Velocity control for an unmanned aerial vehicle

• US 10,001,778 B2
• Filed: 09/27/2016
• Issued: 06/19/2018
• Est. Priority Date: 09/05/2014
• Status: Active Grant

• Current Assignee: SZ DJI Technology Co., Ltd. (d/b/a DJI) (iFlight Technology Company Limited)
• Original Assignee: SZ DJI Technology Co., Ltd. (d/b/a DJI) (iFlight Technology Company Limited)
• Inventors: Hu, Xiao, Liu, Ang, Zhou, Guyue, Pan, Xuyang
• Primary Examiner(s): Beaulieu, Yonel
• Application Number:
  US15/277,455
• Publication Number:
  US 20170052541A1
• Time in Patent Office: 630 Days
• CPC Class Codes: B64C 39/024 of the remote controlled vehicle type, i.e. RPV
• 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: B64U 2201/20 Remote controls
• CPC Class Codes: G05D 1/0011 associated with a remote control arrangement
• 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 1/042 specially adapted for aircraft
• CPC Class Codes: G05D 1/102 specially adapted for vertical take-off of aircraft
• CPC Class Codes: G05D 13/00 Control of linear speed; Control of angular speed; Control of acceleration or deceleration, e.g. of a prime mover
• CPC Class Codes: G08G 5/0069 specially adapted for an unmanned aircraft
• CPC Class Codes: G08G 5/04 Anti-collision systems
• 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

Systems and methods for controlling an unmanned aerial vehicle within an environment are provided. In one aspect, a system comprises one or more sensors carried on the unmanned aerial vehicle and configured to receive sensor data of the environment and one or more processors. The one or more processors may be individually or collectively configured to: determine, based on the sensor data, an environmental complexity factor representative of an obstacle density for the environment; determine, based on the environmental complexity factor, one or more operating rules for the unmanned aerial vehicle; receive a signal indicating a desired movement of the unmanned aerial vehicle; and cause the unmanned aerial vehicle to move in accordance with the signal while complying with the one or more operating rules.

Claims

1. A system for controlling an unmanned aerial vehicle within an environment, the system comprising:

one or more sensors carried on the unmanned aerial vehicle; and

one or more processors individually or collectively configured to;

determine, based on data from the one or more sensors, an environmental complexity factor representative of an obstacle density for the environment,

determine, based on the environmental complexity factor, a first set of operating rules for the unmanned aerial vehicle,

detect, based on data from the one or more sensors, a change in the environmental complexity factor corresponding to a change in the obstacle density for the environment, and

modify the first set of operating rules based on the change in the environmental complexity factor to provide a second set of operating rules for the unmanned aerial vehicle.

2. The system of claim 1, wherein the one or more sensors comprise at least one of the following:

a vision sensor, a lidar sensor, or an ultrasonic sensor.

3. The system of claim 1, wherein the one or more sensors comprise a plurality of different sensor types.

4. The system of claim 1, wherein the one or more sensors are configured to receive the data, and wherein the data is indicative of the obstacle density for the environment.

5. The system of claim 1, wherein the environmental complexity factor is determined based on a three-dimensional digital representation of the environment generated using the sensor data.

6. The system of claim 5, wherein the three-dimensional digital representation comprises a three-dimensional point cloud or an occupancy grid.

7. The system of claim 1, wherein the first set of operating rules comprises a first set of velocity rules and the second set of operating rules comprises a second set of velocity rules.

8. The system of claim 7, wherein at least one of the first and second sets of velocity rules is determined based on a minimum braking distance for the unmanned aerial vehicle.

9. The system of claim 7, wherein the first and second sets of velocity rules each comprise a velocity limit for the unmanned aerial vehicle.

10. The system of claim 9, wherein when the change in the environmental complexity factor corresponds to a decrease in the obstacle density, the velocity limit of the second set of velocity rules is greater than the velocity limit of the first set of velocity rules.

11. A method for controlling an unmanned aerial vehicle within an environment, the method comprising:

receiving, with aid of a processor, sensor data of the environment from one or more sensors carried on the unmanned aerial vehicle;

determining, based on the sensor data, an environmental complexity factor representative of an obstacle density for the environment,

determining, based on the environmental complexity factor and with aid of the processor, a first set of operating rules for the unmanned aerial vehicle,

detecting, based on the sensor data, a change in the environmental complexity factor corresponding to a change in the obstacle density for the environment, and

modifying the first set of operating rules based on the change in the environmental complexity factor to provide a second set of operating rules for the unmanned aerial vehicle.

12. The method of claim 11, wherein the one or more sensors comprise at least one of the following:

a vision sensor, a lidar sensor, or an ultrasonic sensor.

13. The method of claim 11, wherein the one or more sensors comprise a plurality of different sensor types.

14. The method of claim 11, wherein the sensor data is indicative of the obstacle density for the environment.

15. The method of claim 11, further comprising the environmental complexity factor is determined based on a three-dimensional digital representation of the environment generated using the sensor data.

16. The method of claim 15, wherein the three-dimensional digital representation comprises a three-dimensional point cloud or an occupancy grid.

17. The method of claim 11, wherein the first set of operating rules comprises a first set of velocity rules and the second set of operating rules comprises a second set of velocity rules.

18. The method of claim 17, wherein at least one of the first and second sets of velocity rules is determined based on a minimum braking distance for the unmanned aerial vehicle.

19. The method of claim 17, wherein the first and second sets of velocity rules each comprise a velocity limit for the unmanned aerial vehicle.

20. The method of claim 19, wherein when the change in the environmental complexity factor corresponds to a decrease in the obstacle density, the velocity limit of the second set of velocity rules is greater than the velocity limit of the first set of velocity rules.