Skydio, Inc.

Kristen Marie Holtz

Hayk Martirosyan

Jack Louis Zhu

Abraham Galton Bachrach

Adam Parker Bry

Matthew Joseph Donahoe

Peter Benjamin Henry

Gareth Benoit Cross

Ryan David Kennedy

Teodor Tomic

Samuel Shenghung Wang

Dominic William Pattison

James Anthony Ferrandini

Harrison Zheng

Josiah Timothy VanderMey

Roshan Neel Jobanputra

Mark Edward Rubin

Jeffrey Robert DeCew

Anurag Makineni

Matthew Thomas Beaudouin-Lafon

Saumya Pravinbhai Shah

Vladimir Nekrasov

Bradley Matthew Solliday

Nathan Leo Bucki

Rowland Wilde O'Flaherty

Yixiao Sun

Aaron Christopher Miller

Charles VanSchoonhoven Wood

Clara Kelley

Patrick Allen Lowe

Shreetej Varakantam Reddy

Maximilian Joseph Halper Krogius

Anh Tung Dang

Jeremy Christopher Crowley

Margaret Bai Wang

A calibration of an unmanned aerial vehicle is performed without the use of a magnetometer. The unmanned aerial vehicle generates a first acceleration vector in a navigation frame of reference and a second acceleration vector in a GPS frame of reference. The unmanned aerial vehicle estimates a heading of the unmanned aerial vehicle based on the first acceleration vector and the second acceleration vector. The unmanned aerial vehicle performs a calibration based on the estimated heading of the unmanned aerial vehicle.

Motion-based calibration of an aerial device

A computer of an unmanned aerial vehicle (UAV) accesses, from a memory unit, a problem definition comprising cost functions associated with travel of the UAV. The computer causes movement of the UAV based on the cost functions. The computer adjusts one or more of the cost functions during a flight of the UAV. The computer causes further movement of the UAV based on the adjusted one or more of the cost functions.

Unmanned aerial vehicle operated based on cost functions

A computer stores dense maps generated by one or more aerial vehicles. The computer generates a global graph based on the dense maps and a sparse map. The computer stores a representation of one or more paths traversed by the one or more aerial vehicles within the global graph. The computer determines a path from an origin location to a destination location based on the global graph. The determined path enables an aerial vehicle to avoid objects.

Aerial vehicle path determination

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.

Computer-implemented symbolic differentiation using first-order retraction

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.

Applications and skills for an autonomous unmanned aerial vehicle

In some examples, an aerial vehicle may determine, based on sensor information received from at least one onboard sensor, that an amount of light fails to satisfy a light threshold. Based at least in part on determining that the amount of light fails to satisfy the light threshold, the aerial vehicle is caused to takeoff at a specified trajectory and a specified acceleration for enabling navigation via an inertial measurement unit (IMU) and a satellite positioning system. Further, the aerial vehicle is directed to navigate an environment based at least on determining a relative heading of the aerial vehicle from information received from the IMU and information received from the satellite positioning system.

Low-light and no-light aerial navigation

A computer accesses a first symbolic expression for an output matrix as a function of an input matrix at a computing device comprising processing circuitry and memory. The computer computes a first Jacobian of the input matrix with respect to an input tangent space. The computer computes a second Jacobian of the output matrix with respect to the input matrix. The computer computes a third Jacobian of an output tangent space with respect to the input matrix. The computer applies symbolic matrix multiplication to the first Jacobian, the second Jacobian, and the third Jacobian to obtain a second symbolic expression for the output tangent space with respect to the input tangent space. The computer provides a representation of the second symbolic expression, the second symbolic expression representing a computed tangent-space Jacobian.

Computer-implemented computation of tangent-space jacobian

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.

Aerial vehicle touchdown detection

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.

Graphical user interface for enhanced unmanned aerial vehicle flight along computed splines

Autonomous aerial navigation in low-light and no-light conditions includes using night mode obstacle avoidance intelligence and mechanisms for vision-based unmanned aerial vehicle (UAV) navigation to enable autonomous flight operations of a UAV in low-light and no-light environments using infrared data.

Autonomous aerial navigation in low-light and no-light conditions

Autonomous aerial navigation in low-light and no-light conditions includes using night mode obstacle avoidance intelligence, training, and mechanisms for vision-based unmanned aerial vehicle (UAV) navigation to enable autonomous flight operations of a UAV in low-light and no-light environments using infrared data.

An unmanned aerial vehicle (UAV) comprises a flight control system and an electromechanical system directed by the flight control system. The flight control system is configured to track a position of a beacon that is in motion and monitor a difference between an actual position of the unmanned aerial vehicle and a desired position of the unmanned aerial vehicle relative to the position of the beacon. The flight control system configures one or more flight objectives based on one or more factors comprising whether the difference between the actual position and the desired position exceeds a threshold, wherein the flight objectives comprise a velocity objective and a position objective. The flight control system also commands the electromechanical system based at least on the one or more flight objectives.

Enhanced unmanned aerial vehicle flight with situational awareness for moving vessels

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.

Enhanced unmanned aerial vehicle flight along computed splines

In some examples, an unmanned aerial vehicle (UAV) may determine a first acceleration of the UAV based at least on information from an onboard accelerometer received at least one of prior to or during takeoff. The UAV may determine a second acceleration of the UAV based at least on location information received via a satellite positioning system receiver at least one of prior to or during takeoff. The UAV may further determine a relative heading of the UAV based at least in part on the first acceleration and the second acceleration, and may be directed to navigate an environment based at least on the determined relative heading.

Growing community of inventors

Redwood City, CA, United States of America

Kristen Marie Holtz