Systems and methods for scheduling and navigating unmanned aerial vehicles

Abstract

A system for dispatching and navigating an unmanned aerial vehicle (UAV) to a target location includes a UAV and a navigation module, the navigation module including a processor and a memory, the memory storing a 3D map including the target location and machine-readable instructions that, when executed by the navigation module processor, cause the processor to perform a method including: identifying a location of the UAV relative to the 3D map; receiving a target location input; identifying a target location relative to the 3D map; generating at least one potential route connecting the location of the UAV and the target location; assigning an evaluation score to the at least one potential route according to at least one route evaluation criterion; selecting a potential route having a highest evaluation score as a preferred route; and transmitting the preferred route to the UAV.

Description

[0001] Cross-reference to related applications

[0002] This application claims the priority benefit of U.S. Provisional Patent Application Serial No. 63 / 127,469, filed December 18, 2020, the entire contents of which are incorporated herein by reference.

[0003] References

[0004] All publications and patent applications mentioned in this specification are incorporated herein by reference in their entirety, as if each individual publication or patent application were specifically and individually indicated to be incorporated herein by reference in its entirety. Technical Field

[0005] This disclosure generally relates to the field of navigation and control systems, and more specifically, to the field of autonomous navigation of unmanned aerial vehicles (UAVs). This document describes systems and methods for scheduling and navigating UAVs. Background Technology

[0006] Unmanned aerial vehicles (UAVs) (often referred to as "drones") have the potential to become powerful tools for disaster and emergency response teams. When equipped with cameras or other sensors, UAVs offer a relatively inexpensive and convenient means of acquiring information about an ongoing disaster or emergency without endangering human actors or more expensive equipment. However, the difficulties in deploying and navigating UAVs prevent their widespread use in this role.

[0007] Flying a UAV is no simple task. It can require hours of training to properly educate professionals on the UAV's high maneuverability and vulnerability to wind and weather conditions. Additionally, in many places, particularly in residential areas, various laws prohibit UAVs from entering certain airspaces for security (e.g., airports) or privacy reasons. Therefore, the UAV pilot must additionally be familiar with the areas where he or she can navigate the drone. Finally, during emergency response operations requiring manually operated UAVs, one member of the team must be fully committed to piloting the drone for the entire flight time. In some jurisdictions, such as rural areas, the response team may not have the personnel to carry out such a limited operation.

[0008] Previously existing automated UAVs have also been affected by similar issues. Some UAVs can maintain a constant altitude in mild weather conditions, but struggle in severe weather conditions or in areas where the terrain exhibits rapid changes in elevation or sudden sharp obstacles (such as very mountainous areas or mountainous regions, or those characterized by isolated but large groups of trees). These types of UAVs have a significant risk of colliding with stationary objects (such as the aforementioned hillsides and trees). Equipping UAVs with sophisticated digital vision systems to avoid these hazards greatly increases the cost of UAVs, therefore, due to the fear of damaging or destroying drones, response teams are discouraged from taking on sufficient risk with the UAVs they might need during emergency response situations.

[0009] Therefore, there is a need for a new, useful, and cost-effective system for scheduling and navigating UAVs that overcomes at least the aforementioned limitations. Summary of the Invention

[0010] In some embodiments, one aspect of the disclosure herein includes a system for scheduling and navigating an unmanned aerial vehicle (UAV) to a target location, the system comprising: a UAV; and a navigation module in communication with the UAV, the navigation module comprising: a navigation module processor; and a navigation module memory storing a 3D map including the target location and machine-readable instructions such that, when executed by the navigation module processor, the processor performs a method comprising: identifying the position of the UAV relative to the 3D map; receiving a target location input; identifying the target location relative to the 3D map; generating at least one potential route connecting the position of the UAV and the target location; assigning evaluation scores to the at least one potential route according to at least one route evaluation criterion; selecting the potential route with the highest evaluation score as a preferred route; and transmitting the preferred route to the UAV; and wherein the UAV includes at least one of a UAV memory and a UAV processor or a UAV microcontroller in communication with the UAV memory, the UAV memory storing machine-readable instructions such that, when executed by the UAV processor or the UAV microcontroller, the UAV processor or the UAV microcontroller performs a method comprising: receiving the preferred route from the navigation module; and activating the propulsion mechanism of the UAV to maneuver the UAV according to the preferred route.

[0011] In some embodiments, the system further includes a user equipment communicating with the navigation module and the UAV. In another embodiment, the user equipment transmits target location input to the navigation module after receiving user input. In some embodiments, the UAV further includes at least one reconnaissance sensor, and machine-readable instructions stored in the UAV memory further instruct the UAV processor or UAV microcontroller to: acquire sensor data from the at least one reconnaissance sensor and transmit at least a portion of the sensor data to the user equipment. In another embodiment, the at least one reconnaissance sensor is selected from the group consisting of: cameras, infrared cameras, microphones, acoustic sensors, LiDAR sensors, ultrasonic sensors, sonar, radar, gyroscopes, electrochemical toxic gas sensors, thermometers, humidity sensors, proximity sensors, atmospheric pressure sensors, radiation sensors, or combinations thereof.

[0012] In some embodiments, the UAV and navigation module communicate via at least one of the following: cellular, Wi-Fi, radio frequency, infrared frequency, optical system, laser system, or satellite network. In some embodiments, the UAV and navigation module communicate via at least two of the following: cellular, Wi-Fi, radio frequency, infrared frequency, optical system, laser system, or satellite network. In some embodiments, the UAV, navigation module, and user equipment communicate via at least one of the following: cellular, Wi-Fi, radio frequency, infrared frequency, optical system, laser system, or satellite network. In other embodiments, the UAV, navigation module, and user equipment communicate via at least two of the following: cellular, Wi-Fi, radio frequency, infrared frequency, optical system, laser system, or satellite network.

[0013] In some embodiments, the 3D map includes at least one of LiDAR data or photogrammetric calculations. In other embodiments, the 3D map also includes zone indicator labels, which include at least one of the following: geofence no-fly zones, landing or take-off zones, collision risk indicators, weather risk indicators, environmental risk indicators, or combinations thereof.

[0014] In some embodiments, the navigation module is physically attached to the UAV. In other embodiments, the navigation module is electronically integrated into the UAV and communicates with the UAV via circuitry. In yet another embodiment, the navigation module is physically separate from the UAV. In still another embodiment, the navigation module is one or more computing devices on a cloud network system. In yet another embodiment, the navigation module is a virtual machine. In yet another embodiment, the user equipment includes the navigation module. In yet another embodiment, the user equipment is a virtual machine.

[0015] In some embodiments, multiple potential routes are generated based on at least one route constraint criterion, which includes at least one of the following: a collision safety buffer zone, total route distance or time, maximum altitude, at least one geofenced no-fly zone, remaining battery life of the UAV, or a combination thereof. In some embodiments, at least one route evaluation criterion includes at least one of the following: total route distance or time, minimum altitude change, maximum altitude, duration of travel time exceeding a predetermined altitude threshold, collision risk indicator, weather risk indicator, environmental risk indicator, or a combination thereof. In other embodiments, assigning evaluation scores to at least one potential route based on at least one route evaluation criterion further includes analyzing at least one route evaluation criterion using artificial intelligence or machine learning techniques.

[0016] In some embodiments, machine-readable instructions stored on the navigation module further instruct the navigation module processor to: receive at least one of updated 3D map data, geofenced no-fly zones, landing or territories, collision risk indicators, weather risk indicators, or environmental risk indicators; and store at least one of the updated 3D map data, geofenced no-fly zones, landing or territories, collision risk indicators, weather risk indicators, and environmental risk indicators into the navigation module's memory. In some embodiments, the navigation module processor identifies the UAV's position relative to the 3D map using global coordinate data.

[0017] In some embodiments, machine-readable instructions stored in the navigation module memory further instruct the navigation module processor to: receive flight position data of the UAV during at least a portion of the UAV's flight along a preferred route; compare the flight position data with the preferred route to identify whether a route deviation has occurred; when a route deviation has been identified, calculate a corrected route connecting the UAV's position to a target position; and transmit the corrected route to the UAV; and wherein the machine-readable instructions stored in the UAV memory further instruct the UAV processor or UAV microcontroller to: receive the corrected route from the navigation module; and activate the propulsion system to maneuver the UAV according to the corrected route. In some embodiments, the corrected route is calculated based on at least one route constraint criterion, which includes at least one of the following: a collision safety buffer zone, total route distance or time, maximum altitude, at least one geofence no-fly zone, the UAV's remaining battery life, or a combination thereof. In other embodiments, the corrected route is calculated by analyzing at least one route constraint criterion using artificial intelligence or machine learning techniques. In some embodiments, the flight position data includes global coordinate data.

[0018] In some embodiments, machine-readable instructions stored in the navigation module memory further instruct the navigation module processor to: receive flight position data from the UAV during at least a portion of the UAV's flight; match the position data with locations on a 3D map; generate at least one suggested exploration route based on at least one exploration criterion, the at least one exploration criterion including at least one of: predicted reconnaissance sensor detection improvement, collision safety buffer, total route distance or time, maximum altitude, or a combination thereof; display at least one suggested exploration route on a display of the user equipment; receive a selected exploration route from user input; and transmit the selected exploration route to the UAV; and wherein the machine-readable instructions stored in the UAV memory further instruct the UAV processor or UAV microcontroller to: receive the selected exploration route from the navigation module; and activate the UAV's propulsion system to maneuver the UAV according to the selected exploration route. In some embodiments, at least one suggested exploration route is also based on at least one route constraint criterion, the at least one route constraint criterion including at least one of: collision safety buffer, total route distance or time, maximum altitude, at least one geofence no-fly zone, remaining battery life of the UAV, or a combination thereof. In some embodiments, the flight position data includes global coordinate data.

[0019] In some embodiments, machine-readable instructions stored in the navigation module memory further instruct the navigation module processor to: assign risk assessment scores to at least one suggested exploration route according to at least one exploration risk assessment criterion, the at least one exploration risk assessment criterion including at least one of the following: minimum altitude change, maximum altitude, duration of travel time exceeding a predetermined altitude threshold, collision risk indicator, weather risk indicator, environmental risk indicator, or a combination thereof; and display the corresponding risk assessment score for each suggested exploration route on a display of the user equipment. In other embodiments, assigning risk assessment scores to at least one suggested exploration route further includes analyzing at least one exploration risk assessment criterion using artificial intelligence or machine learning techniques. In some embodiments, the machine-readable instructions stored in the navigation module memory further instruct the navigation module processor to: identify whether at least one suggested exploration route fails to meet a predetermined risk assessment score threshold; and, if identified, delete at least one suggested exploration route that fails to meet the predetermined risk assessment score. In yet another embodiment, the machine-readable instructions stored in the UAV memory further instruct the UAV processor or UAV microcontroller to: receive a manual override command from the user equipment; and operate the UAV according to manual operation input.

[0020] In some embodiments, another aspect of the disclosure herein includes a computer-implemented method for scheduling and navigating an unmanned aerial vehicle (UAV) to a target location, the method comprising: identifying the position of the UAV relative to a 3D map; receiving target location input; identifying the target location relative to the 3D map; generating at least one potential route connecting the position of the UAV and the target location; assigning evaluation scores to the at least one potential route according to at least one route evaluation criterion; selecting the potential route with the highest evaluation score as the preferred route; and transmitting the preferred route to the UAV.

[0021] In some embodiments of the method, the 3D map includes at least one of LiDAR data or photogrammetric calculations. In other embodiments, the 3D map also includes zone indicator labels, which include at least one of the following: geofence no-fly zones, landing zones, landing areas, collision risk indicators, weather risk indicators, environmental risk indicators, or combinations thereof.

[0022] In some embodiments of the method, multiple potential routes are generated based on at least one route constraint criterion, which includes at least one of the following: a collision safety buffer zone, total route distance or time, maximum altitude, at least one geofence no-fly zone, the remaining battery life of the UAV, or a combination thereof. In some embodiments, at least one route evaluation criterion includes at least one of the following: total route distance or time, minimum altitude change, maximum altitude, duration of travel time exceeding a predetermined altitude threshold, a collision risk indicator, a weather risk indicator, an environmental risk indicator, or a combination thereof. In other embodiments, assigning evaluation scores to at least one potential route based on the route evaluation criteria further includes analyzing at least one route evaluation criterion using artificial intelligence or machine learning techniques. In some embodiments, the location of the UAV is identified using global coordinate data relative to a 3D map.

[0023] In some embodiments, the method further includes: receiving flight position data of the UAV during at least a portion of the flight of the UAV along a preferred route; comparing the flight position data with the preferred route to identify whether a route deviation has occurred; when a route deviation has been identified, calculating a corrected route connecting the UAV's position to a target position; and transmitting the corrected route to the UAV. In some embodiments, the corrected route is calculated based on at least one route constraint criterion, which includes at least one of the following: a collision safety buffer zone, total route distance or time, maximum altitude, at least one geofence no-fly zone, the UAV's remaining battery life, or a combination thereof. In some embodiments, the flight position data includes global coordinate data. In other embodiments, the corrected route is calculated by analyzing at least one route constraint criterion using artificial intelligence or machine learning techniques.

[0024] In some embodiments, the method further includes: receiving flight position data from the UAV during at least a portion of the UAV's flight; matching the position data with locations on a 3D map; generating at least one suggested exploration route based on at least one exploration criterion, the at least one exploration criterion including at least one of the following: predicted reconnaissance sensor detection improvement, collision safety buffer, total route distance or time, maximum altitude, or a combination thereof; displaying at least one suggested exploration route on a display of a user device; receiving a selected exploration route from user input; and transmitting the selected exploration route to the UAV. In some embodiments, the at least one suggested exploration route is also based on at least one route constraint criterion, the at least one route constraint criterion including at least one of the following: collision safety buffer, total route distance or time, maximum altitude, at least one geofence no-fly zone, remaining battery life of the UAV, or a combination thereof. In some embodiments, the flight position data includes global coordinate data.

[0025] In some embodiments, the method further includes: assigning a risk assessment score to at least one suggested exploration route according to at least one exploration risk assessment criterion, the at least one exploration risk assessment criterion including at least one of the following: minimum altitude change, maximum altitude, duration of travel time exceeding a predetermined altitude threshold, collision risk indicator, weather risk indicator, environmental risk indicator, or a combination thereof; and displaying the corresponding risk assessment score for each suggested exploration route on a display of a user device. In other embodiments, assigning a risk assessment score to at least one suggested exploration route further includes analyzing at least one exploration risk assessment criterion using artificial intelligence or machine learning techniques.

[0026] In some embodiments, the method further includes: identifying whether at least one suggested exploration route fails to meet a predetermined risk assessment score threshold; and, when identified, deleting at least one suggested exploration route that fails to meet the predetermined risk assessment score. Attached Figure Description

[0027] The foregoing is an overview, and therefore, is necessarily limited in detail. The following description, in conjunction with various embodiments and with reference to the accompanying drawings, describes the foregoing aspects, as well as other aspects, features, and advantages of the invention.

[0028] Figure 1 A block diagram of various components of one embodiment of the system is shown.

[0029] Figures 2A to 2C Block diagrams illustrating various embodiments of system components are shown.

[0030] Figure 3 An animation showing one embodiment of the system during its intended use is illustrated.

[0031] Figure 4 A method for generating a preferred route for a UAV to reach a target location is shown.

[0032] Figure 5 A method for monitoring the flight of a UAV to a target location to detect route deviations and correcting them when they occur is shown.

[0033] Figure 6 The method is shown for monitoring the location of a UAV in flight, generating and suggesting exploration maneuvers to the user, and performing them based on the selected maneuvers.

[0034] The embodiments shown are merely examples and are not intended to limit this disclosure. The schematic diagrams are for illustrating features and concepts and are not necessarily drawn to scale. Detailed Implementation

[0035] The foregoing is an overview and therefore necessarily limited in detail. The aforementioned aspects, as well as other aspects, features, and advantages of the invention, will now be described in conjunction with various embodiments. The inclusion of the following embodiments is not intended to limit this disclosure to these embodiments, but rather to allow those skilled in the art to make and use the contemplated invention. Other embodiments may be utilized and modifications may be made without departing from the spirit or scope of the subject matter presented herein. The aspects of this disclosure as described and illustrated herein can be arranged, combined, modified, and designed in a variety of different conceptual forms, all of which are expressly covered and form part of this disclosure.

[0036] This document discloses systems and methods for scheduling and navigating UAVs. In many embodiments, the system and method allow UAVs to navigate semi-autonomously to target locations of interest (such as locations where disasters or emergencies are occurring, e.g., wildfires, building fires, floods, etc.) with greater ease of use than current systems. In many embodiments, utilizing a navigation module that stores at least three-dimensional (3D) map data, the system and method allow for a more "hands-off" approach to scheduling and navigating UAVs while still avoiding obvious collision hazards and other flight limitations, and enabling and assisting the user in inputting exploratory and alternative flight maneuvers. In some embodiments, the UAV used may be a commercially available unmanned aerial vehicle (UAV) modified with specific hardware. In other embodiments, the UAV may be custom-built to perform the features described herein.

[0037] As discussed herein, these systems and methods can be used for emergency and disaster response, but they can also be used, additionally or alternatively, for any suitable application where semi-autonomous navigation of UAVs is desired, such as aerial photography, delivery services, and recreational or military purposes. As used herein, the terms “UAV” and “unmanned aerial vehicle” will be considered synonymous and can be used interchangeably throughout.

[0038] In many embodiments, the devices and methods described herein employ coordinates from one or more global or regional coordinate systems (e.g., satellite navigation systems). These systems include, but are not limited to, the Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), BeiDou Navigation Satellite System (BDS), Galileo, Quasi-Zenith Satellite System (QZSS), and Indian Regional Navigation Satellite System (IRNSS), as well as any other current or future systems that provide explicit coordinates about a given location on Earth, with or without the use of satellites. As used herein, the term “global coordinates” is intended to refer to coordinates from any of the aforementioned or similar systems or their equivalents. In many embodiments, global coordinates will be GPS coordinates; however, those skilled in the art will understand that the use of the term “GPS coordinates” herein is not limiting, at least for the reasons stated above.

[0039] Systems and equipment

[0040] The system functions to schedule and navigate the UAV to the target location. For example... Figure 1As shown, in many embodiments, system 100 may include a UAV 102 communicating 125 with navigation module 152. UAV 102 may include a UAV processor or microcontroller 104 communicating with UAV memory 106, which may store various machine-readable instructions executable by the UAV processor or microcontroller 104. UAV 102 may additionally include a propulsion system (not shown) (such as any number of rotors or engines), a power supply (not shown) for powering the propulsion system, a housing (not shown) providing structural strength for the UAV, and all wiring and electronics necessary to make the UAV operable, as understood by those skilled in the art. UAV memory 106 may store various machine-readable instructions executable by UAV processor or microcontroller 104, enabling UAV 102 to operate its propulsion system in a controlled manner as understood by those skilled in the art, in addition to the specific tasks described herein.

[0041] In another embodiment, UAV 102 may include at least one reconnaissance sensor 108 that collects various data about its environment. The reconnaissance sensor 108 may include, but is not limited to, a camera, infrared camera, microphone, acoustic sensor, LiDAR sensor, ultrasonic, sonar, radar, gyroscope, electrochemical toxic gas sensor, thermometer, humidity sensor, proximity sensor, atmospheric pressure sensor, radiation sensor, or combinations thereof. The at least one reconnaissance sensor 108 may collect data and store it locally on UAV memory 106. In other embodiments, the UAV may transmit data collected by the at least one reconnaissance sensor 108 to navigation module 152 or to another device, such as user equipment (not shown). In some embodiments, the UAV may transmit data collected by the at least one reconnaissance sensor 108 to one or more devices that are part of a cloud computing system as described herein. In some embodiments, the cloud computing system may run one or more virtual machines or components.

[0042] In many embodiments, navigation module 152 includes navigation module processor 154 and navigation module memory 156 storing machine-readable instructions executable by navigation module processor 154 and 3D map data 160. In some embodiments, 3D map data 160 may be a LiDAR map of a region (e.g., a LiDAR topographic map). In other embodiments, 3D map data 160 may include additional data, such as photogrammetric and other digital visual computation data that generate terrain and structure information. In various embodiments, 3D map data 160 may describe one or more of the following: ground surface topography and the extent and height of tree cover and other obstacles (e.g., utility poles, streetlights, traffic lights, etc.). In some embodiments, the 3D map data includes cross-references or alignments of LiDAR map data with global coordinates. In these embodiments, global coordinates may be used to find or return one or more of LiDAR terrain, satellite imagery, or other associated data for a given location. In some embodiments, the navigation module may be a virtual machine or may include one or more virtual components.

[0043] In some embodiments, the 3D map data 160 also includes zone indicator labels, which are assigned values ​​that can be read and manipulated by the system described herein. Zone indicator labels represent additional considerations useful for navigating certain areas of the 3D map data 160. In some embodiments, these zone indicator labels may include, but are not limited to, geofenced no-fly zones, landing or descent zones, collision risk indicators, weather risk indicators, environmental risk indicators, or combinations thereof. For example, a geofenced no-fly zone indicator label may inform the navigation module during its calculation, as discussed below, that the UAV should not enter the area (e.g., the area is marked as an airport where launching a UAV is dangerous). In some embodiments, geofenced no-fly zones may also have a maximum altitude set, meaning that the UAV is permitted to fly within the specific area as long as it remains below a predetermined altitude. In some embodiments, geofenced no-fly zones may be temporary or permanent. In various embodiments, geofenced no-fly zones may be automatically incorporated based on pilot notification (NOTAM) messages or temporary flight restrictions (TFRs) from relevant aviation and government agencies. Landing or landing zone indicator tags can inform the navigation module of areas designated as safe for materials (e.g., packages for delivery, supplies, life jackets, etc.) to land or land on. In many embodiments, landing or landing zone indicator tags represent areas that are generally not dangerous and are sufficiently open and accessible to the UAV. Example areas that can be tagged using landing or landing zone indicator tags include, but are not limited to, rooftops of buildings and open spaces. For example, in some embodiments, collision risk indicators can indicate areas where there is a significant risk of accidental collision but which are insufficient to be represented solely in the 3D map data 160. For example, areas with numerous power lines or telephone lines or thin tree branches can be marked using collision risk indicators. In many embodiments, weather risk indicators can indicate where hazardous weather conditions (e.g., very strong winds, hail, lightning, etc.) are currently present and thus indicate a risk to the UAV. In many embodiments, environmental risk indicators can indicate any other risks inherent to a particular location in the 3D map data 160, which are not yet described. For example, areas currently experiencing wildfires can be marked using environmental risk indicators to indicate areas with high heat or low visibility due to smoke. In some embodiments, environmental risk indicators can also be used to mark areas where the 3D map data 160 has been suspected of being defective, inaccurate, and / or altered since its collection (e.g., a portion of forest after a forest fire). In some embodiments, collision risk indicators, weather risk indicators, and / or environmental risk indicators may include scalar values ​​that describe a greater or lesser risk compared to other indicators of their type.In other embodiments, the aforementioned zone indicator labels may be collectively or subsets of each other into a single value to represent a combined risk of UAVs in that area of ​​the 3D map data 160 that can be considered by the system 100 during route calculation, as described herein. Additional types of zone indicator labels may be included without departing from the scope of this disclosure. In various embodiments, the 3D map data (i.e., LiDAR or photogrammetric calculations with or without zone indicator labels) may be provided to the system via manual input and / or automatic upload (including from integrated third-party systems).

[0044] In some embodiments, system 100 may store common route data as part of 3D map data 160. In these embodiments, the common route data represents a previously calculated preferred route between frequently used start points and frequently used destination locations of UAV 102 (e.g., see...). Figure 4 In this way, system 100 can save time by using public route data to provide previously calculated preferred routes as potential routes when navigation module 152 identifies the relevant origin and destination locations, rather than regenerating previously used routes. In many embodiments, the potential routes provided by public route data will then be analyzed against instantaneous 3D map data (e.g., weather and environmental risk indicators) and may be adjusted or rejected before being transmitted to UAV 102 as the preferred route for a given situation.

[0045] In many embodiments, the navigation module memory 156 is capable of receiving updated 3D map data 160. In many of these embodiments, when the navigation module memory 156 receives updated 3D map data 160, it appropriately uses the updated data 160 for any future operations, unless otherwise instructed to retrieve and utilize outdated data 160. The updated 3D map data may include new LiDAR map portions (e.g., new LiDAR scans of newly constructed buildings or cleared forests since the previous scan), new photogrammetric calculations, and new or updated zone indicator labels. In some embodiments, updates to certain portions of the 3D map data 160 (e.g., new LiDAR scans) may occur relatively infrequently compared to other portions of the 3D map data (e.g., weather indicator labels may be updated according to a real-time update schedule in some embodiments). In other embodiments, new landing or territorial zone indicator labels may be provided in real-time as updated 3D map data while the UAV 102 is en route to a nearby location. In another embodiment, at least one reconnaissance sensor 108 may collect new 3D map data 160 while en route to the target location. In these embodiments, the navigation module 152 may immediately analyze this newly collected 3D map data 160 to generate an updated or alternative route for the UAV 102. In some embodiments, artificial intelligence (AI) or machine learning (ML) techniques may be employed to analyze the new 3D map data 160 (e.g., visual images of the location) collected en route by at least one reconnaissance sensor 108 to further inform decisions made by the navigation module 152. For example, in one embodiment, the reconnaissance sensor 108 may collect new LiDAR scans that reveal previously unknown obstacles or open areas to the navigation module 152. In another example, the reconnaissance sensor 108 may detect and monitor weather or environmental conditions that contribute new or updated weather or environmental risk indicators to the 3D map data.

[0046] UAV 102 and navigation module 152 communicate with each other 125. In some embodiments, each also communicates with one or more user devices (not shown). In some embodiments, the user device may be a virtual user device. A wide variety of user devices may be employed, including those with augmented reality (AR) and virtual reality (VR) capabilities as described herein. In other embodiments, a single navigation module 152 may communicate with multiple UAVs 102. In some embodiments, a single navigation module 152 may communicate with multiple UAVs 102 and guide the multiple UAVs to the same target location. In other embodiments, a single navigation module 152 may communicate with multiple UAVs and guide the multiple UAVs to a single target location. In yet another embodiment, UAV 102 may communicate with one or more individual UAVs 102 125. In some embodiments, UAV 102 and navigation module 152 communicate with each other 125 via at least one of the following: cellular, Wi-Fi, radio frequency, infrared frequency, optical system, laser system, or satellite network. In other embodiments, multiple UAVs 102 communicate with each other 125 via at least one of the following: cellular, Wi-Fi, radio frequency, infrared frequency, optical system, laser system, or satellite network. When this document refers to components of system 100 (e.g., UAV 102 and navigation module 152) communicating with each other, those skilled in the art will understand that the components include the necessary hardware and store any necessary machine-readable instructions to utilize the communication protocol or method. In this way, in some embodiments where appropriate, UAV 102 and navigation module 152 may be considered to each include a communication module (not shown). In other embodiments, the communication protocol or method may be selected based on a 3D map, zone indicator labels, and the availability of the communication protocol. In some embodiments, if a protocol is unavailable, communication may be switched to another protocol or method.

[0047] In other embodiments, UAV 102 and navigation module 152 communicate with each other 125 via at least two of the following: cellular, Wi-Fi, radio frequency, infrared frequency, optical system, laser system, or satellite network. By communicating via multiple communication protocols or methods, redundancy is built into system 100 in the event of failure of any part of system 100.

[0048] In some embodiments, UAV 102, navigation module 152, and one or more user equipment communicate with each other 125 via at least one of the following: cellular, Wi-Fi, radio frequency, infrared frequency, optical system, laser system, or satellite network. In other embodiments, UAV 102, navigation module 152, and one or more user equipment communicate with each other 125 via at least two of the following: cellular, Wi-Fi, radio frequency, infrared frequency, optical system, laser system, or satellite network. Additionally, in some embodiments, various components of system 100 communicate simultaneously via multiple communication protocols or methods for different purposes. For example, a satellite network may be used for unidirectional transmission of global coordinate information, while a cellular network may be used for sending commands and receiving data from at least one reconnaissance sensor 108. In other embodiments, location data regarding the location of UAV 102 and / or one or more user equipment may be transmitted to navigation module 152 via communication protocols other than satellite networks to allow system 100 to operate in emergency situations where a suitable satellite network is unavailable. In other embodiments, RFID tags and readers may be implemented on UAV 102 and other devices or objects (e.g., user equipment, delivery equipment or packages, other UAVs, etc.) for detection and identification by UAV 102 or navigation module 152. In some of these embodiments, RFID tags and readers may facilitate the platooning of multiple UAVs 102 or the identification of a package by a single UAV among multiple packages for delivery capacity.

[0049] In some embodiments, UAV 102 and navigation module 152 are physically separate devices. In these embodiments, they can then communicate 125 via a wireless system as described above. In other embodiments, navigation module 152 is physically attached to UAV 102. This can be done by mechanically fastening a housing (not shown) containing navigation module 152 to UAV 102. In some versions of this embodiment, navigation module 152 may be adapted to draw its power from a separate power source (not shown) or from one or more of the same power sources (not shown) of UAV 102. In some embodiments where navigation module 152 is fastened to UAV 102, UAV 102 and navigation module 152 can still communicate 125 via a wireless system as described above.

[0050] In other embodiments, the navigation module 152 is electronically integrated into and circuit-communicates with the UAV 102. In some embodiments, the terms "electronically integrated" and "circuit-communicating" are uniform within a single electronic system, sharing their characteristics and at least a portion of the circuit system. For example, in embodiments where the navigation module 152 is electronically integrated into and circuit-communicates with the UAV 102, a single processor or microcontroller performs all the steps described herein for both the UAV processor or microcontroller 104 and the navigation module processor 154. In similar embodiments, a single memory may exist, in addition to storing the 3D map data 160, to store machine-readable instructions to be executed by the UAV processor or microcontroller 104 and those instructions to be executed by the navigation module processor 154. In many embodiments, electronically integrating the navigation module 152 into and circuit-communicating with the UAV 102 can significantly reduce communication time delays between the two. In embodiments where the navigation module 152 is electronically integrated into and circuit-communicates with the UAV 102, the UAV 102 may be considered to include the navigation module 152.

[0051] In other embodiments, navigation module 152 includes a plurality of navigation module processors 154 and navigation module memory 156, which operate as a cloud computing system, communicate 125 with UAV 102, and in some embodiments communicate with user equipment (not shown). In these embodiments, the hardware of navigation module 152 is not exposed to the same hazards as UAV 102 during flight. Additionally, the use of a cloud computing system can accelerate route calculation by navigation module 152, as described herein in some embodiments. In some embodiments, the cloud computing system may include various virtual machines or virtual components. In these embodiments, virtual machines and components may emulate various hardware components and software operations that contribute to the performance of the systems and methods described herein, including but not limited to navigation modules and user equipment. In still further embodiments, the navigation module may employ artificial intelligence (AI) and / or machine learning (ML) techniques, including but not limited to those involving computer vision, image processing and pattern discovery, recognition and classification, and those used for route optimization. In some embodiments, a single navigation module 152 may communicate 125 with and navigate a plurality of UAVs 102.

[0052] Various user devices can be used in many embodiments. In some embodiments, the user device may be a tablet computer, mobile device (e.g., a mobile phone), personal computer, laptop computer, augmented reality (AR) device, virtual reality (VR) device, wearable device (e.g., glasses, watch, etc.), etc. In other embodiments, the user device may be a virtual machine (e.g., a virtual user device). In some embodiments, the user device includes a display that shows various information about UAV 102, including, but not limited to, the location of UAV 102, one or more actual or potential flight paths of UAV 102, and any data collected by the UAV's reconnaissance sensor 108. In some embodiments, the display may include a display of an AR device, such as a pair of glasses with an AR heads-up display (HUD) or a similar HUD displayed on the windshield of a vehicle. In other embodiments, the display may include a display of a VR device, such as a pair of virtual reality goggles with or without additional peripheral devices. In various embodiments, a user can input user input via a VR or AR user device by performing physical gestures with or without additional wearable or peripheral devices, such as gloves containing markers or cameras. In other embodiments, the user equipment is capable of receiving user input from a user and submitting it to at least one of UAV 102 and / or navigation module 152. This input (e.g., voice, text, haptic feedback, etc.) may include, but is not limited to, selection of a target location and / or selection of one or more flight paths for the UAV, as described herein. In yet another embodiment, the input may include manual overriding commands and subsequent manual control inputs allowing the user equipment to manually navigate and manipulate the UAV 102. In some embodiments, a single user equipment may provide one or more of the above and / or additional functions. In other embodiments, multiple user equipments communicate with UAV 102 and navigation module 152, each user equipment having a subset of one or more of the above or additional functions to implement various technical features of the user equipment as described herein. For example, a first user equipment may include a display for displaying information about UAV 102 and may transmit flight path selections to UAV 102 or navigation module 152, while a second user equipment may send manual overriding commands and manual control inputs to UAV 102 and / or navigation module 152.

[0053] Figures 2A to 2C Various embodiments of a system including a UAV, a navigation module, and at least one user device are described. For example... Figure 2AAs shown, in many embodiments, system 200a may include physically separate UAV 202a, navigation module 204a, and user equipment 206a (all communicating with each other 210a). In some embodiments, user equipment 206a may include multiple user equipments. In some embodiments, navigation module 204a may be a cloud computing system, as described herein. In other embodiments, navigation module 204a may be physically attached to UAV 202a but not electronically integrated with or in circuit communication with it, as described herein. In these embodiments, separate communication channels 210a may be required between all three of UAV 202a, navigation module 204a, and user equipment 206a. Any two of UAV 202a, navigation module 204a, and user equipment 206a may communicate with each other 210a via at least one of the following: cellular, Wi-Fi, radio frequency, infrared frequency, optical system, laser system, or satellite network, as described herein in some embodiments. In other embodiments, any two of UAV 202a, navigation module 204a, and user equipment 206a may communicate with each other 210a via at least two of the following: cellular, Wi-Fi, radio frequency, infrared frequency, optical system, laser system, or satellite network, as described herein. In some embodiments, all three of UAV 202a, navigation module 204a, and user equipment 206a may communicate with each other 210a via at least one of the following: cellular, Wi-Fi, radio frequency, infrared frequency, optical system, laser system, or satellite network, as described herein. In still other embodiments, all three of UAV 202a, navigation module 204a, and user equipment 206a may communicate with each other 210a via at least two of the following: cellular, Wi-Fi, radio frequency, infrared frequency, optical system, laser system, or satellite network, as described herein.

[0054] Figure 2BAn alternative embodiment of system 200b is shown, wherein UAV 202b includes a navigation module 204b as described herein. Because the navigation module 204b is electronically integrated with and circuit-communicates with UAV 202b, system 200b can be considered to require consideration only of communication 210b between user equipment 206b and UAV 202b. In some embodiments, user equipment 206b may be multiple user equipments. In some embodiments, UAV 202b and user equipment 206b may communicate with each other 210b via at least one of the following: cellular, Wi-Fi, radio frequency, infrared frequency, optical system, laser system, or satellite network, as described herein. In other embodiments, UAV 202b and user equipment 206b may communicate with each other 210b via at least two of the following: cellular, Wi-Fi, radio frequency, infrared frequency, optical system, laser system, or satellite network, as described herein.

[0055] Figure 2C Alternative embodiments of system 200c are shown, wherein navigation module 204c is electronically integrated with and in circuit communication with user equipment 206c. In these embodiments, user equipment 206c may be considered to include navigation module 204c. In some embodiments, user equipment 206c may be multiple user equipments, only one of which includes navigation module 204c. In other embodiments, user equipment 206c may be multiple user equipments, each including a portion of the technical features of navigation module 204c, as described herein. In these embodiments, the multiple user equipments may all communicate with each other 210c in addition to communicating 210c with UAV 202c. In some embodiments, UAV 202c and user equipment 206c may communicate with each other 210c via at least one of the following: cellular, Wi-Fi, radio frequency, infrared frequency, optical system, laser system, or satellite network, as described herein. In other embodiments, UAV 202c and user equipment 206c can communicate with each other 210c via at least two of the following: cellular, Wi-Fi, radio frequency, infrared frequency, optical system, laser system, or satellite network, as described herein.

[0056] exist Figures 2A to 2C In various embodiments, one or more UAVs 202a, 202b, 202c, navigation modules 204a, 204b, 204c, and user equipment 206a, 206b, 206c can be configured to be compatible with various third-party software modules and hardware components, thereby allowing the system 200 to be customizable for specific use cases.

[0057] Figure 3An animation depicts one embodiment of system 300 during its intended use. User 301 (such as an incident commander in an emergency response team) wants to deploy UAV 302 to a target location 303 where an emergency is currently occurring in order to acquire data from at least one reconnaissance sensor (not shown) on UAV 302. User 301 can input the target location 303 into system 300 via user equipment 306, which communicates with UAV 302 and navigation module 304 310. Using its 3D map data (not shown), navigation module 304 can calculate a preferred route (i.e., flight path) to dispatch UAV 302 to target location 303, as described herein. As described herein, navigation module 304 can additionally perform autonomous checks to see if UAV 302 deviates from the preferred route and correct the course accordingly. Additionally, as described herein, in various embodiments, navigation module 304 can suggest exploration routes to user 301 via user equipment 306.

[0058] method

[0059] Figure 4 An embodiment of a method 400 for generating a preferred route for a UAV to a target location is described. As used herein, the terms "route" and "flight path" are considered equivalent and can be used interchangeably. In many embodiments, machine-readable instructions stored in the navigation module's memory can cause the navigation module to execute... Figure 4 Method 400. As shown in the figure, an embodiment of method 400 includes identifying the position of the UAV relative to a 3D map in block S402, receiving target position input in block S404, identifying the target position relative to the 3D map in block S406, generating at least one potential route connecting the UAV's position and the target position in block S408, assigning evaluation scores to the at least one potential route according to route evaluation criteria in block S410, selecting the potential route with the highest evaluation score as the preferred route in block S412, and transmitting the preferred route to the UAV in block S414. Method 400 is intended for disaster and emergency response teams, but can be additionally or alternatively used for any suitable application in which semi-autonomous navigation of the UAV is desired. In many embodiments, the generation of the preferred route can be considered as an optimization problem for risk analysis. Those skilled in the art will understand that various specific computational methods can be employed, including but not limited to simplex, fuzzy logic, and symbiotic organism search (SOS) methods. In yet another embodiment, artificial intelligence (AI) and / or machine learning (ML) techniques can be utilized to perform optimization calculations. The method 400 and subsequent methods described in this paper present the organization of the required analysis and are not intended to be limited to any particular computational method.

[0060] In block S402, method 400 includes identifying the position of the UAV relative to a 3D map. To calculate the route of the UAV to a target location, the navigation module can begin by identifying the UAV's current or starting position. In some cases, the UAV may land at a landing field or any solid surface, while in others, the UAV may be currently in flight, or moving to a new location (e.g., a target location, during a planned patrol, etc.) or maintaining a stable position in the air. In some embodiments, the UAV is deployed from a consistent starting position, such as a defined landing field at the operational base of an emergency response team. In other embodiments, the user may first transport the UAV (e.g., by vehicle) to a single starting point from which the use of the system begins. In still other embodiments, the UAV may be deployed from a mobile vehicle, such as a fire truck en route to an accident. In many embodiments, as those skilled in the art will understand, method 400 employs global coordinate tracking technology and / or similar systems to aid in the execution of block S402; however, in these embodiments, global coordinates or similar identifiers may then be cross-referenced or otherwise further identified relative to a 3D map.

[0061] In block S404, method 400 includes receiving target location input. In many embodiments, the target location input is received from a user device that collects user input. For example, this could come from manual user input via a GUI on the display of a user device (e.g., a tablet or mobile device), or via a phone call to emergency services on a mobile phone, where the phone's global coordinates are transmitted directly with the call. In other embodiments, the target location input can be received from an automated system (such as a computer-aided dispatching (CAD) system) that communicates with a navigation module. Because the user must have initially entered the target location into the CAD system, in some embodiments, the CAD system can also be considered a user device. In yet another embodiment, the target location input can be received from a sensor on any device integrated into the system, either directly or via the CAD or an equivalent system. For example, a smoke detector fully integrated into the system (e.g., integrated into an "Internet of Things" device) can send its global coordinates when smoke is detected. Other examples may include, but are not limited to, motion detectors, microphones, thermometers, and other sensors capable of detecting situations where a UAV is expected to be deployed to its location. In these embodiments, any device with such a sensor can be considered a user device because the user must deploy the device to do so. In various embodiments, third-party systems that can perform emergency detection and subsequent information transmission via predefined parameters can be integrated into the systems disclosed herein to provide target location input. Additionally, in some embodiments, third-party systems that allow the direct transmission of target location input (or its equivalent) from telephone calls to the CAD can also be integrated into the systems disclosed herein. In many embodiments, the target location can be received as global coordinates or a similar geographic location reference technology or format. In some embodiments, the target location can be multiple locations accessed sequentially by the UAV.

[0062] In block S406, method 400 includes identifying the target location relative to a 3D map. In some embodiments, this may include associating global coordinates or other indicators with the location on the 3D map. Those skilled in the art will understand that blocks S402 through S404 can be performed in various orders without departing from the scope of this disclosure.

[0063] In block S408, method 400 includes generating at least one potential route connecting the UAV's location and target locations. Various considerations and techniques (including, but not limited to, AI or ML techniques) can be used to generate at least one potential route. In many embodiments, the at least one potential route takes into account motion in all three dimensions (i.e., changes in latitude, longitude, and altitude) and 3D map data, such that the at least one potential route does not intersect with known obstacles (e.g., trees, buildings, hillsides, etc.). In embodiments where multiple target locations exist, the at least one potential route may include sequential visits to multiple target locations. In other embodiments with multiple target locations, the at least one potential route may be a "patrol" route that allows the UAV to make continuous circular movements through at least a portion of the multiple target locations.

[0064] In another embodiment, at least one potential route is generated based on additional route constraint criteria, which include at least one of the following: collision safety buffer zone, total route distance or time, maximum altitude, at least one geofence no-fly zone, remaining battery life of the UAV, or a combination thereof.

[0065] In some embodiments, a collision safety buffer defines the maximum distance between a potential route and known obstacles, as described by the 3D map data. While in some embodiments the 3D maps generated by LiDAR may be very accurate, in certain cases, it may be of interest to some users to further reduce the risk of collisions by limiting the movement of the UAV to a certain buffer distance from known obstacles. In some embodiments, the collision safety buffer can range from approximately 0.25 meters to approximately 100 meters. In some other embodiments, the collision safety buffer can range from approximately 0.25 meters to approximately 50 meters. In other embodiments, the collision safety buffer can range from approximately 0.25 meters to approximately 10 meters. In still other embodiments, the collision safety buffer can range from approximately 0.25 meters to approximately 5 meters. In yet another embodiment, the collision safety buffer can range from approximately 0.25 meters to approximately 2.5 meters. In still another embodiment, the collision safety buffer can be approximately 1 meter. In some embodiments, different types of obstacles captured by the 3D map can be categorized (e.g., trees, buildings, etc.), and different collision safety buffers can be applied to each type of obstacle. For example, in one embodiment, route constraint criteria may allow at least one potential route to pass within 1m of a building, but only within 5m of a tree. In other embodiments, different tree species may be defined as separate obstacle categories and thus assigned different collision safety buffer zones to account for changes in growth since previous LiDAR scans or other updates to the 3D map.

[0066] In some embodiments, the total route distance or time serves as a route constraint criterion, imposing a limit on the total permissible travel distance or time for at least one potential route. In some embodiments, this limitation may be useful to avoid or reduce certain wear and / or reliability issues for certain UAVs. For example, it may be known that a particular UAV model is significantly affected by rotor damage during flights exceeding five hours; therefore, this route constraint criterion will limit the system from generating potential routes at risk of such wear. In some embodiments, the total permissible travel distance or time may differ for different UAV models. The terms "total route distance" and "total route time" are intended to represent the distance or time required to maneuver the UAV from its starting position to its target position. However, in some embodiments, the proposed exploration route may be designed to loop back to its starting position. In some embodiments, the total route distance can range from approximately 10 m to approximately 10 km as a route constraint criterion. In other embodiments, the total route distance can range from approximately 100 m to approximately 10 km. In still other embodiments, the total route distance can range from approximately 250 m to approximately 10 km. In yet still other embodiments, the total route distance can range from approximately 250 m to approximately 5 km. In other embodiments, the total route distance can range from approximately 100m to approximately 5km. In still other embodiments, the total route distance can range from approximately 100m to approximately 1000m. In additional embodiments, the total route distance can range from approximately 10m to approximately 500m. In some embodiments, the total route time, as a route constraint criterion, can range from approximately 1 minute to approximately 12 hours. In other embodiments, the total route time can range from approximately 5 minutes to approximately 4 hours. In still other embodiments, the total route time can range from approximately 30 minutes to approximately 4 hours. In other embodiments, the total route time can range from approximately 1 hour to approximately 2 hours. In still other embodiments, the total route time can range from approximately 1 hour to approximately 8 hours. In additional embodiments, the total route time can range from approximately 1 hour to approximately 6 hours. In many embodiments, the total route distance or time, as a route constraint criterion, is determined based on the known capacity of the UAV being deployed.

[0067] In some embodiments, as a route constraint criterion, the maximum altitude imposes a limit on the highest altitude at which at least one potential route is permitted. In certain situations, some UAV models may be unreliable or inoperable above certain altitudes, or there may be laws in certain areas prohibiting UAVs from flying above certain altitudes. Therefore, in these embodiments, the maximum altitude route constraint criterion prevents the generation of potential routes that endanger the UAV or violate local regulations. In many embodiments, as a route constraint criterion, the maximum altitude is determined based on the known capacity of the UAV being deployed.

[0068] As described above in some embodiments, geofenced no-fly zones, as route constraint criteria, exclude the generation of potential routes traversing restricted areas defined by zone indicator labels on a 3D map. Additionally, as mentioned above, in some embodiments, maximum altitude constraints are alternatively identified as geofenced no-fly zones with appropriate altitude exceptions.

[0069] In some embodiments, as a route constraint criterion, remaining battery life takes into account the planned flight time based on the UAV's current available battery power and prevents the generation of potential routes that would completely deplete power before reaching their destination. In other embodiments, the remaining battery life route constraint criterion further considers the duration of a complete round trip (i.e., to the location of interest and back to the UAV's origin), thereby preventing the generation of potential round trip routes that would completely deplete power. In yet another embodiment, the remaining battery life route constraint criterion also considers the duration of a complete round trip plus a predetermined amount of hovering, monitoring, and / or exploration time at the target location. In many embodiments, as a route constraint criterion, remaining battery life is determined based on the known capacity of the UAV being deployed.

[0070] In block S410, in many embodiments, method 400 includes assigning evaluation scores to at least one potential route according to route evaluation criteria. In various embodiments, route evaluation criteria may include at least one of the following: total route distance or time, minimum altitude change, maximum altitude, duration of travel time exceeding a predetermined altitude threshold, collision risk indicator, weather risk indicator, environmental risk indicator, or a combination thereof. In many embodiments, the evaluation score of a potential route is a composite of scores applied to each route evaluation criterion for the potential route. In some embodiments, the scores for each route evaluation criterion may be uniformly compiled to generate the evaluation score. In other embodiments, the scores may be weighted according to a predetermined metric or function. In some embodiments, the predetermined metric or function may be generated by AI or ML technology trained on a dataset of exemplary successful and unsuccessful routes having various values ​​for one or more of the route evaluation criteria described above. For example, successful flight paths traversing or approaching various zone indicator labels, mountains, or between buildings may be used as part of the training dataset.

[0071] In many embodiments, total route distance or time is more advantageous as a route evaluation criterion for assessing potential routes with shorter total travel distances or times for the UAV to reach the target location. In many cases, the shorter the route, the faster the UAV can reach the target location, and the time saved is crucial in certain emergency or disaster situations. Additionally, reducing the total flight distance or time of the UAV helps minimize or reduce expected wear and tear on the UAV over multiple flights, thereby reducing maintenance and replacement costs. In various embodiments, the score for the total route distance or time of a potential route can change linearly, quadratically, geometrically, stepwise, or according to another function with increasing distance or time. In many embodiments, a potential route with a shorter total distance or time compared to other potential routes to the same target location will score better for the total route distance or time route evaluation criterion.

[0072] In many embodiments, minimum altitude change is more advantageous as a route evaluation criterion for assessing potential routes with smaller altitude variations. In some embodiments, maintaining a constant altitude or a narrow altitude range can facilitate the collection of consistent and reliable data from one or more reconnaissance sensors of the UAV. In other embodiments, frequent and / or extreme altitude variations may cause greater wear and tear on the UAV compared to stable flight at a constant altitude. In various embodiments, the score for minimum altitude change of a potential route may vary linearly, quadratically, geometrically, stepwise, or according to another function with increasing altitude changes. In many embodiments, potential routes with a constant altitude or a narrower altitude range will score better for the minimum altitude change route evaluation criterion compared to other potential routes to the same target location.

[0073] In many embodiments, maximum altitude is more advantageous as a route evaluation criterion for evaluating potential routes that remain below a predetermined altitude value. In some embodiments, operating at higher altitudes poses a greater risk to the UAV than operating at lower altitudes. In various embodiments, the score for the maximum altitude of a potential route can be linear, quadratic, geometric, stepwise, or according to another function with increasing altitude. In many embodiments, potential routes that remain below a predetermined altitude will score better for the maximum altitude route evaluation criterion.

[0074] In many embodiments, collision risk indicators, weather risk indicators, and environmental risk indicators, individually or in arbitrary combinations, are used as route evaluation criteria to more favorably evaluate potential routes with a lower probability of damage to the UAV. As described above, these indicators are used to quantify various hazards to the UAV that may not be adequately represented by the LiDAR map alone and can be assigned to the map as zone indicator labels. In many embodiments, when a potential route traverses one or more zones labeled using at least one of these risk indicator labels, values ​​representing the risk of those zones can be compiled and considered as route evaluation criteria. Potential routes that spend less flight distance or time within hazardous zones labeled using these risk indicators will score better for collision risk indicator route evaluation criteria, weather risk indicator route evaluation criteria, and / or environmental risk indicator route evaluation criteria compared to other potential routes reaching the same destination. In some embodiments, the values ​​associated with the individual collision risk indicators, weather risk indicators, and environmental risk indicators can be generated using AI or ML techniques trained on datasets of successful and unsuccessful UAV flights using the risk indicator categories.

[0075] Considering one or more route evaluation criteria, method 400 generates and assigns evaluation scores to at least one potential route, the evaluation scores representing the overall optimism of the potential route and its compatibility with predetermined parameters of the route evaluation criteria used. In step S412, method 400 includes selecting the potential route with the highest evaluation score as the preferred route. The term "highest evaluation score" as used herein is intended to indicate the potential route that best meets the parameters of the route evaluation criteria; however, the specific details of the calculation and ranking may vary without departing from the scope of this disclosure. In some embodiments, "highest evaluation score" may be the largest value among the evaluation scores of each generated potential route. In other embodiments, "highest evaluation score" may be the smallest value among the evaluation scores of each generated potential route. Regardless of the details (including but not limited to the various examples and embodiments disclosed herein), the method selects the potential route with the highest evaluation score as defined herein as the preferred route. In various embodiments, method 400 may employ simplex, fuzzy logic, and symbiotic search (SOS) methods to generate the preferred route. In still other embodiments, artificial intelligence (AI) and / or machine learning (ML) techniques may be utilized to compute the preferred route.

[0076] In some embodiments, upon receiving an emergency overrun input, certain route evaluation criteria may be ignored and / or a potential route with a poorer evaluation score may be selected. In many embodiments, the emergency overrun input may be received from user input on a user device or from an automated system such as CAD. For example, method 400 may generate two potential routes across a wildfire to a target location for a stranded hiker. The first route is much faster but poses a greater risk to the UAV because it must fly through hotter, more dangerous sections of fire. The second route is longer but safer because it avoids particularly dangerous areas. In many embodiments, method 400 may select the second route because it will receive a higher evaluation score; however, the emergency overrun input may force method 400 and / or the system executing method 400 to alternatively select the first route. For example, an incident commander at the scene may decide that the time saved by bringing the UAV's sensors to the target location of the stranded hiker is worth the potential fatal risks to the UAV, and he or she may input the emergency overrun input on a user device. In some embodiments of this example, method 400 may be considered to ignore environmental risk indicators as route evaluation criteria. In other embodiments of this example, method 400 can be considered as selecting potential routes with poor evaluation scores. Another illustrative example for emergency overrun inputs could include deploying a UAV beyond its safe return capability, meaning the UAV may crash at the target location or en route, potentially an irrecoverable crash.

[0077] In some embodiments, the method includes examining public route data between the UAV's location and the target location. As described herein, public route data may store one or more preferred routes connecting frequently used origin and frequently used target locations. In this way, method 400 can save time by utilizing previously preferred routes from the public route data as potential routes for new instances, thereby avoiding the need to regenerate old routes each time. In many embodiments, the potential routes provided by the public route data will then be analyzed to obtain transient factors, such as weather or environmental risk indicators as described herein.

[0078] In block S414, method 400 includes transmitting a preferred route to the UAV. In many embodiments, the preferred route is transmitted to the UAV in such a way that the UAV can utilize its processor or microcontroller and memory to activate its propulsion mechanism to maneuver according to the preferred route to reach a target location. Through various embodiments of method 400, the UAV can be automatically scheduled to the target location by simply inputting the target location using 3D map data.

[0079] Figure 5Embodiments of a method 500 for monitoring a UAV's flight to a target location (or on an exploration route, as described herein) to detect route deviations and correct them when they occur are described. In many embodiments, machine-readable instructions stored in the navigation module's memory can cause the navigation module to execute... Figure 5 Method 500. As shown in the figure, an embodiment of method 500 includes receiving flight position data of the UAV during at least a portion of the UAV's flight along a preferred route in block S502, comparing the flight position data with the preferred route in block S504 to identify whether a route deviation has occurred. Depending on whether a route deviation has occurred, method 500 either returns to block S502 or proceeds to block S506. If method 500 proceeds, it includes calculating a corrected route connecting the UAV's position to a target position in block S508, and transmitting the corrected route to the UAV in block S510. In yet another embodiment, AI and / or ML techniques may be used to perform various parts of method 500. The blocks of method 500 and subsequent methods described herein present an organization of the required analysis and are not intended to be limited to any particular computational method.

[0080] In block S502, method 500 includes receiving flight position data of the UAV during at least a portion of the UAV's flight along a preferred route. In many embodiments, the preferred route is determined by... Figure 4 Method 400 or by Figures 1 to 3 Any of these systems generate the data. In an alternative embodiment, the flight position data is data from UAVs along the exploration route described herein, such as data generated by [system name missing]. Figure 6 The data generated by method 600. In some embodiments, flight position data of the UAV's location during flight may be received using global coordinate tracking and / or similar techniques as understood by those skilled in the art. In many embodiments, the flight position data may be cross-referenced or associated with a location on a 3D map. In some embodiments, flight position data may only be received at regular intervals (e.g., 30 seconds, 1 minute, 2 minutes, 5 minutes, 7 minutes, 10 minutes, etc.). In other embodiments, flight position data is received as frequently and continuously as possible, as permitted by the tracking technology employed.

[0081] In block S504, method 500 includes comparing flight position data with a preferred route to identify whether a route deviation has occurred. In many embodiments, when the system as Figure 4When generating, identifying, and transmitting a preferred route to the UAV as in method 400, the system can store a representation compatible with the received location data of the preferred route to be adopted. In some embodiments, this can be done by storing a series of GPS coordinates associated with a 3D map following the preferred route. In other embodiments, this can be done by storing a series of global coordinates associated with the 3D map data as a series of waypoints connected by linear or other vectors. Method 500 compares the most recent flight position data point with the stored preferred route to determine whether the UAV is still on the preferred route. Route deviations can occur in a variety of ways. For example, a sudden strong wind may blow the UAV off course, a bird may accidentally strike the UAV, or the UAV may have mechanical, electrical, or computational defects that prevent or impair its adherence to the initially transmitted preferred route, etc.

[0082] In block S506, if the most recent received flight position data is not aligned with the stored preferred route (i.e., a route deviation has occurred), method 500 proceeds to block S508. If the most recent received flight position data is aligned with the stored preferred route or at least within its deviation tolerance, method 500 returns to block S502 to continue receiving additional and subsequent flight position data for future comparisons. In some embodiments, the deviation tolerance may be from approximately 0.25m to approximately 10m. In other embodiments, the deviation tolerance may be from approximately 0.25m to approximately 5m. In yet another embodiment, the deviation tolerance may be from approximately 1m to approximately 3m. In still another embodiment, the deviation tolerance may be approximately 2m. In some embodiments, specific zones or areas of the 3D map may be limited to narrower or wider deviation tolerances based on zone indicator labels.

[0083] In block S508, method 500 includes calculating a corrected route connecting the UAV's location to a target location. By calculating the corrected route, method 500 seeks to adjust the UAV's flight path to a path that will guide it to the target location. In some embodiments, the corrected route includes the shortest maneuver required to return it to the preferred route. In other embodiments, such as those where significant deviations have occurred, method 500 may calculate a new route to the target location that does not overlap with or partially overlaps with the preferred route. In many embodiments, the calculated corrected route in block S508 may be performed according to similar or identical procedures and considerations as in blocks S408 through S410 (i.e., generating at least one corrected route according to route constraint criteria and evaluating the at least one corrected route according to route evaluation criteria). As described herein, in some embodiments, AI and / or ML techniques may be used to perform analysis of the route evaluation criteria to generate the corrected route.

[0084] In block S510, method 500 includes transmitting a corrected route to the UAV. In many embodiments, the corrected route is transmitted to the UAV in such a way that the UAV can utilize its processor or microcontroller and memory to activate its propulsion mechanism to maneuver according to the corrected route to reach a target location. Through various embodiments of method 500, the UAV can be automatically scheduled to the target location even if it accidentally deviates from the initially provided preferred route.

[0085] Figure 6 Embodiments of a method 600 for monitoring the position of a UAV in flight, generating and suggesting exploratory maneuvers to the user, and executing them based on the selected maneuvers are described. In many embodiments, machine-readable instructions stored in the navigation module memory can cause the navigation module, which communicates with the user equipment, to execute... Figure 6 Method 600. As shown in the figure, an embodiment of this method 600 includes receiving flight position data of the UAV during at least a portion of the UAV's flight in block S602, matching the position data with positions on a 3D map in block S604, generating at least one suggested exploration route based on exploration criteria and route constraint criteria in block S606, optionally, in optional block S608, assigning risk assessment scores to the at least one suggested exploration route according to exploration risk assessment criteria, displaying the at least one suggested exploration route on a display of a user device in block S610, receiving a selected exploration route from user input in block S612, and transmitting the selected exploration route to the UAV in block S614. In yet another embodiment, AI and / or ML technologies may be used to perform various parts of method 600. The blocks of method 600 and subsequent methods described herein present the organization of the required analysis and are not intended to be limited to any particular computational method.

[0086] In block S602, method 600 includes receiving flight position data of the UAV during at least a portion of the UAV's flight. In various embodiments, the flight of the UAV may include, but is not limited to, a portion of movement along a preferred or corrected route to a target location, a portion of patrol maneuvers between multiple target locations, a portion of movement along a selected exploration route as described herein, and hovering or holding maneuvers at a target location. In some embodiments, flight position data of the UAV's location may be received during flight using global coordinate tracking and / or similar techniques as understood by those skilled in the art. In many embodiments, the flight position data may be cross-referenced or associated with a location on a 3D map. In some embodiments, flight position data may only be received at regular intervals (e.g., 30 seconds, 1 minute, 2 minutes, 5 minutes, 7 minutes, 10 minutes, etc.). In other embodiments, flight position data is received as frequently and continuously as possible, as permitted by the tracking technology employed. In still other embodiments, flight position data is received on demand, such as after a user inputs a request for updated flight position data.

[0087] In block S604, method 600 includes identifying the target location relative to a 3D map. In some embodiments, this may include associating GPS coordinates or other indicators with the location on the 3D map.

[0088] In block S606, method 600 includes generating at least one suggested exploration route based on exploration criteria. In many embodiments, exploration criteria include at least one of the following: predicted reconnaissance sensor detection improvements, collision safety buffer zones, total route distance or time, maximum altitude, or combinations thereof. By analyzing at least one of these criteria, method 600 can procedurally identify and generate possible maneuvers to nearby, reachable, and / or beneficial alternative or additional target locations, which can assist the system user.

[0089] In many embodiments, as an exploration criterion, the predicted reconnaissance sensor detection improvement seeks to enhance the reliability or accuracy of measurements taken by one or more reconnaissance sensors mounted on the UAV. For example, a reconnaissance sensor (e.g., a toxic gas sensor) may only make reliable measurements when within a certain distance of a feature of interest (e.g., a wildfire). Using a second reconnaissance sensor, the system can detect that the UAV is not within the necessary distance of the feature of interest. Using the predicted reconnaissance sensor detection improvement as an exploration criterion, method 600 can generate at least one suggested exploration route that would manipulate the UAV to move closer to the feature of interest. In alternative embodiments, the suggested detection route could manipulate the UAV to move further away from the feature of interest or change its orientation toward it to utilize different reconnaissance sensors for more accurate measurements.

[0090] In many embodiments, as an exploration criterion, a collision safety buffer seeks to maximize the proximity of the UAV to features of interest near the target location up to a predetermined safety distance. For example, the initial target location might position the UAV substantially beyond a predetermined collision safety buffer to the nearest obstacle (e.g., at approximately 20m when the safety buffer is set to approximately 5m). Upon arrival, the UAV's position may prove too far to be optimally helpful. In these and similar embodiments, method 600 may generate at least one exploration route that maneuvers the UAV closer to the limits of its collision safety buffer.

[0091] In many embodiments, as an exploration criterion, the total route distance or time seeks to reposition the UAV within a certain maximum distance or flight time, which can provide alternative advantages or better advantages than the original target position. The terms "total route distance" and "total route time" are intended to represent the distance or time required to maneuver the UAV from the starting position to the endpoint of the proposed exploration route. However, in some embodiments, the proposed exploration route may be designed to move in a loop back to its starting position. In these scenarios, the entire loop of the proposed exploration route can be considered as the total route distance or time. In some embodiments, the total route distance can range from approximately 1 m to approximately 10 km as an exploration criterion. In some other embodiments, the total route distance can range from approximately 1 m to approximately 1000 m. In other embodiments, the total route distance can range from approximately 1 m to approximately 100 m. In still other embodiments, the total route distance can range from approximately 1 m to approximately 50 m. In yet another embodiment, the total route distance can range from approximately 1 m to approximately 25 m. In still another embodiment, the total route distance can range from approximately 1 m to approximately 10 m. In additional embodiments, the total route distance can range from approximately 100m to approximately 250m. In some embodiments, the total route time can range from approximately 1 minute to approximately 12 hours. In some other embodiments, the total route time can range from approximately 1 second to approximately 1 hour. In other embodiments, the total route time can range from approximately 1 second to approximately 15 minutes. In still other embodiments, the total route time can range from approximately 1 second to approximately 7 minutes. In yet another embodiment, the total route time can range from approximately 30 seconds to approximately 5 minutes. In still another embodiment, the total route time can range from approximately 5 minutes to approximately 30 minutes. In many embodiments, the total route distance or time is determined as an exploration criterion based on the known capacity of the UAV being deployed. In various embodiments, method 600 can generate at least one suggested exploration route for repositioning the UAV to a location within a predetermined total route distance or time.

[0092] In many embodiments, as an exploration criterion, the maximum altitude seeks to elevate the UAV to a predetermined maximum altitude to gain a potentially better advantage compared to the target location. In some cases, the original target location may be below the maximum permissible altitude, and a higher location may offer better visibility. In many embodiments, the maximum altitude as the exploration criterion is determined based on the known capacity of the UAV being deployed. In these embodiments, method 600 may generate at least one suggested exploration route with an altitude higher than the original target location but below the maximum altitude limit.

[0093] In many embodiments, to avoid navigating the UAV to known obstacles (e.g., trees, buildings, hillsides, etc.), at least one suggested exploration route can be additionally generated based on 3D map data and taking into account route constraint criteria, as described above. Figure 3 As discussed in [the document]. In some embodiments, route constraint criteria may include, but are not limited to, at least one of the following: collision safety buffer zone, total route distance or time, maximum altitude, at least one geofenced no-fly zone, UAV's remaining battery life, or a combination thereof.

[0094] In optional box S608, method 600 may include assigning risk assessment scores to at least one suggested exploration route based on exploration risk assessment criteria. In many embodiments, exploration risk assessment criteria may include, but are not limited to, at least one of the following: minimum altitude change, maximum altitude, duration of travel time exceeding a predetermined altitude threshold, collision risk indicator, weather risk indicator, environmental risk indicator, or a combination thereof. In many embodiments, these exploration risk assessment criteria are at least similar to those described above for... Figure 4 Method 400 discusses those operations related to the route evaluation criteria. As described herein, AI and / or ML techniques may be used in some embodiments to perform the analysis of the exploration risk assessment criteria to generate one or more suggested exploration routes. In some embodiments, Method 600 removes or otherwise disregards any suggested exploration routes that fail to score sufficiently favorably to meet a predetermined risk threshold. Additionally, in some embodiments, Method 600 may additionally compare multiple suggested exploration routes and exclude those that exceed a predetermined route similarity threshold to routes with better ratings. This prevents consideration and subsequent presentation of nearly identical suggested exploration routes from a long list.

[0095] In block S610, method 600 includes displaying at least one suggested exploration route on a display of a user equipment communicating with the system. This informs the user which options are available for exploration. In some embodiments, method 600 also displays each suggested exploration route and its corresponding risk assessment score (if available). This can inform the user which options pose a greater risk to the UAV than other options. In some embodiments, method 600 displays all generated suggested exploration routes. In other embodiments, method 600 displays only those exceeding a predetermined risk threshold as described above. In yet another embodiment, method 600 displays only a subset of the generated suggested exploration routes.

[0096] In block S612, method 600 includes receiving a selected exploration route from user input. In many embodiments, a user interacting with a user input element (e.g., a button, slider, etc.) on a user device communicating with the system can input which of at least one suggested exploration route he or she wants the UAV to perform.

[0097] In block S614, method 600 includes transmitting a selected exploration route to the UAV. In many embodiments, the corrected route is transmitted to the UAV in such a way that the UAV can utilize its processor or microcontroller and memory to activate its propulsion device for maneuvering according to the selected exploration route. Through various embodiments of method 600, the UAV can be semi-automatically scheduled along an exploration route programmatically generated by the system and / or method 600.

[0098] The methods described herein are merely for the purpose of illustrating the boxes presented in a particular order and should not be construed as a limitation. Those skilled in the art will understand that the methods herein can be performed in various orders in many embodiments.

[0099] The preferred embodiments and variations of the systems and methods may be at least partially embodied and / or implemented as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions are preferably executed by computer-executable components, which are preferably integrated with one or more portions of a system and processor on a UAV and / or computing device. The computer-readable medium may be stored on any suitable computer-readable medium, such as RAM, ROM, flash memory, EEPROM, optical devices (e.g., CDs or DVDs), hard disk drives, floppy disk drives, or any suitable device. The computer-executable components are preferably general-purpose or special-purpose processors, but any suitable special-purpose hardware or hardware / firmware combination may alternatively or additionally execute the instructions.

[0100] As used in the specification and claims, the singular forms “a / an” and “the” include both singular and plural references, unless the context clearly indicates otherwise. For example, the term “UAV” may include and be considered to include multiple UAVs. Sometimes, the claims and disclosure may contain terms such as “multiple,” “one or more,” or “at least one”; however, the absence of such terms is not intended to mean and should not be construed as meaning that multiple are not contemplated.

[0101] The terms “approximately” or “roughly”, when used before numerical names or ranges (e.g., to define length or pressure), indicate an approximate value that may vary by (+) or (-) 5%, 1%, or 0.1%. All numerical ranges provided herein include the stated start and end values. The term “substantially” indicates the majority (i.e., greater than 50%) or substantially all of a device, substance, or composition.

[0102] As used herein, the terms "comprising" or "comprises" are intended to mean that an apparatus, system, or method comprises the listed elements and may additionally include any other elements. "Substantially constitutes" means that the apparatus, system, or method comprises the listed elements and excludes other elements that are significant to the combination of the stated purposes. Therefore, a system or method substantially composed of the elements defined herein does not exclude other materials, features, or steps that do not materially affect the essential and novel features of the claimed disclosure. "Comprising" means that the apparatus, system, or method comprises the listed elements and excludes any additional, less important, or incoherent elements or steps. Embodiments defined by each of these transitional terms fall within the scope of this disclosure.

[0103] The examples and illustrations contained herein show specific embodiments in which the subject matter can be practiced by way of illustration and not limitation. Other embodiments may be utilized and derived therefrom, thereby allowing structural and logical substitutions and changes to be made without departing from the scope of this disclosure. Such embodiments of the subject matter of the invention may be referred to individually or collectively by the term "invention" herein merely for convenience, and are not intended to limit the scope of this application to any single invention or inventive concept where more than one invention or inventive concept is actually disclosed. Therefore, although specific embodiments have been illustrated and described herein, any arrangement suitable for achieving the same purpose may replace the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of the various embodiments. Combinations of the above embodiments and other embodiments not specifically described herein will be apparent to those skilled in the art upon reading the above description.

Claims

1. A computer-implemented method (600, 500, 400) for scheduling and navigating an unmanned aerial vehicle (UAV) (302, 202c, 202b, 202a, 102) to a target location (303), the method (600, 500, 400) comprising: Access 3D map data (160), which includes LiDAR map data aligned to global coordinates or photogrammetric calculations; The position of the UAV (302, 202c, 202b, 202a, 102) relative to the 3D map data (160) is identified by the global coordinates; Receive input including the target location (303); The target location (303) is determined relative to the 3D map data (160); Generate at least one suggested route between the location of the UAV (302, 202c, 202b, 202a, 102) and the target location (303), wherein the at least one suggested route is based on at least one exploration criterion, the at least one exploration criterion including at least one of the following: predicted reconnaissance sensor detection improvement, collision safety buffer, total route distance or time, remaining battery life of the UAV (302, 202c, 202b, 202a, 102), maximum altitude or a combination thereof; Risk assessment scores are assigned to the at least one suggested route based on at least one exploration risk assessment criterion, which includes at least one of the following: minimum altitude change, maximum altitude, duration of driving time exceeding a predetermined altitude threshold, collision risk indicator, weather risk indicator, environmental risk indicator, or a combination thereof. Determine whether the at least one suggested route meets or exceeds a predetermined risk assessment score threshold; as well as When the at least one suggested route meets or exceeds the predetermined risk assessment score threshold, the at least one suggested route is selected as the preferred route, and the UAV (302, 202c, 202b, 202a, 102) is automatically dispatched to the target location (303) via the preferred route. The method is characterized in that it further includes: Flight position data of the UAV (302, 202c, 202b, 202a, 102) are received during at least a portion of the flight of the UAV (302, 202c, 202b, 202a, 102) along at least one of the suggested routes; The flight position data is compared with the at least one suggested route to identify whether a route deviation has occurred; When the route deviation has been identified, calculate the corrected route connecting the positions of the UAVs (302, 202c, 202b, 202a, 102) to the target position (303); and The corrected route is transmitted to the UAV (302, 202c, 202b, 202a, 102); and When an emergency over-control input is received, the UAV (302, 202c, 202b, 202a, 102) is automatically dispatched to the target location (303) via a suggested route that does not meet or exceeds the predetermined risk assessment score threshold.

2. The computer-implemented method (600, 500, 400) according to claim 1 further includes receiving sensor data from at least one reconnaissance sensor (108) coupled to the UAV (302, 202c, 202b, 202a, 102); and transmitting at least a portion of the sensor data to a user equipment (306, 206c, 206b, 206a).

3. The computer-implemented method (600, 500, 400) according to claim 2, wherein the at least one reconnaissance sensor (108) is selected from the group consisting of: camera, infrared camera, image sensor, microphone, acoustic sensor, LiDAR sensor, ultrasonic sensor, sonar sensor, radar sensor, gyroscope sensor, electrochemical toxic gas sensor, temperature sensor, humidity sensor, proximity sensor, atmospheric pressure sensor, radiation sensor, or a combination thereof.

4. The computer-implemented method (600, 500, 400) according to claim 1, wherein the method (600, 500, 400) is performed by a navigation module (304, 204c, 204b, 204a, 152).

5. The computer-implemented method (600, 500, 400) according to claim 4, wherein the navigation module (304, 204c, 204b, 204a, 152) is physically attached to the UAV (302, 202c, 202b, 202a, 102).

6. The computer-implemented method (600, 500, 400) according to claim 4, wherein the navigation module (304, 204c, 204b, 204a, 152) is electronically integrated into the UAV (302, 202c, 202b, 202a, 102) and communicates with the UAV (302, 202c, 202b, 202a, 102) via circuit communication (310, 210c, 210b, 210a, 125).

7. The computer-implemented method (600, 500, 400) according to claim 4, wherein the navigation module (304, 204c, 204b, 204a, 152) is one or more computing devices on a cloud network system.

8. The computer-implemented method (600, 500, 400) according to claim 4, wherein the navigation module (304, 204c, 204b, 204a, 152) is a virtual machine.

9. The computer-implemented method (600, 500, 400) according to claim 4, wherein, User equipment (306, 206c, 206b, 206a) communicates with the UAV (302, 202c, 202b, 202a, 102) (310, 210c, 210b, 210a, 125) and includes the navigation module (304, 204c, 204b, 204a, 152).

10. The computer-implemented method (600, 500, 400) of claim 4, further comprising receiving at least one of: updated 3D map data (160), updated geofence no-fly zones, updated landing or territories, updated collision risk indicators, updated weather risk indicators, or updated environmental risk indicators; and In the 3D map data (160), one or more zone indicator labels are updated based on at least one of the following: the updated 3D map data, the updated geofence no-fly zone, the updated landing or landing zone, the updated collision risk indicator, the updated weather risk indicator, and the updated environmental risk indicator.

11. The computer-implemented method (600, 500, 400) of claim 4 further includes receiving the input from a computer-aided scheduling (CAD) system communicating (310, 210c, 210b, 210a, 125) with the navigation modules (304, 204c, 204b, 204a, 152).

12. The computer-implemented method (600, 500, 400) of claim 1 further includes storing at least one suggested route as public route data for future use with associated origin and destination locations.

13. The computer-implemented method (600, 500, 400) of claim 1, wherein assigning risk assessment scores to at least one suggested route further comprises analyzing at least one exploration risk assessment criterion using artificial intelligence or machine learning techniques.

14. The method according to claim 1 (600, 500, 400), further comprising: Flight position data is received from the UAV (302, 202c, 202b, 202a, 102) during at least a portion of the flight of the UAV (302, 202c, 202b, 202a, 102); Match the location data with the location on the 3D map; The at least one suggested route is displayed on the display of the user equipment (306, 206c, 206b, 206a); Receive at least one suggested route selected from the user's (301) input; as well as Transmit at least one of the selected suggested routes to the UAV (302, 202c, 202b, 202a, 102).

15. A system for scheduling and navigating an unmanned aerial vehicle (UAV) to a target location, the system comprising: A navigation module, which communicates with the UAV, includes: Processor; and A memory storing a 3D map including the target location and machine-readable instructions, such that when executed by the processor, the processor performs the following method, the method comprising: Determine the position of the UAV relative to the 3D map, wherein the 3D map includes LiDAR map data aligned to global coordinates or photogrammetric calculations; Receive input indicating the target location; The target location is determined relative to the 3D map; Generate at least one suggested exploration route between the location of the UAV and the target location, wherein the at least one suggested exploration route is based on at least one exploration criterion, the at least one exploration criterion including at least one of the following: predicted reconnaissance sensor detection improvement, collision safety buffer, total route distance or time, maximum altitude or a combination thereof; Risk assessment scores are assigned to the at least one suggested exploration route according to at least one exploration risk assessment criterion, the at least one exploration risk assessment criterion including at least one of the following: minimum altitude change, maximum altitude, duration of travel time exceeding a predetermined altitude threshold, collision risk indicator, weather risk indicator, environmental risk indicator, or a combination thereof. Determine whether the at least one suggested exploration route meets or exceeds a predetermined risk assessment score threshold; and When the at least one suggested exploration route meets or exceeds the predetermined risk assessment score threshold, the at least one suggested exploration route is selected as the preferred route, and the UAV is automatically dispatched to the target location via the preferred route; The method is characterized in that it further includes: Flight position data of the UAVs (302, 202c, 202b, 202a, 102) are received during at least a portion of the flight of the UAVs (302, 202c, 202b, 202a, 102) along the preferred route; The flight position data is compared with the at least one suggested route to identify whether a route deviation has occurred; When the route deviation has been identified, calculate the corrected route connecting the positions of the UAVs (302, 202c, 202b, 202a, 102) to the target position (303); and The corrected route is transmitted to the UAV (302, 202c, 202b, 202a, 102); and When an emergency over-control input is received, the UAV (302, 202c, 202b, 202a, 102) is automatically dispatched to the target location (303) via a suggested route that does not meet or exceeds the predetermined risk assessment score threshold.

16. The system of claim 15, further comprising the UAV and a user equipment communicating with the navigation module and the UAV, wherein the user equipment receives user input at the user equipment and then transmits the input to the navigation module.

17. The system of claim 15, further comprising the UAV, wherein the UAV includes at least one reconnaissance sensor, wherein the machine-readable instructions stored in the memory further instruct the processor to: cause the UAV to acquire sensor data from the at least one reconnaissance sensor; and cause the UAV to transmit the acquired sensor data to a user equipment.

18. The system of claim 17, wherein the at least one reconnaissance sensor is selected from the group consisting of: camera, infrared camera, image sensor, microphone, acoustic sensor, LiDAR sensor, ultrasonic sensor, sonar sensor, radar sensor, gyroscope sensor, electrochemical toxic gas sensor, temperature sensor, humidity sensor, proximity sensor, atmospheric pressure sensor, radiation sensor, or a combination thereof.

19. The system of claim 15, further comprising the UAV, wherein the navigation module is physically attached to the UAV.

20. The system of claim 15, further comprising the UAV, wherein the navigation module is electronically integrated into the UAV and communicates with the UAV via circuitry.

21. The system of claim 15, wherein the navigation module is one or more computing devices on a cloud network system.

22. The system of claim 15, wherein the machine-readable instructions stored on the navigation module further instruct the processor to: receive at least one of: updated 3D map data, updated geofence no-fly zones, updated landing or territories, updated collision risk indicators, updated weather risk indicators, or updated environmental risk indicators; and In the 3D map, one or more zone indicator labels are updated based on at least one of the following: the updated 3D map data, the updated geofence no-fly zone, the updated landing or landing zone, the updated collision risk indicator, the updated weather risk indicator, and the updated environmental risk indicator.

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