB1PCT9. The system of any preceding claim, wherein the ground robot, autonomous vehicle, or manually operated transport mechanism are equipped with communication means in operable communication between one-another to coordinate the timing and location of the handoff.

10. The system of any preceding claim, wherein the scheduling system operates in conjunction with a machine learning system that accounts for timing, scheduling, re-scheduling and restrictions of past delivery operations and adjusts the function of the scheduling system for future deliveries to improve efficiency.

11. The system of claim 10, wherein improve efficiency includes reduced transit time, increase the number of deliveries in a specific time period, combine multiple goods in a single transport mechanism within the transportation network, navigate government restrictions on the operation of one or more transport mechanism in the transportation network, decrease the impact on pedestrian traffic or roadway traffic within a delivery route, and / or decrease the environmental impact of the operation of the transportation network during delivery and handoff functions.

12. The system of any preceding claim, wherein the scheduling system operates in conjunction with a machine learning system that provides a ranking of suitability of each transport mechanism in the goods transportation network to ensure the integrity of the goods and optimizing the speed of the transport, wherein the ranking determines which transport mechanism in the goods transportation network is selected for use on at least a portion of the route.

13. The system of claim 12, wherein the ranking of suitability includes a consideration of one or of total distance of the delivery, target or allowable duration of the delivery, pick-up and drop-off locations, dynamic availability of each modality including their relative locations, cargo size and type, walkability score, traffic, time of day, weather, legal and regulatory restrictions, and environmental impact.

14. The system of any preceding claim, further comprising a tracking means the goods within the goods transportation network.P A T E N TDocket No. SROB1PCT15. The system of claim 13, wherein the tracking means comprises a barcode, QR code, RFID Tag, NFC Tag, BLE Beacon, UWV communication system, IR Marker, AR Marker, Magnetic Tag, and / or Acoustic Tag and an instrument for identifying and logging the barcode, QR code, RFID Tag, NFC Tag, BLE Beacon, UWV communication system, IR Marker, AR Marker, Magnetic Tag, and / or Acoustic Tag with the scheduling system.

16. The system of claim 3, wherein the scheduling system operates in conjunction with a machine learning system that accounts for timing, scheduling, re-scheduling and restrictions of past delivery operations and adjusts the function of the scheduling system for future deliveries to improve efficiency.

17. The system of claim 16, wherein improve efficiency includes reduced transit time, increase the number of deliveries in a specific time period, combine multiple goods in a single transport mechanism within the transportation network, navigate government restrictions on the operation of one or more transport mechanism in the transportation network, decrease the impact on pedestrian traffic or roadway traffic within a delivery route, and / or decrease the environmental impact of the operation of the transportation network during delivery and handoff functions.

18. The system of claim 3, wherein the scheduling system operates in conjunction with a machine learning system that provides a ranking of suitability of each transport mechanism in the goods transportation network to ensure the integrity of the goods and optimizing the speed of the transport, wherein the ranking determines which transport mechanism in the goods transportation network is selected for use on at least a portion of the route.

19. The system of claim 18, wherein the ranking of suitability includes a consideration of one or of total distance of the delivery, target or allowable duration of the delivery, pick-up and drop-off locations, dynamic availability of each modality including their relative locations, cargo size and type, walkability score, traffic, time of day, weather, legal and regulatory restrictions, and environmental impact.

20. The system of claim 19, wherein the ground robot, autonomous vehicle, or manually operated transport mechanism is selected for operation in preference to an aerial drone by theP A T E N TDocket No. SROB1PCT scheduling system within an urban environment where aerial drones face operational limitations due to obstacles, noise, and safety concerns.

21. The system of claim 19, wherein the aerial drone is selected for operation in preference to a ground robot, autonomous vehicle, or manually operated transport mechanism by the scheduling system in less densely populated environments to transport goods over long distances.

22. The system of claim 18, wherein the hand-off mechanism includes an automated mechanism configured to automatically transfer goods between the ground robot, autonomous vehicle, and / or manually operated transport mechanism.

23. The system of claim 17, wherein the hand-off between the ground robot, autonomous vehicle, or manually operated transport mechanism is performed by a human operator.

24. The system of claim 18, wherein the ground robot, autonomous vehicle, or manually operated transport mechanism are equipped with communication means in operable communication with the scheduling system to coordinate the timing and location of the handoff.

25. The system of claim 18, wherein the ground robot, autonomous vehicle, or manually operated transport mechanism are equipped with communication means in operable communication between one-another to coordinate the timing and location of the handoff.

26. The system of claim 25, wherein the tracking means comprises a barcode, QR code, RFID Tag, NFC Tag, BLE Beacon, UWV communication system, IR Marker, AR Marker, Magnetic Tag, and / or Acoustic Tag and an instrument for identifying and logging the barcode, QR code, RFID Tag, NFC Tag, BLE Beacon, UWV communication system, IR Marker, AR Marker, Magnetic Tag, and / or Acoustic Tag with the scheduling system.

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