Amazon Technologies, Inc.

James Christopher Curlander

Jules Cook Graybill

Barry James O'Brien

Marshall Friend Tappen

Robert Alexander Colburn

Gur Kimchi

Pragyana K Mishra

Udit Madan

Jason Leonard Peacock

Ryan Scott Russell

Jon Stuart Battles

Larry Felser

Darren Ernest Canavor

Wesley Scott Lauka

Michael Ellsworth Bundy

Allan Scott Bathurst

David Daniel Glick

Michael Donoser

Jonathan Blair Norwood

Pradeep Krishna Yarlagadda

Cédric Philippe Charles Jean Ghislain Archambeau

Ralf Herbrich

Andrew Stubbs

Scott Raymond Harris

Danny Guan

Michael Anthony Molello

Hilliard Bruce Siegel

Brandon William Porter

Peter R Wurman

Ricky Dean Welsh

Paul A Viola

Gurunandan Krishnan Gorumkonda

Leon Robert Warman

Peter K Mansfield

Jon T Hanlon

Ned Lecky

Erik Resch Parker

Lauren Marie Kisser

Asaf Gilboa-Amir

Lilian De Greef

Robert Arthur Koch

Venketaram Ramachandran

Jill Kathleen Sestini

Tally Etel Portnoi

This disclosure describes a device and system for verifying the content of items in a bin within a materials handling facility. In some implementations, a bin content verification apparatus may pass by one or more bins and capture images of those bins. The images may be processed to determine whether the content included in the bins has changed since the last time images of the bins were captured. A determination may also be made as to whether a change to the bin content was expected and, if so, if the determined change corresponds with the expected change.

Bin content verification

Described are systems and methods that utilize nodes distributed at different geographic locations to detect and track the approximate position, trajectory, and/or predicted path of aerial vehicles operating below a defined altitude (e.g., 500 feet). As nodes detect an aerial vehicle, the node determines a bearing toward the aerial vehicle and provides the bearing to an air-traffic system. The air-traffic system processes bearings received from each node and determines one or more of an approximate position, trajectory, and/or predicted path of the detected aerial vehicle. The approximate position, trajectory, and/or predicted path may be provided to one or more subscribing clients and/or used to alter paths of one or more aerial vehicles.

Air-traffic system

An unmanned aerial vehicle (UAV) expandable landing marker system may include a an expandable volume. The landing marker may be expanded prior to arrival of a UAV delivering an item to be received by the landing marker. The landing marker may be expanded by regulating an amount of fluid in the volume. An anchor may be coupled to the landing marker to restrain movement of the expanded landing marker. An optional retraction mechanism may retract the landing marker. The landing marker can be retracted with the deposited item, moving the item to a location for later retrieval.

Unmanned aerial vehicle expandable landing marker system

Methods and systems for collecting camera calibration data using wearable devices are described. An augmented reality interface may be provided at a wearable device. Three-dimensional virtual information may be presented at the augmented reality interface. The three-dimensional information may identify a field of view of a remote camera and may be associated with collection of calibration data for the remote camera. Calibration data collected by the remote camera viewing a calibration target in the field of view may be received. The camera may be calibrated based at least in part on the calibration data.

Collection of camera calibration data using augmented reality

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 for operating autonomous vehicles are described. An autonomous vehicle may detect strategy modes and/or actions of other vehicles in a local environment. The autonomous vehicle may then select a strategy mode for its operations based on the detected strategy modes and/or actions of other vehicles, and based on an operational goal for the autonomous vehicle. The strategy modes may include an uncoupled strategy mode, a permissive strategy mode, an assistive strategy mode, and a preventative strategy mode. The autonomous vehicle may further select an action for its operations based on the selected strategy mode.

Strategy modes for autonomous vehicle operations

Systems and methods for coordinating operations of a plurality of autonomous vehicles are described. An autonomous vehicle management system may receive information from the plurality of autonomous vehicles related to strategy modes, actions, and the environments. The strategy modes may include an uncoupled strategy mode, a permissive strategy mode, an assistive strategy mode, and a preventative strategy mode. The autonomous vehicle management system may then process the received information based on an operational goal for the system as a whole. The autonomous vehicle management system may then instruct modifications to operations of one or more of the plurality of autonomous vehicles to achieve the operational goal for the system.

Coordinated optimization of autonomous vehicle operations

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

Systems and methods for broadcasting strategy modes of autonomous vehicles are described. An autonomous vehicle may include one or more notification devices that broadcast a selected strategy mode for the autonomous vehicle. The selected strategy mode may include an uncoupled strategy mode, a permissive strategy mode, an assistive strategy mode, and a preventative strategy mode. The notification devices may include visual, audio, or signal notification devices. In addition, the autonomous vehicle may broadcast other information associated with the vehicle, such as a position, a velocity, a priority, or an identity of the vehicle.

Broadcast strategy modes for autonomous vehicle operations

Systems and methods for determining strategy modes for autonomous vehicles are described. An autonomous vehicle may detect aspects of other vehicles and aspects of the environment using one or more sensors. The autonomous vehicle may then determine strategy modes of the other vehicles, and select a strategy mode for its own operation based on the determined strategy modes and an operational goal for the autonomous vehicle. The strategy modes may include an uncoupled strategy mode, a permissive strategy mode, an assistive strategy mode, and a preventative strategy mode. The autonomous vehicle may further determine elements in the environment and topological constraints associated with the environment, and select the strategy mode for its own operation based thereon.

Determination of strategy modes for autonomous vehicle operations

Visual task feedback for workstations in a materials handling facility may be implemented. Image data of a workstation surface may be obtained from image sensors. The image data may be evaluated with regard to the performance of an item-handling task at the workstation. The evaluation of the image data may identify items located on the workstation surface, determine a current state of the item-handling task, or recognize an agent gesture at the workstation. Based, at least in part on the evaluation, one or more visual task cues may be selected to project onto the workstation surface. The projection of the selected visual task cues onto the workstation surface may then be directed.

Visual task feedback for workstations in materials handling facilities

The high-intensity specular reflection of light from a surface of an object may create distortions in images of the object, in locations determined based on a perspective of an optical sensor. Multiple images of the object taken from different perspectives may include multiple specular artifacts in different locations, and a composite image of the object that omits such artifacts may be generated based on the multiple images. In particular, pixels of each of the images corresponding to the locations of the specular artifacts and having optimal intensities may be identified. The composite image may be generated based on the pixels having optimal intensities, and by excluding the pixels corresponding to the specular artifacts.

Imaging system for addressing specular reflection

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Mercer Island, WA, United States of America

James Christopher Curlander