Aerial vehicle touchdown detection

Patent metadata

Field	Value
Publication number	US-10996683-B2
Application number	US-201916272132-A
Country	US
Kind code	B2
Filing date	Feb 11, 2019
Priority date	Feb 9, 2018
Publication date	May 4, 2021
Grant date	May 4, 2021

Abstract

A technique is introduced for touchdown detection during autonomous landing by an aerial vehicle. In some embodiments, the introduced technique includes processing perception inputs with a dynamics model of the aerial vehicle to estimate the external forces and/or torques acting on the aerial vehicle. The estimated external forces and/or torques are continually monitored while the aerial vehicle is landing to determine when the aerial vehicle is sufficiently supported by a landing surface. In some embodiments, semantic information associated with objects in the environment is utilized to configure parameters associated with the touchdown detection process.

First claim

What is claimed is:

1. A method of for landing an unmanned aerial vehicle (UAV), the method comprising:
estimating, by a processor based on perception inputs, external forces and/or external torques acting on the UAV while the UAV is descending to land on a physical surface in a physical environment;
determining, by the processor based on the estimated external forces and/or external torques, that the UAV is in contact with the physical surface;
generating, by the processor, a first control command configured to cause a propulsion system of the UAV to gradually reduce thrust over a period of time;
monitoring, by the processor, changes in the estimated external forces and/or external torques as the propulsion system gradually reduces thrust over the period of time;
determining, by the processor based on the monitoring that the UAV is supported by the physical surface; and
generating, by the processor, a second control command configured to cause the propulsion system to power down in response to determining that the UAV is supported by the physical surface.

2. The method of claim 1 , wherein the perception inputs include sensor data from sensors onboard the UAV.

3. The method of claim 1 , wherein the perception inputs include data output by any one or more of:
an image capture device onboard the UAV;
an accelerometer onboard the UAV;
a gyroscope onboard the UAV;
an inertial measurement unit (IMU) onboard the UAV;
a state observer; or
the propulsion system.

4. The method of claim 1 , wherein the perception inputs do not include data from a tactile force sensor.

5. The method of claim 1 , wherein the perception inputs include sensor data from sensors onboard the UAV, the method further comprising:
processing the sensor data to generate semantic information associated with the physical environment; and
adjusting a parameter used to determine that the UAV is supported by the physical surface based on the semantic information.

6. The method of claim 1 , wherein determining that the UAV is supported by the physical surface includes processing information regarding the changes in the estimated external forces and/or external torques using a machine learning model.

7. The method of claim 6 , further comprising:
training the machine learning model based on data gathered by the UAV during one or more previous landings.

8. The method of claim 1 , wherein the processor begins estimating the external forces and/or external torques acting on the UAV in response to determining that the UAV is within a threshold proximity to the physical surface in the physical environment.

9. The method of claim 1 , wherein estimating the external forces and/or external torques acting on the UAV includes estimating a magnitude and location on a body of the UAV where the external forces and/or external torques are applied.

10. The method of claim 1 , further comprising:
continuing to monitor, by the processor, changes in the estimated external forces and/or external torques after the propulsion system has powered down;
detecting, by the processor, based on the continued monitoring, that the UAV is no longer supported by the physical surface; and
generating, by the processor, a third control command configured to cause the propulsion system to power up to cause the UAV to take off.

11. The method of claim 1 , wherein estimating the external forces and/or external torques acting on the UAV is further based on one or more physical properties of the UAV.

12. The method of claim 1 , wherein determining that the UAV is supported by the physical surface includes determining whether the physical surface is a ground surface in the physical environment or a hand of a person that has caught the UAV.

13. The method of claim 1 , further comprising:
before landing, receiving, by the processor, an input indicative of a user selection of a type of physical surface that the UAV will land on; and
adjusting, by the processor, a parameter that is applied when determining that the UAV is supported by the physical surface based on the input.

14. The method of claim 13 , wherein the parameter is associated with a machine learning model that is used to process information regarding the changes in the estimated external forces and/or external torques acting on the UAV.

15. The method of claim 13 , wherein the type of physical surface is selected from a list that includes: a substantially level surface, a sloped surface, or a moving surface.

16. The method of claim 1 , wherein generating any of the first control command or the second control command includes:
generating a behavioral objective; and
inputting the behavioral objective into a motion planner configured to process a plurality of behavioral objectives to generate a planned trajectory;
wherein the first control command and/or second control command are generated based on the planned trajectory.

17. An unmanned aerial vehicle (UAV) comprising:
a propulsion system;
a sensor device; and
a navigation system communicatively coupled to the sensor device and the propulsion system, the navigation system configured to:
to estimate, based on sensor data received from the sensor device, external forces and/or external torques acting on the UAV while the UAV is in flight through a physical environment;
determine, based on the estimated external forces and/or external torques, that the UAV is in contact with a physical surface in the physical environment;
cause the propulsion system to gradually reduce thrust over a period of time;
monitor changes in the estimated external forces and/or external torques as the propulsion system gradually reduces thrust over the period of time;
determine, based on the monitoring, that the UAV is supported by the physical surface; and
cause the propulsion system to power down in response to determining that the UAV is supported by the physical surface.

18. The UAV of claim 17 , wherein the sensor device includes any of:
an image capture device;
an accelerometer;
a gyroscope;
an inertial measurement unit (IMU); or
a current sensor coupled to the propulsion system.

19. The UAV of claim 17 , wherein the sensor device is not a tactile force sensor.

20. The UAV of claim 17 , wherein to estimate the external forces and/or external torques acting on the UAV, the navigation system is configured to:
process semantic information associated with physical objects in the physical environment with the sensor data,
wherein the estimate of the external forces and/or external torques acting on the UAV is further based on the semantic information.

21. The UAV of claim 17 , further comprising:
a flight controller configured to:
receive a planned trajectory from the navigation system; and
output control commands to the propulsion system to cause the UAV to autonomously fly along the planned trajectory.

22. The UAV of claim 17 , further comprising:
a wireless communication interface for communicating wirelessly with a mobile device.

23. The UAV of claim 17 , wherein the propulsion system includes:
a plurality of electronic rotor devices;
wherein the sensor device includes a current sensor for sensing an electric current at any one or more of the plurality of electronic rotor devices.

24. The UAV of claim 17 , wherein the navigation system is further configured to:
cause the propulsion system to increase thrust to a takeoff level in response to determining that

Assignees

• Skydio Inc

Inventors

• O'Flaherty Rowland Wilde
• Tomic Teodor
• Martirosyan Hayk
• Bachrach Abraham Galton
• Holtz Kristen Marie
• Zhu Jack Louis

Classifications

• B64U2201/10

  autonomous, i.e. by navigating independently from ground or air stations, e.g. by using inertial navigation systems [INS] · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/09

  Supervised learning · CPC title

• G05D1/654

  Landing (docking at a base station G05D1/661) · CPC title