Amazon Technologies, Inc.

Joshua John Watson

Barry James O'Brien

Scott Michael Wilcox

Sarah Graber

Chengwu Luke Cui

Scott Patrick Boyd

Scott Raymond Harris

Benjamin Novak

Ricky Dean Welsh

Louis LeRoi LeGrand, Iii

Taylor David Grenier

Paul A Viola

Jason Leonard Peacock

Ishay Kamon

Martin Koestinger

Gerald Schweighofer

Mario Sormann

Scott Battocchi

Sameet Ramakrishnan

Christopher Raymond Grajewski

Ravikant Cherukuri

Matthew Tyler Wilkinson

Brad Nathaniel Arnold

Maya Shirish Sathaye

Samuel Elbert McGowan

Michael Van Gorkom

Daniel Borrelli

Benjamin Israel Caro

A wearable device includes various sensors including a microphone, heart rate monitor, pressure sensor, and so forth. The user can set the device to acquire audio data of their speech. For example, if the pressure sensor indicates the device is being worn and the user's heart rate exceeds a threshold value, audio data may be acquired. Onboard the device the audio data is processed in various ways such as detecting whether the audio data includes speech, applying a beamforming algorithm, compressing and encrypting the audio, and so forth. The audio data may be processed onboard the device, sent to another device, or a combination thereof for processing to determine sentiment data indicative of perceived emotional content of the speech. A user interface provides the user with information based on the sensor data, sentiment data, and so forth.

Multi-sensor wearable device with audio processing

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

A configurable unmanned aerial vehicle (UAV) may include swappable components that may be selectable to configure a customized UAV just prior to deployment of the UAV that is configured to deliver a package to a destination. The UAV may include a plurality of ports that may accept swappable components. The ports may be coupled to a logic board to enable control of the swappable components. The ports and swappable components may enable quick replacement of a malfunctioning components, such as an image sensor, which may avoid subjecting a UAV to significant downtime for service. The malfunctioning component may then be serviced after the UAV is readied for a subsequent flight or deployed on a subsequent flight.

Unmanned aerial vehicle with ports configured to receive swappable components

A mobile calibration room may be used for calibrating one or more sensors used on unmanned aerial vehicles (UAVs). A system can include folding or collapsible walls to enable the system to be moved between a stowed position and a deployed position. In the deployed position, the system can comprise a calibration room including one or more 2D or 3D targets used to calibrate one or more sensors (e.g., cameras) on a UAV. The system can include a turntable to rotate the UAV about a first axis during calibration. The system can also include a cradle to rotate the UAV around, or translate the UAV along, a second axis. The turntable can include a frame to rotate the UAV around a third axis during calibration. The mobile calibration room can be coupled to a vehicle to enable the mobile calibration room to be moved between locations.

Self-contained mobile sensor calibration structure

Described is an aerial vehicle, such as an unmanned aerial vehicle ('UAV'), that includes a plurality of sensors, such as stereo cameras, mounted along a perimeter frame of the aerial vehicle and arranged to generate a scene that surrounds the aerial vehicle. The sensors may be mounted in or on winglets of the perimeter frame. Each of the plurality of sensors has a field of view and the plurality of optical sensors are arranged and/or oriented such that their fields of view overlap with one another throughout a continuous space that surrounds the perimeter frame. The fields of view may also include a portion of the perimeter frame or space that is adjacent to the perimeter frame.

Aerial vehicle sensor positioning

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

An aerial vehicle is configured to process an image captured by an imaging device, and to identify a portion of the image that is likely to appear in images subsequently captured by the imaging device based on the motion of the aerial vehicle. A control unit aboard the aerial vehicle generates instructions for controlling such motion and provides the instructions to the imaging device. Based on such instructions, the imaging device processes the image to identify a portion of the image that will appear within a field of view of the imaging device following the motion, and selects a shutter speed, an aperture, a level of gain, or another attribute of the imaging device based on the portion of the image, in order to optimize the quality of an image subsequently captured by the imaging device.

Configuring imaging devices based on control signals

Systems, methods, and computer-readable media are disclosed for systems and methods for initiating automated actions using ring-shaped electronic devices. Example methods may include determining, by a ring-shaped electronic device comprising a button, a first user interaction with the button, determining that a first action associated with the first button sequence identifier is to initiate a synchronous communication, determining contact information associated with the first action, and initiating the synchronous communication using the contact information.

Initiating automated actions using ring-shaped electronic devices

Systems, methods, and computer-readable media are disclosed for systems and methods for detecting hand gestures using ring-shaped electronic devices. Example methods may include determining, by a ring-shaped electronic device comprising a motion sensor, first motion sensor data using the motion sensor, determining a first hand motion associated with the first motion sensor data, determining a first action associated with the first hand motion, and causing the first action to be implemented at a user device that is in wireless communication with the ring-shaped electronic device.

Detecting hand gestures using ring-shaped electronic devices

A system for detecting transparent elements in a vehicle environment are described. In some examples, this may include accessing an image of a scene captured by an image capture device attached to a vehicle. A reflected image present in the image may be detected. The reflected image may include a portion of the vehicle. It may be determined that the scene includes a transparent element based at least in part on detecting the reflected image present in the image.

Transparent element detection for vehicles

Temperature management systems for aerial vehicles may include heat pipes that are thermally connected to components that generate heat. The heat pipes may be routed through or adjacent to a propeller or propulsion airflow, or within or across a vehicle airflow, to dissipate heat from the components. A heat pipe may be selected from a plurality of heat pipes based on measured temperatures of components that generate heat, operational characteristics of the aerial vehicle and/or one or more propellers, and/or measured temperatures of other components that may be heated. Additional temperature management systems may include cool air ducts and cool air pipes that may be routed from a propeller or propulsion airflow, or a vehicle airflow, to components that generate heat or other components that may be cooled.

Temperature management systems for aerial vehicles

A system may include sensor modules configured to generate sensor signals representative of an environment surrounding a vehicle, and a sensor configured to be coupled to the frame of the vehicle at a location spaced from a first sensor module and configured to generate sensor signals representative of movement of the first sensor module relative to a portion of the frame. The system may also include a sensor processor configured to receive the sensor signals representative of movement of the first sensor module and estimate relative motion of the first sensor module relative to the portion of the frame of the vehicle. The sensor processor may also be configured to calculate, based at least in part on the relative motion estimation, a position, orientation, and/or velocity of the vehicle, and a position of objects in the surrounding environment and/or movement of the objects in the surrounding environment.

Determining and using relative motion of sensor modules

Techniques for verifying a location and identification of a landing marker to aid an unmanned aerial vehicle (UAV) to deliver a payload to a location may be provided. For example, upon receiving an indication that a UAV has arrived to a delivery location, a server computer may process one or more images of an area that are provided by the UAV and/or a user interacting with a user device. A landing marker may be identified in the image and a representation of the landing marker along with instructions to guide the UAV to deliver the payload to the landing marker may be transmitted to the UAV and implemented by the UAV.

Drone marker and landing zone verification

Camera calibration may be performed in a mobile environment. One or more cameras can be mounted on a mobile vehicle, such as an unmanned aerial vehicle (UAV) or an automobile. Because of the mobility of the vehicle the one or more cameras may be subjected to inaccuracy in imagery caused by various factors, such as environmental factors (e.g., airflow, wind, etc.), impact by other objects (e.g., debris, vehicles, etc.), vehicle vibrations, and the like. To reduce the inaccuracy in imagery, the mobile vehicle can include a mobile camera calibration system configured to calibrate the one or more cameras while the mobile vehicle is traveling along a path. The mobile camera calibration system can cause the one or more cameras to capture an image of an imaging target while moving, and calibrate the one or more cameras based on a comparison between the image and imaging target data.

Camera calibration in a mobile environment

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.

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Seattle, WA, United States of America

Joshua John Watson