Aerial Vehicle Touchdown Detection

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.