POLYHEDRAL GEOFENCES

• US 20190129039A1
• Filed: 10/31/2018
• Published: 05/02/2019
• Est. Priority Date: 10/31/2017
• Status: Active Grant

• Current Assignee: Analytical Mechanics Associates Incorporated, United States of America as represented by the Administrator of NASA
• Original Assignee: Analytical Mechanics Associates Incorporated
• Inventors: Schubert, Matthew, Moore, Andrew
• Granted Patent: US 10,962,650 B2
• Time in Patent Office: Days
• CPC Class Codes: B64U 2201/10 autonomous, i.e. by navigating independently from ground or air stations, e.g. by using inertial navigation systems [INS]
• CPC Class Codes: G01S 17/86 Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
• CPC Class Codes: G01S 17/89 for mapping or imaging
• CPC Class Codes: G01S 17/933 of aircraft or spacecraft
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/106 Change initiated in response to external conditions, e.g. avoidance of elevated terrain or of no-fly zones
• CPC Class Codes: G08G 5/0021 located in the aircraft
• CPC Class Codes: G08G 5/0052 for cruising combined instruments indicating more than one navigational value G01C23/00
• CPC Class Codes: G08G 5/006 in accordance with predefined flight zones, e.g. to avoid prohibited zones
• CPC Class Codes: G08G 5/0069 specially adapted for an unmanned aircraft
• CPC Class Codes: G08G 5/0086 for monitoring terrain radar or analogous systems specially adapted for terrain avoidance G01S13/935
• CPC Class Codes: G08G 5/045 Navigation or guidance aids, e.g. determination of anti-collision manoeuvers radar or analogous systems specially adapted for anti-collision between aircraft G01S13/933

Abstract

Exemplary methods and systems may generate a point cloud data generated polyhedral geofence for use in navigating a vehicle. The polyhedral geofence may be generated, or created, from point cloud data such as lidar data. Further, a vehicle may utilize propulsion devices and controllers for moving the vehicle based on a point map data generated polyhedral geofence.

Claims

1. An aerial vehicle comprising:

a propulsion apparatus comprising at least one engine to provide propulsion; and

a controller comprising one or more processors and operably coupled to the propulsion apparatus to control flight of the vehicle, the controller configured to fly the vehicle using the propulsion apparatus based on at least a lidar-data generated polyhedral geofence.

2. The aerial vehicle of claim 1, further comprising a positioning system used to determine three-dimensional coordinates of the aerial vehicle;

and

wherein the controller flies the aerial vehicle using the propulsion apparatus further based on at least the determined three-dimensional coordinates.

3. The aerial vehicle of claim 2, wherein the positioning system comprises a lidar sensor system to sense distance between the aerial vehicle and one or more structures.

4. The aerial vehicle of claim 3, wherein the controller determines three-dimensional coordinates based on at least the lidar-data generated polyhedral geofence and the distance between the aerial vehicle and the one or more structures.

5. The aerial vehicle of claim 2, wherein the positioning system comprises a global positioning system (GPS) to determine the three-dimensional coordinates, wherein the GPS comprises an antenna to receive GPS signals.

6. The aerial vehicle of claim 1, wherein the controller flies the aerial vehicle to avoid one or more objects defined by the lidar-data generated polyhedral geofence.

7. The aerial vehicle of claim 1, wherein the controller flies the aerial vehicle according to a flight path to inspect one or more objects defined by the lidar-data generated polyhedral geofence.

8. The aerial vehicle of claim 7, wherein the controller flies the vehicle to within a selected inspection distance from the one or more objects for inspection.

9. The aerial vehicle of claim 1, further comprising a sensor to gather inspection data from one or more objects defined by the lidar-data generated polyhedral geofence.

10. The aerial vehicle of claim 1, wherein the lidar-data generated polyhedral geofence is a 2.5-dimensional (2.5D) polyhedral geofence comprising a plurality of 2.5D polyhedrons, wherein each of the plurality of 2.5D polyhedrons are defined by a polygonal area extending from a ground surface to a maximum altitude.

11. The aerial vehicle of claim 1, wherein the lidar-data generated polyhedral geofence is a 3-dimensional (3D) polyhedral geofence comprising a plurality of 3D polyhedrons, wherein each of the plurality of 3D polyhedrons are defined by a polyhedral volume.

12. A method of flying an aerial vehicle comprising:

flying the vehicle based on at least a lidar-data generated polyhedral geofence.

13. The method of claim 12, further comprising determining three-dimensional coordinates of the aerial vehicle, wherein flying the aerial vehicle is further based on at least the determined three-dimensional coordinates.

14. The method of claim 13, further comprising sensing distance between the aerial vehicle and one or more structures.

15. The method of claim 14, wherein determining three-dimensional coordinates is based on at least the lidar-data generated polyhedral geofence and the distance between the aerial vehicle and the one or more structures.

16. The method of claim 13, further comprising receiving GPS signals, wherein determining three-dimensional coordinates is based on at least the received GPS signals.

17. The method of claim 12, further comprising flying the aerial vehicle to avoid one or more objects defined by the lidar-data generated polyhedral geofence.

18. The method of claim 12, further comprising flying the aerial vehicle according to a flight path to inspect one or more objects defined by the lidar-data generated polyhedral geofence.

19. The method of claim 18, further comprising flying the aerial vehicle to within a selected inspection distance of the one or more objects for inspection.

20. The method of claim 12, further comprising detecting a defect in one or more objects defined by the lidar-data generated polyhedral geofence.

21. The method of claim 12, wherein the lidar-data generated polyhedral geofence is a 2.5-dimensional (2.5D) polyhedral geofence comprising a plurality of 2.5D polyhedrons, wherein each of the plurality of 2.5D polyhedrons are defined by a polygonal area extending from a ground surface to a maximum altitude.

22. The method of claim 12, wherein the lidar-data generated polyhedral geofence is a 3-dimensional (3D) polyhedral geofence comprising a plurality of 3D polyhedrons, wherein each of the plurality of 3D polyhedrons are defined by a polyhedral volume.

23. A method of generating a polyhedral geofence for a vehicle comprising:

receiving lidar data; and

generating a polyhedral geofence usable by a vehicle based on the lidar data.

24. The method of claim 23, wherein the lidar-data generated polyhedral geofence is a 2.5-dimensional (2.5D) polyhedral geofence comprising a plurality of 2.5D polyhedrons, wherein each of the plurality of 2.5D polyhedrons are defined by a polygonal area extending from a ground surface to a maximum altitude.

25. The method of claim 23, wherein the lidar-data generated polyhedral geofence is a 3-dimensional (3D) polyhedral geofence comprising a plurality of 3D polyhedrons, wherein each of the plurality of 3D polyhedrons are defined by a polyhedral volume.

26. The method of claim 23, wherein the lidar-data generated polyhedral geofence comprises at least one 2.5-dimensional polyhedron and at least one 3-dimensional polyhedron.

27. The method of claim 23, wherein generating the polyhedral geofence from the lidar data comprises:

determining a set of triplets from the lidar data, each triplet of the set of triplets representing a latitude, a longitude, and an altitude of a point of an object;

determining one or more features based on the set of triplets;

determining one or more polyhedrons, each polyhedron fitted to one of the one or more features; and

generating the polyhedral geofence comprising the determined one or more polyhedrons.

28. The method of claim 27, wherein determining the one or more features comprises determining a grid for each of the one or more features based on a minimum feature spacing of the latitude and longitude of each triplet of the set of triplets, the grid comprising a polygonal area.

29. The method of claim 27, wherein determining the one or more polyhedrons comprises:

determining a maximum altitude for each of the one or more features; and

determining a shape and size of each of the one or more polyhedrons based on a polygonal area and the maximum altitude.

30. The method of claim 27, wherein determining the one or more features comprises clustering the set of triplets using cylindrical modeling comprising:

grouping the set of triplets into neighborhoods based on a feature radius; and

merging the neighborhoods into the one or more features based on an eigenvector-aligned cylindrical filter and an eigenvector angle alignment filter.

31. The method of claim 27, wherein determining the one or more polyhedrons comprises determining a minimum bounding box for each of the one or more features.

32. The method of claim 27, wherein generating the one or more polyhedral geofences comprises expanding the one or more polyhedrons based on a safe proximity distance, wherein the safe proximity distance is configured to provide a safe distance for the vehicle to move therefrom.