Aerial vehicle touchdown detection
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.
1. A method for landing an aerial vehicle, the method comprising:
processing, by a processor, perception inputs of the aerial vehicle to estimate external forces acting on the aerial vehicle while the aerial vehicle is descending to land on a physical surface in a physical environment;
determining, by the processor, based on the estimated external forces that the aerial vehicle is in contact with a physical surface;
generating, by the processor, a first control command configured to cause a propulsion system of the aerial vehicle to reduce thrust;
executing, by the processor, the first control command to cause the propulsion system of the aerial vehicle to reduce thrust;
monitoring, by the processor, changes in the external forces as the propulsion system reduces thrust;
generating, by the processor, a second control command configured to cause the propulsion system to power down in response to determining that the aerial vehicle is supported by the physical surface; and
executing, by the processor, the second control command to cause the aerial vehicle to land.
2. The method of claim 1 , wherein the external forces include external torques.
3. The method of claim 1 , wherein the processor is configured to process the perception inputs using a dynamic model of the aerial vehicle.
4. The method of claim 1 , further comprising:
determining, by the processor, based on the monitoring, that the aerial vehicle is supported by the physical surface.
5. The method of claim 4 , further comprising:
processing the perception inputs to generate semantic information associated with the physical environment; and
adjusting a parameter used to determine that the aerial vehicle is supported by the physical surface based on the semantic information.
6. The method of claim 4 , wherein determining that the aerial vehicle is supported by the physical surface includes processing information regarding the changes in the estimated external forces using a machine learning model.
7. The method of claim 6 , further comprising:
training the machine learning model using data gathered by the aerial vehicle during one or more previous landings.
8. The method of claim 1 , wherein the processor begins estimating the external forces acting on the aerial vehicle in response to determining that the aerial vehicle is within a threshold proximity to the physical surface in the physical environment.
9. The method of claim 1 , wherein estimating the external forces acting on the UAV includes estimating a magnitude and location on a body of the aerial vehicle where the external forces are applied.
10. The method of claim 1 , further comprising:
continuing to monitor, by the processor, changes in the estimated external forces after the propulsion system has powered down;
detecting, by the processor, based on the continued monitoring, that the aerial vehicle 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 aerial vehicle to take off.
11. The method of claim 1 , wherein estimating the external forces acting on the aerial vehicle is further based on one or more physical properties of the aerial vehicle.
12. The method of claim 1 , wherein determining that the aerial vehicle 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 aerial vehicle.
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 aerial vehicle will land on; and
adjusting, by the processor, a parameter that is applied when determining that the aerial vehicle 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 acting on the aerial vehicle.
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. The method of claim 1 , wherein the first control command is configured to cause the propulsion system to gradually reduce thrust over a period of time.
18. The method of claim 1 , wherein the perception inputs include data output by any one or more of:
an image capture device onboard the aerial vehicle;
an accelerometer onboard the aerial vehicle;
a gyroscope onboard the aerial vehicle;
an inertial measurement unit (IMU) onboard the aerial vehicle;
a state observer; or
the propulsion system.
19. A method for landing an aerial vehicle, the method comprising:
continually estimating, based on perception inputs sensed by a non-tactile based sensor device, external forces acting on the aerial vehicle while the aerial vehicle is descending to land on a physical surface in a physical environment;
determining, based on the estimated external forces, that the aerial vehicle is supported by the physical surface;
responsively generating a control command configured to cause the propulsion system to power down; and
executing the control command to cause the propulsion system to power down.
20. An apparatus comprising:
one or more non-transitory computer readable storage media having program instructions stored thereon that, when executed by a processor, direct the processor to:
continually estimate, based on perception inputs sensed by a non-tactile based sensor device, external forces acting on the aerial vehicle while the aerial vehicle is descending to land on a physical surface in a physical environment;
determine, based on the estimated external forces, that the aerial vehicle is supported by the physical surface;
responsively generate a control command configured to cause the propulsion system to power down; and
execute the control command to cause the propulsion system to power down.