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EP-4263350-B1 - METHOD FOR DISPATCHING AND NAVIGATING AN UNMANNED AERIAL VEHICLE

EP4263350B1EP 4263350 B1EP4263350 B1EP 4263350B1EP-4263350-B1

Inventors

  • STASIOWSKI, Donald, Frederick
  • AJMANI, Shaman

Dates

Publication Date
20260506
Application Date
20211217

Claims (13)

  1. A computer-implemented method (600, 500, 400) for dispatching and navigating an unmanned aerial vehicle (UAV) (302, 202c, 202b, 202a, 102) to a target location (303), the method comprising: accessing 3D map data (160) comprising LiDAR map data or photogrammetric calculations aligned to global coordinates; identifying a location of the UAV with respect to the 3D map data (160) via the global coordinates; receiving an input comprising a target location; determining the target location with respect to the 3D map data; generating at least one suggested route between the location of the UAV to the target location, wherein the at least one suggested route is based on at least one exploration criterion comprising at least one of: a predicted scouting sensor detection improvement, a collision safety buffer, a total route distance or time, a remaining battery life of the UAV (302, 202c, 202b, 202a, 102), a maximum altitude, or a combination thereof; assigning to the at least one suggested route a risk evaluation score according to at least one exploration risk assessment criterion comprising at least one of: a minimum altitude change, a maximum altitude, a duration of travel time spent above a predetermined altitude threshold, collision risk indicators, weather risk indicators, environment risk indicators, or a combination thereof; determining whether the at least one suggested route meets or exceeds a predetermined risk evaluation score threshold; when the at least one suggested route meets or exceeds the predetermined risk evaluation score threshold and is selected as a preferred route, causing automatic dispatch of the UAV to the target location via the at least one suggested route; characterised by receiving flight location data of the UAV (302, 202c, 202b, 202a, 102) during at least a portion of flight of the UAV (302, 202c, 202b, 202a, 102) along the at least one suggested route; comparing the flight location data to the at least one suggested route to identify whether a route deviation has occurred; when the route deviation has been identified, calculating a corrected route connecting the location of the UAV (302, 202c, 202b, 202a, 102) to the target location (303); and transmitting the corrected route to the UAV (302, 202c, 202b, 202a, 102); and receiving an urgency override input, and causing automatic dispatch of the UAV (302, 202c, 202b, 202a, 102) to the target location (303) via a suggested route that does not meet or exceed the predetermined risk evaluation score threshold.
  2. The computer-implemented method (600, 500, 400) of claim 1, further comprising receiving sensor data from at least one scouting sensor (108) coupled to the UAV (302, 202c, 202b, 202a, 102); and transmitting at least a portion of the sensor data to a user device (306, 206c, 206b, 206a).
  3. The computer-implemented method (600, 500, 400) of claim 2, wherein the at least one scouting sensor (108) is selected from the group consisting of a camera, an infrared camera, an image sensor, a microphone, an acoustic sensor, a LiDAR sensor, an ultrasonic sensor, a sonar sensor, a radar sensor, a gyroscope sensor, an electrochemical toxic gas sensor, a temperature sensor, a humidity sensor, a proximity sensor, a barometric air pressure sensor, a radiation sensor, or a combination thereof.
  4. The computer-implemented method (600, 500, 400) of 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) of 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) of claim 4, wherein the navigation module (304, 204c, 204b, 204a, 152) is electronically integrated into and in circuit communication (310, 210c, 210b, 210a, 125) with the UAV (302, 202c, 202b, 202a, 102).
  7. The computer-implemented method (600, 500, 400) of 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) of claim 4, wherein the navigation module (304, 204c, 204b, 204a, 152) is a virtual machine.
  9. The computer-implemented method (600, 500, 400) of claim 4, wherein a user device (306, 206c, 206b, 206a) is in communication (310, 210c, 210b, 210a, 125) with the UAV (302, 202c, 202b, 202a, 102) 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 geofenced no-fly zones, updated drop-off or landing zones, updated collision risk indicators, updated weather risk indicators, or updated environment risk indicators; and updating, in the 3D map data (160), one or more zone indicator tags based on the at least one of: the updated 3D map data, the updated geofenced no-fly zones, the updated drop-off or landing zones, the updated collision risk indicators, the updated weather risk indicators, and the updated environment risk indicators.
  11. The computer-implemented method (600, 500, 400) of claim 4, further comprising receiving the input from a computer-aided dispatch (CAD) system in communication (310, 210c, 210b, 210a, 125) with the navigation module (304, 204c, 204b, 204a, 152).
  12. The computer-implemented method (600, 500, 400) of claim 1, wherein assigning to at least one suggested route a risk evaluation score further comprises analyzing at least one exploration risk assessment criterion with an artificial intelligence or machine learning technique.
  13. The computer-implemented method (600, 500, 400) of claim 1 further comprising: matching the location data to a position on a 3D map; displaying the at least one suggested route on a display of a user device (306, 206c, 206b, 206a); receiving a selected at least one suggested route from user (301) input; and transmitting the selected at least one suggested route to the UAV.

Description

TECHNICAL FIELD This disclosure relates generally to the field of navigation and control systems, and more specifically to the field of autonomous navigation of unmanned aerial vehicles (UAVs). Described herein are systems and methods for dispatching and navigating a UAV. BACKGROUND Unmanned aerial vehicles (UAVs), commonly known as "drones," have the potential to be a powerful tool for disaster and emergency response teams. When fitted with a camera or other sensors, UAVs offer a relatively affordable and expedient means to acquire information regarding an ongoing disaster or emergency without endangering a human actor or more expensive equipment. However, the difficulty of deploying and navigating a UAV prevent their widespread adoption for this role. Flying a UAV is no simple task. It can require hours of training in order to properly educate a professional to handle a UAVs' high maneuverability but also vulnerability to wind and weather conditions. Furthermore, in many locales, especially in residential areas, various legislation prohibits the entry of UAVs into certain airspaces for either safety (e.g., airports) or privacy concerns. A pilot of a UAV must therefore be additionally versed in which areas he or she may navigate the drone. Finally, during an emergency response operation that seeks to employ a manually operated UAV, one member of the team must remain fully committed to piloting the drone during its flight time. In certain jurisdictions, such as rural areas, the response team may not have the personnel to spare to that narrow of an activity. Pre-existing automated UAVs suffer from similar problems. Some UAVs are capable of maintaining a constant altitude in gentle weather conditions but will struggle under intense weather conditions or in areas where the terrain exhibits rapid changes in elevation or sudden, sharp obstacles, such as very hilly or mountainous regions or those that feature isolated but large groups of trees. UAVs of these types have an immense risk of crashing into stationary objects, like the aforementioned hillsides and trees. Outfitting a drone with complicated digital vision systems to avoid these hazards dramatically increases the cost of the UAV, thus discouraging response teams to take the adequate risks with the UAV that may be necessary during an emergency response situation due to the fear of damaging or destroying the drone. US2019/0196507 A1 relates to a path planning method and apparatus for an unmanned aerial vehicle, a flight management method and apparatus and an unmanned aerial vehicle. The method includes: determining a start point and an end point of flight of the unmanned aerial vehicle; determining a flight route of the unmanned aerial vehicle based on the start point and the end point; obtaining a height of an obstacle on the flight route; determining whether a height at which the unmanned aerial vehicle is capable of flying is greater than the height of the obstacle; and if yes, flying, by the unmanned aerial vehicle, at a height greater than the height of the obstacle according to the flight route. US2016/0140851 A1 relates to a method for navigation of a drone through a geographical air space, including: identifying a drone within or in proximity to a geographical air space; receiving flight data representing a certain flight path through the geographical air space; evaluating the flight data based on a flight risk map to determine the flight risk through the geographical air space, wherein the flight risk map includes zones, each zone being associated with a certain flight safety score; and one or more of: approving the certain flight path when the flight risk of the drone is within an acceptable risk threshold, blocking the certain flight path when the flight risk of the drone is outside the acceptable risk threshold, and obtaining external control of navigation of the drone to navigate the drone through at least one zone having the acceptable risk threshold. Therefore, there is a need for a new, useful, and cost-effective system for dispatching and navigating a UAV that overcomes at least these above-described limitations. SUMMARY According to the present invention, there is provided a method as set out in claim 1. Optional features are set out in the dependent claims. One aspect of the disclosure herein includes for, in some embodiments, a system for dispatching and navigating an unmanned aerial vehicle (UAV) to a target location 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 comprising the target location and machine-readable instructions such that, when executed by the navigation module processor, cause the processor to perform a method comprising: identifying a location of the UAV with respect to the 3D map; receiving a target location input; identifying the target location with respect to the 3D map; genera