Skydio, Inc.

Google Inc.

Wing Aviation LLC

Adam Parker Bry

Abraham Galton Bachrach

Matthew Joseph Donahoe

Hayk Martirosyan

Jack Louis Zhu

Peter Benjamin Henry

Ryan David Kennedy

Patrick Allen Lowe

Benjamin Scott Thompson

Daniel Thomas Adams

Brian Richman

Harrison Zheng

Yevgeniy Kozlenko

Kristen Marie Holtz

Justin Michael Sadowski

Gareth Benoit Cross

Asher Mendel Robbins-Rothman

Zachary Albert West

Himel Mondal

Quentin Allen Wah Yen Delepine

Kevin Patrick Smith O'Leary

Josiah Timothy VanderMey

Teodor Tomic

Stephen Robert McClure

David A Kalinowski

Roshan Neel Jobanputra

Saumitro Dasgupta

Mark Edward Rubin

Samuel Shenghung Wang

James Anthony Ferrandini

Jeffrey Robert DeCew

Matthew Thomas Beaudouin-Lafon

Charles VanSchoonhoven Wood

Clara Kelley

Vladimir Nekrasov

Saumya Pravinbhai Shah

Austin Stone

Alex Kendall

Hema Koppula

Tom Moss

Bruno Andre Posokhow

Clement Godard

Christopher Brian Grasberger

Enyu Luo

Abraham Bacharach

Kevin Patrick Smith O′Leary

Stephen R Mcclure

Kevin Patrick Smith O'LEARY

Autonomous aerial vehicle technology and, more specifically, environmental illumination for autonomous unmanned aerial vehicles involves, in some embodiments, a UAV including upward-facing image capture devices, downward-facing image capture devices, one or more illumination sources, and a computer system. The computer system is configured to direct the one or more illumination sources to selectively emit light into a surrounding physical environment while the UAV is in flight, process images captured by any one or more of the plurality of upward-facing image capture devices or the plurality of downward-facing image capture devices to estimate a position and/or orientation of the aerial vehicle, generate a planned trajectory for the aerial vehicle through a physical environment based on the processing of the images, and control a propulsion system and/or flight surface of the aerial vehicle to cause the aerial vehicle to autonomously maneuver along the planned trajectory.

Environment illumination for autonomous aerial vehicles

In some examples, a computing device receives, from an unmanned aerial vehicle (UAV), a first image from a first camera on the UAV and a plurality of second images from a plurality of second cameras on the UAV. The plurality of second cameras may be positioned on the UAV for providing a plurality of different fields of view in a plurality of different directions around the UAV. Further, the first camera has a longer focal length than the second cameras. The computing device presents, on a display, a composite image including at least a portion of the first image within a merged image generated from the plurality of second images. The presented composite image enables a user to at least one of: zoom out from the at least one first image to the merged image, or zoom in from the merged image to the at least one first image.

Unmanned aerial vehicle with virtual un-zoomed imaging

Sports and fitness applications for an autonomous unmanned aerial vehicle (UAV) are described. In an example embodiment, a UAV can be configured to track a human subject using perception inputs from one or more onboard sensors. The perception inputs can be utilized to generate values for various performance metrics associated with the activity of the human subject. In some embodiments, the perception inputs can be utilized to autonomously maneuver the UAV to lead the human subject to satisfy a performance goal. The UAV can also be configured to autonomously capture images of a sporting event and/or make rule determinations while officiating a sporting event.

Fitness and sports applications for an autonomous unmanned aerial vehicle

Methods and systems are disclosed for an unmanned aerial vehicle (UAV) configured to autonomously navigate a physical environment while capturing images of the physical environment. In some embodiments, the motion of the UAV and a subject in the physical environment may be estimated based in part on images of the physical environment captured by the UAV. In response to estimating the motions, image capture by the UAV may be dynamically adjusted to satisfy a specified criterion related to a quality of the image capture.

Unmanned aerial image capture platform

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

Embodiments are described for a stabilization system configured, in some embodiments, for stabilizing image capture from an aerial vehicle (e.g., a UAV). According to some embodiments, the stabilization systems employs both active and passive stabilization means. A passive stabilization assembly includes a counter-balanced suspension system that includes an elongated arm that extends into and is coupled to the body of a vehicle. The counter-balanced suspension system passively stabilizes a mounted device such as an image capture device to counter motion of the UAV while in use. In some embodiment the counter-balanced suspension system passively stabilizes a mounted image capture assembly that includes active stabilization means (e.g., a motorized gimbal and/or electronic image stabilization). In some embodiments, the active and passive stabilization means operate together to effectively stabilize a mounted image capture device to counter a wide range of motion characteristics.

Counter-balancing vibrations from a vehicle for stabilizing image capture

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

An introduced autonomous aerial vehicle can include multiple cameras for capturing images of a surrounding physical environment that are utilized for motion planning by an autonomous navigation system. In some embodiments, the cameras can be integrated into one or more rotor assemblies that house powered rotors to free up space within the body of the aerial vehicle. In an example embodiment, an aerial vehicle includes multiple upward-facing cameras and multiple downward-facing cameras with overlapping fields of view to enable stereoscopic computer vision in a plurality of directions around the aerial vehicle. Similar camera arrangements can also be implemented in fixed-wing aerial vehicles.

Autonomous aerial vehicle hardware configuration

Systems and methods are disclosed for tracking objects in a physical environment using visual sensors onboard an autonomous unmanned aerial vehicle (UAV). In certain embodiments, images of the physical environment captured by the onboard visual sensors are processed to extract semantic information about detected objects. Processing of the captured images may involve applying machine learning techniques such as a deep convolutional neural network to extract semantic cues regarding objects detected in the images. The object tracking can be utilized, for example, to facilitate autonomous navigation by the UAV or to generate and display augmentative information regarding tracked objects to users.

Object tracking by an unmanned aerial vehicle using visual sensors

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

Growing community of inventors

Redwood City, CA, United States of America

Adam Parker Bry