Abstract: Technology for generating and displaying a graphical user interface for operating an unmanned aerial vehicle (UAV) is disclosed herein that generates and updates a representation of a spline flight path. In various implementations, a graphical user interface detects user interactions with a remote control device directing the flight control subsystem of the UAV to record keyframes and to compute a spline based on the keyframes during flight. The graphical user interface displays a real-time perspective of the UAV with a representation of the spline and the keyframes overlaying the view. The graphical user interface continually updates the representation as the UAV flies and when the spline is updated as the keyframes are updated.

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.

Abstract: Technology for operating an unmanned aerial vehicle (UAV) is disclosed herein that allows a drone to be flown along a computed spline, while also accommodating in-flight modifications. In various implementations, a UAV includes a flight control subsystem and an electromechanical subsystem. The flight control subsystem records keyframes during flight and computes a spline based on the keyframes. The flight control subsystem then saves the computed spline for playback, at which time the UAV automatically flies in accordance with the computed spline.

Abstract: Technology for operating an unmanned aerial vehicle (UAV) is disclosed herein that allows a drone to be flown along a computed spline, while also accommodating in-flight modifications. In various implementations, a UAV includes a flight control subsystem and an electromechanical subsystem. The flight control subsystem records keyframes during flight and computes a spline based on the keyframes. The flight control subsystem then saves the computed spline for playback, at which time the UAV automatically flies in accordance with the computed spline.

Abstract: Techniques are described for developing and using applications and skills with autonomous vehicles. In some embodiments, a development platform is provided that enables access to a developer console for developing software modules for use with autonomous vehicles. For example, a developer can specify instructions for causing an autonomous vehicle to perform one or more operations. To control the behavior of an autonomous vehicle, the instructions can cause an executing computer system at the autonomous vehicle to generate calls to an application programming interface (API) associated with an autonomous navigation system of autonomous vehicle. Such calls to the API can be configured to adjust parameters of a behavioral objective associated with a trajectory generation process performed by the autonomous navigation system that controls the behavior of the autonomous vehicle. The instructions specified by the developer can be packaged as a software module that can be deployed for use at autonomous vehicle.

Abstract: Technology for generating and displaying a graphical user interface for operating an unmanned aerial vehicle (UAV) is disclosed herein that generates and updates a representation of a spline flight path. In various implementations, a graphical user interface detects user interactions with a remote control device directing the flight control subsystem of the UAV to record keyframes and to compute a spline based on the keyframes during flight. The graphical user interface displays a real-time perspective of the UAV with a representation of the spline and the keyframes overlaying the view. The graphical user interface continually updates the representation as the UAV flies and when the spline is updated as the keyframes are updated.

Abstract: An autonomous vehicle that is equipped with image capture devices can use information gathered from the image capture devices to plan a future three-dimensional (3D) trajectory through a physical environment. To this end, a technique is described for image-space based motion planning. In an embodiment, a planned 3D trajectory is projected into an image-space of an image captured by the autonomous vehicle. The planned 3D trajectory is then optimized according to a cost function derived from information (e.g., depth estimates) in the captured image. The cost function associates higher cost values with identified regions of the captured image that are associated with areas of the physical environment into which travel is risky or otherwise undesirable. The autonomous vehicle is thereby encouraged to avoid these areas while satisfying other motion planning objectives.

Abstract: A method includes: establishing wireless connection between an unmanned aerial vehicle (UAV) and a user interface; generating, via the user interface, a flight path for the unmanned aerial vehicle; generating, via the user interface, a flight schedule for the unmanned aerial vehicle, the flight schedule being associated with the flight path and include one or more designated times; and initiating, via the user interface, autonomous operation of the unmanned aerial vehicle for the unmanned aerial vehicle to autonomously fly the flight path at the one or more designated times

Abstract: Technology for generating and displaying a graphical user interface for operating an unmanned aerial vehicle (UAV) is disclosed herein that generates and updates a representation of a spline flight path. In various implementations, a graphical user interface detects user interactions with a remote control device directing the flight control subsystem of the UAV to record keyframes and to compute a spline based on the keyframes during flight. The graphical user interface displays a real-time perspective of the UAV with a representation of the spline and the keyframes overlaying the view. The graphical user interface continually updates the representation as the UAV flies and when the spline is updated as the keyframes are updated.

Abstract: An autonomous vehicle that is equipped with image capture devices can use information gathered from the image capture devices to plan a future three-dimensional (3D) trajectory through a physical environment. To this end, a technique is described for image-space based motion planning. In an embodiment, a planned 3D trajectory is projected into an image-space of an image captured by the autonomous vehicle. The planned 3D trajectory is then optimized according to a cost function derived from information (e.g., depth estimates) in the captured image. The cost function associates higher cost values with identified regions of the captured image that are associated with areas of the physical environment into which travel is risky or otherwise undesirable. The autonomous vehicle is thereby encouraged to avoid these areas while satisfying other motion planning objectives.

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.

Abstract: Techniques are described for controlling an autonomous vehicle such as an unmanned aerial vehicle (UAV) using objective-based inputs. In an embodiment, the underlying functionality of an autonomous navigation system is exposed via an application programming interface (API) allowing the UAV to be controlled through specifying a behavioral objective, for example, using a call to the API to set parameters for the behavioral objective. The autonomous navigation system can then incorporate perception inputs such as sensor data from sensors mounted to the UAV and the set parameters using a multi-objective motion planning process to generate a proposed trajectory that most closely satisfies the behavioral objective in view of certain constraints. In some embodiments, developers can utilize the API to build customized applications for the UAV. Such applications, also referred to as “skills,” can be developed, shared, and executed to control behavior of an autonomous UAV and aid in overall system improvement.

Abstract: An autonomous vehicle that is equipped with image capture devices can use information gathered from the image capture devices to plan a future three-dimensional (3D) trajectory through a physical environment. To this end, a technique is described for image-space based motion planning. In an embodiment, a planned 3D trajectory is projected into an image-space of an image captured by the autonomous vehicle. The planned 3D trajectory is then optimized according to a cost function derived from information (e.g., depth estimates) in the captured image. The cost function associates higher cost values with identified regions of the captured image that are associated with areas of the physical environment into which travel is risky or otherwise undesirable. The autonomous vehicle is thereby encouraged to avoid these areas while satisfying other motion planning objectives.

Abstract: A technique is described for developing and using applications and skills with an autonomous vehicle. In an example embodiment, a development platform is provided that enables access to a developer console for developing software modules for use with an autonomous vehicle. Using the developer console, a developer user can specify instructions for causing an autonomous vehicle to perform one or more operations. For example, to control the behavior of an autonomous vehicle, the instructions can cause an executing computer system at the autonomous vehicle to generate calls to an application programming interface (API) associated with an autonomous navigation system of autonomous vehicle. Such calls to the API can be configured to adjust a parameter of a behavioral objective associated with a trajectory generation process performed by the autonomous navigation system that controls the behavior of the autonomous vehicle.

Abstract: A dock assembly includes a docking station and a stand or mount coupled to the docking station. The dock assembly may be configured for an unmanned aerial vehicle (UAV). The docking station may include a landing surface configured to interface with the UAV, an extended portion coupled to the landing surface and extending from the landing surface, and a fiducial located on the extended portion.

Abstract: A battery configured to power an unmanned aerial vehicle. The battery includes an enclosure configured to house a power module of the battery. The battery also includes one or more conducting contacts located on the enclosure configured to contact one or more pogo pins of a battery charger located on a docking station of the unmanned aerial vehicle.

Abstract: An autonomous vehicle that is equipped with image capture devices can use information gathered from the image capture devices to plan a future three-dimensional (3D) trajectory through a physical environment. To this end, a technique is described for image-space based motion planning. In an embodiment, a planned 3D trajectory is projected into an image-space of an image captured by the autonomous vehicle. The planned 3D trajectory is then optimized according to a cost function derived from information (e.g., depth estimates) in the captured image. The cost function associates higher cost values with identified regions of the captured image that are associated with areas of the physical environment into which travel is risky or otherwise undesirable. The autonomous vehicle is thereby encouraged to avoid these areas while satisfying other motion planning objectives.

Abstract: Techniques are described for controlling an autonomous vehicle such as an unmanned aerial vehicle (UAV) using objective-based inputs. In an embodiment, the underlying functionality of an autonomous navigation system is exposed via an application programming interface (API) allowing the UAV to be controlled through specifying a behavioral objective, for example, using a call to the API to set parameters for the behavioral objective. The autonomous navigation system can then incorporate perception inputs such as sensor data from sensors mounted to the UAV and the set parameters using a multi-objective motion planning process to generate a proposed trajectory that most closely satisfies the behavioral objective in view of certain constraints. In some embodiments, developers can utilize the API to build customized applications for the UAV. Such applications, also referred to as “skills,” can be developed, shared, and executed to control behavior of an autonomous UAV and aid in overall system improvement.

Abstract: A computer accesses an input element storage and an output element storage. The computer accesses a symbolic expression for output element storage as a function of the input element storage. The computer computes, using a symbolic computation engine of the computer, a symbolic expression for the tangent space Jacobian of the output element storage with respect to an input tangent space. The computer outputs the computed expression.

Abstract: An autonomous vehicle that is equipped with image capture devices can use information gathered from the image capture devices to plan a future three-dimensional (3D) trajectory through a physical environment. To this end, a technique is described for image-space based motion planning. In an embodiment, a planned 3D trajectory is projected into an image-space of an image captured by the autonomous vehicle. The planned 3D trajectory is then optimized according to a cost function derived from information (e.g., depth estimates) in the captured image. The cost function associates higher cost values with identified regions of the captured image that are associated with areas of the physical environment into which travel is risky or otherwise undesirable. The autonomous vehicle is thereby encouraged to avoid these areas while satisfying other motion planning objectives.