Skydio, Inc.

Jack Louis Zhu

Abraham Galton Bachrach

Adam Parker Bry

Hayk Martirosyan

Peter Benjamin Henry

Matthew Joseph Donahoe

Ryan David Kennedy

Harrison Zheng

Kristen Marie Holtz

Brian Richman

Teodor Tomic

Gareth Benoit Cross

Himel Mondal

Quentin Allen Wah Yen Delepine

Yevgeniy Kozlenko

Josiah Timothy VanderMey

Roshan Neel Jobanputra

Mark Edward Rubin

Samuel Shenghung Wang

Jeffrey Robert DeCew

Dominic William Pattison

James Anthony Ferrandini

Matthew Thomas Beaudouin-Lafon

Saumya Pravinbhai Shah

Bradley Matthew Solliday

Rowland Wilde O'Flaherty

Nathan Leo Bucki

Yixiao Sun

Aaron Christopher Miller

Charles VanSchoonhoven Wood

Clara Kelley

Patrick Allen Lowe

Shreetej Varakantam Reddy

Maximilian Joseph Halper Krogius

Margaret Bai Wang

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

Described herein are systems for automated docking of an unmanned aerial vehicle. For example, some systems include an unmanned aerial vehicle including a propulsion mechanism, a battery, and a processing apparatus; and a dock including a landing surface with a funnel geometry shaped to fit a bottom surface of the unmanned aerial vehicle at a base of the funnel, wherein tapered sides of the funnel form corners at the base of the funnel, and a battery charger configured to charge the battery of the unmanned aerial vehicle while the unmanned aerial vehicle is on the landing surface, wherein conducting contacts of the battery charger are on the landing surface, positioned at the bottom of the funnel.

Automated docking of unmanned aerial vehicle

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

Described herein are systems for automated docking of an unmanned aerial vehicle. For example, some systems include a landing surface configured to hold an unmanned aerial vehicle; a box configured to enclose the landing surface in a first arrangement of the dock and expose the landing surface in a second arrangement of the dock; and a retractable arm, wherein the landing surface is positioned at an end of the retractable arm and the retractable arm is configured to extend to move the landing surface outside of the box and contract to pull the landing surface inside of the box.

Method of landing an unmanned aerial vehicle on a freestanding dock including a reconfigurable landing surface

Described herein are systems for roof scan using an unmanned aerial vehicle. For example, some methods include capturing, using an unmanned aerial vehicle, an overview image of a roof of a building from above the roof; presenting a suggested bounding polygon overlaid on the overview image to a user; determining a bounding polygon based on the suggested bounding polygon and user edits; based on the bounding polygon, determining a flight path including a sequence of poses of the unmanned aerial vehicle with respective fields of view at a fixed height that collectively cover the bounding polygon; fly the unmanned aerial vehicle to a sequence of scan poses with horizontal positions matching respective poses of the flight path and vertical positions determined to maintain a consistent distance above the roof; and scanning the roof from the sequence of scan poses to generate a three-dimensional map of the roof.

Roof scan using unmanned aerial vehicle

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

Described herein are systems and methods for structure scan using an unmanned aerial vehicle. For example, some methods include accessing a three-dimensional map of a structure; generating facets based on the three-dimensional map, wherein the facets are respectively a polygon on a plane in three-dimensional space that is fit to a subset of the points in the three-dimensional map; generating a scan plan based on the facets, wherein the scan plan includes a sequence of poses for an unmanned aerial vehicle to assume to enable capture, using image sensors of the unmanned aerial vehicle, of images of the structure; causing the unmanned aerial vehicle to fly to assume a pose corresponding to one of the sequence of poses of the scan plan; and capturing one or more images of the structure from the pose.

Structure scan using unmanned aerial vehicle

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.

Image space motion planning of an autonomous vehicle

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

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

In some examples, one or more processors of an unmanned aerial vehicle (UAV), control a propulsion mechanism of the UAV to cause the UAV to navigate to a plurality of positions in relation to a scan target. Using one or more image sensors of the UAV, a first image of the scan target is captured from a first position of the plurality of positions, and a second image of the scan target is captured from a second position of the plurality of positions. A disparity is determined between the first image captured at the first position and the second image captured at the second position. A three-dimensional model corresponding to the scan target is determined based in part on the disparity determined between the first image and the second image.

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Redwood City, CA, United States of America

Jack Louis Zhu