Amazon Technologies, Inc.

Athorus, Pllc

Scott Raymond Harris

Joshua John Watson

Barry James O'Brien

Paul A Viola

Jason Leonard Peacock

Scott Michael Wilcox

Benjamin Novak

Brian C Beckman

Daniel Buchmueller

Louis LeRoi LeGrand, Iii

James Christopher Curlander

Amir Navot

Frederik Schaffalitzky

Ishay Kamon

Stephen Thomas Safarik

Michael John Elzinga

Varsha Raghavan

Jon T Hanlon

David Moro-Ludena

Peter Joseph Williams

Tally Etel Portnoi

Paul Viola

Image data is used to determine wind speed and wind direction during takeoff and landing by an unmanned aerial vehicle (UAV). The flight data, including image data, may be received using sensors onboard the UAV and/or the flight data may be received from other sources, such as nearby anemometer, cameras, other UAVs, other vehicles, and/or local weather stations. Machine learning models may train using the flight data gathered by the UAVs to determine the wind velocity based on image data. The UAV may adjust flight control settings to generate side forces to overcome the predicted wind velocity.

Computer vision wind direction for unmanned aerial vehicle (UAV) applications

Described is an imaging component for use by an unmanned aerial vehicle ('UAV') for object detection. As described, the imaging component includes one or more cameras that are configured to obtain images of a scene using visible light that are converted into a depth map (e.g., stereo image) and one or more other cameras that are configured to form images, or thermograms, of the scene using infrared radiation ('IR'). The depth information and thermal information are combined to form a representation of the scene based on both depth and thermal information.

Combined information for object detection and avoidance

This disclosure describes a configuration of an aerial vehicle, such as an unmanned aerial vehicle ('UAV'), that includes a plurality of cameras that may be selectively combined to form a stereo pair for use in obtaining stereo images that provide depth information corresponding to objects represented in those images. Depending on the distance between an object and the aerial vehicle, different cameras may be selected for the stereo pair based on the baseline between those cameras and a distance between the object and the aerial vehicle. For example, cameras with a small baseline (close together) may be selected to generate stereo images and depth information for an object that is close to the aerial vehicle. In comparison, cameras with a large baseline may be selected to generate stereo images and depth information for an object that is farther away from the aerial vehicle.

Selectively paired imaging elements for stereo images

A delivery robot may provide an approach notification to enable people to understand and interpret actions by an unmanned aerial vehicle (UAV), such as an intention to land or deposit a package at a particular location. The delivery robot may include a display, lights, a speaker, and one or more sensors to enable the robot to provide information, barcodes, and text to the UAV and/or bystanders. The robot can provide final landing authority, and can 'wave-off' the UAV, if an obstacle or person exists in the landing zone. The delivery robot can receive packages and (1) store them for retrieval (2) deliver them to the delivery location and/or (3) deliver them to an automated locker system. The delivery robot can temporarily close lanes or streets to enable a package to be delivered by UAV. The system may include a shelter to secure, maintain, and charge the delivery robot.

Landing and delivery robot

Systems and methods to actively control vibrations affecting an aerial vehicle are described. Vibrations affecting a location of interest on an aerial vehicle may be measured, and phases of one or more propellers of the aerial vehicle may be determined. Based on the measured vibrations and determined phases of propellers, adjustments to the phases of the propellers may be determined to modify the vibrations affecting the location of interest. In this manner, vibrations at a location of interest on an aerial vehicle may be reduced, minimized, increased, induced, or otherwise modified as desired.

Active vibration control for aerial vehicles

This disclosure describes a configuration of a multirotor aircraft that will facilitate enhanced yaw control. The multirotor aircraft includes one or more adjustable members that will twist the frame of the multirotor aircraft, thereby adjusting the orientation of the motors and propellers and enhance the yaw control of the multirotor aircraft. In some implementations, the adjustable member(s) are passive and twist in response to differential thrusts generated by the propellers. In other implementations, the adjustable members are active and twist in response to a yaw command from the multirotor aircraft control system.

Operating multirotor aircraft with enhanced yaw control

An electronic marker may provide an approach notification to enable people to understand and interpret actions by a UAV, such as an intention to land or deposit a package at a particular location. The marker may communicate a specific intention of the UAV and/or communicate a request to a person. The marker may monitor the person or data signals for a response from the person, such as movement of the person that indicates a response. The marker may be equipped with hardware and/or software configured to provide notifications and/or exchange information with a person or the UAV at or near a destination. The marker may include a display, lights, a speaker, and one or more sensors to enable the UAV to provide information, barcodes, and text. The marker can provide final landing authority and can 'wave-off' the UAV if an obstacle or person exists in the landing zone.

Electronic landing marker

This disclosure describes deflecting moisture out of the field of view of an imaging device during operation of an aerial vehicle, such as a UAV. The imaging device and/or a deflector positioned in the field of view of the imaging device is positioned within a path of a propulsion motor air disturbance. The deflector protects the lens from environmental conditions, such as moisture and rain and is positioned such that the force of the propulsion motor air disturbance moves any moisture, rain, or other debris contacting the surface of the deflector across the deflector surface and out of the field of view of the imaging device. As a result, distortion of image data generated by the imaging device as a result of water or moisture on the lens or in the field of view of the lens of the imaging device is reduced, if not eliminated.

Moisture deflection apparatus for aerial vehicle cameras

Described are systems and methods for surveying a destination as an unmanned aerial vehicle ('UAV') descends toward the destination. To confirm that the destination is clear of objects and includes a safe landing or delivery location, such as a substantially planar surface, the UAV may capture and process images at different altitudes during the descent. Feature points of a first image captured at a first altitude may be paired with feature points of a second image captured at a second, different altitude. A homography may be computed to confirm that the paired feature points lie in the same plane and then the two images may be registered based on the paired feature points. The registered images may then be processed to determine depth information and determine if descent of the UAV is to continue or be aborted.

Aerial vehicle descent with delivery location identifiers

Described are systems, methods, and apparatus for detecting objects within a distance of an aerial vehicle, and developing a three-dimensional model or representation of those objects. Rather than attempting to use stereo imagery to determine distances and/or depth of objects, the described implementations utilize range-gating, or time-gating, and the known position of the aerial vehicle to develop a three-dimensional representation of objects. For example, when the aerial vehicle is at a first position it may use range-gating to detect an object at a defined distance from the vehicle. The aerial vehicle may then alter its position and use range-gating to detect an object that is the defined distance from the vehicle at the new position. This may be done at several different positions and the resulting information and aerial vehicle position information combined to form a three-dimensional representation of those objects.

Three-dimensional representations of objects detected by an unmanned aerial vehicle

An unmanned aerial vehicle (UAV) landing marker transmits a reply signal in response to receiving radar signals emitted by a UAV. The landing marker can include a passive transponder that emits the reply signal, with the reply signal being a harmonic of the fundamental frequency of the radar signal emitted by the UAV. The landing marker can also include a transmitter to transmit the reply signal. Additionally, the landing marker can include sensors to monitor the environment about the landing marker and this environmental information can be transmitted to the UAV as part of the reply signal.

Unmanned aerial vehicle (UAV) landing marker responsive to radar signals

An unmanned aerial vehicle (UAV) landing marker that absorbs incoming radar signals emitted by a UAV and/or disperses reflected radar signals. The absorption and/or dispersion of the radar signals creates a reduced radar return in comparison to the environment about the landing marker. The UAV can detect the area of reduced radar return and determine that it is a landing marker. Additionally, the UAV can determine a position of the landing marker relative to the UAV, based on the reduced radar return, to effect delivery of an item by the UAV. The landing marker can include materials, structures and/or features to absorb and/or disperse radar signals to cause the reduced radar return.

An aerial vehicle may include a first sensor, such as a digital camera, having a lens or other component that includes a second sensor mounted thereto. Information or data, such as digital images, captured using the second sensor may be used to determine or predict motion of the lens, which may include components of translational and/or rotational motion. Once the motion of the lens has been determined or predicted, such motion may be used to stabilize information or data, such as digital images, captured using the first sensor, according to optical or digital stabilization techniques. Where operations of the first sensor and the second sensor are synchronized, motion of the second sensor may be modeled based on information or data captured thereby, and imputed to the first sensor.

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Bainbridge Island, WA, United States of America

Scott Raymond Harris