Amazon Technologies, Inc.

Other

Athorus, Pllc

Amir Navot

Gur Kimchi

Brian C Beckman

Daniel Buchmueller

Scott A Green

Brandon William Porter

Fabian Hensel

Severan Sylvain Jean-Michel Rault

Avi Bar-Zeev

Frederik Schaffalitzky

Scott M Isaacs

Jeffrey P Bezos

Steven Gregory Dunn

Scott Raymond Harris

Robert John Anderson

Elad Ben-Israel

Severan Sylvain Rault

Atishkumar Kalyan

Martin Koestinger

Avi Bar-zeev

David Ferstl

This disclosure describes an unmanned aerial vehicle ('UAV') configured to autonomously deliver items of inventory to various destinations. The UAV may receive inventory information and a destination location and autonomously retrieve the inventory from a location within a materials handling facility, compute a route from the materials handling facility to a destination and travel to the destination to deliver the inventory.

Aerial vehicle delivery location

The disclosure describes an automated aerial vehicle (AAV) and system for automatically detecting a contact or an imminent contact between a propeller of the AAV and an object (e.g., human, pet, or other animal). When a contact or an imminent contact is detected, a safety profile may be executed to reduce or avoid any potential harm to the object and/or the AAV. For example, if a contact with a propeller of the AAV by an object is detected, the rotation of the propeller may be stopped to avoid harming the object. Likewise, an object detection component may be used to detect an object that is nearing a propeller, stop the rotation of the propeller, and/or navigate the AAV away from the detected object.

Propeller safety for automated aerial vehicles

This disclosure describes an unmanned aerial vehicle that may be configured during flight to optimize for agility or efficiency.

Adjustable unmanned aerial vehicles with adjustable body portions

Described are systems and methods to utilize an objective function of an aerial vehicle in constructing and/or updating an occupancy map. The described systems and methods can determine whether to include, add, and/or remove an object from an occupancy map based on one or more confidence score(s) that can be determined for the presence (or absence) of an object at a given location. The confidence score for an object at a given location can be determined, for example, based on various sources of information, which can each be provided different weights, parameters, thresholds, etc. based on the objective function of the aerial vehicle.

Systems and methods for creating an occupancy map based on an objective function

Disclosed are methods and systems for delivery of items using an unmanned aerial vehicle ('UAV'). A user may be provided with a delivery location identifier ('DLI') that is to be placed at a delivery location within a delivery destination to identify where a UAV is to position an item as part of a delivery to the delivery destination. For example, the delivery destination may be a user's home. Within the deliver destination of the user's home, the user may select a delivery location, such as a spot in the back yard wherein the UAV is to position the ordered item as part of the delivery. To aid the UAV in navigating to the delivery location, the user places the DLI at the delivery location. The UAV detects the DLI and positions the item at or near the DLI as part of the item delivery.

Delivery location identifiers

A system and method for operating an automated aerial vehicle are provided wherein influences of ground effects (e.g., which may increase the effective thrusts of propellers by interfering with the respective airflows) are utilized for sensing the ground or other surfaces. In various implementations, operating parameters of the automated aerial vehicle are monitored to determine when ground effects are influencing the parameters associated with the propellers, which correspondingly indicate proximities to a surface (e.g., the ground). Such ground effect sensing techniques may be utilized as a backup to other sensors (e.g., which may be determined to not be functioning properly and/or may be otherwise inhibited due factors such as to rain, snow, fog, reflections, bright sunlight, etc.).

Ground effect based surface sensing utilized with other sensing technologies in automated aerial vehicles

This disclosure describes an automated mobile vehicle that includes one or more distance determining elements configured to detect the presence of objects and to cause the automated mobile vehicle to alter its path to avoid the object. For example, a distance determining element may be incorporated into one or more of the motors of the automated mobile vehicle and configured to determine a distance to an object. Based on the determined distance, a path of the automated mobile vehicle may be altered.

Sense and avoid for automated mobile vehicles

Described is an airborne monitoring station ('AMS') for use in monitoring a coverage area and/or unmanned aerial vehicles ('UAVs') positioned within a coverage area of the AMS. For example, the AMS may be an airship that remains at a high altitude (e.g., 45,000 feet) that monitors a coverage area that is within a line-of-sight of the AMS. As UAVs enter, navigate within and exit the coverage area, the AMS may wirelessly communicate with the UAVs, facilitate communication between the UAVs and one or more remote computing resources, and/or monitor a position of the UAVs.

Airborne unmanned aerial vehicle monitoring station with adjustable image capture devices

An automated aerial vehicle (AAV) and system for automatically detecting a contact or an imminent contact between a propeller of the AAV and an object (e.g., human, pet, or other animal) are described. A safety profile for the AAV may be selected based on various factors including a position or configuration of the AAV. When a contact or an imminent contact is detected, the selected safety profile may be executed to reduce or avoid any potential harm to the object and/or the AAV. For example, if a contact with a propeller of the AAV by an object is detected, the rotation of the propeller may be stopped to avoid harming the object. Likewise, an object detection component may be used to detect an object that is nearing a propeller, stop the rotation of the propeller, and/or navigate the AAV away from the detected object.

This disclosure describes an automated aerial vehicle that includes one or more object detection elements configured to detect the presence of objects and an avoidance determining element configured to cause the automated aerial vehicle to automatically determine and execute an avoidance maneuver to avoid the objects. For example, an object may be detected and an avoidance maneuver determined based on a position of the object and an object vector representative of a direction and a magnitude of velocity of the object.

Object avoidance methods for automated aerial vehicles

Systems and methods for providing a multi-direction wind tunnel, or 'windball,' are disclosed. The system can have a series of fans configured to provide air flow in a plurality of directions to enable accurate testing of aircraft, unmanned aerial vehicles (UAVs), and other vehicles capable of multi-dimensional flight. The system can comprise a spherical or polyhedral test chamber with a plurality of fans. The fans can be arranged in pairs, such that a first fan comprises an intake fan and a second fan comprises an exhaust fan. The direction of the air flow can be controlled by activating one or more pairs of fans, each pair of fan creating a portion of the air flow in a particular direction. The direction of the air flow can also be controlled by rotating one or more pairs of fans with respect to the test chamber on a gimbal device, or similar.

Multi-faceted test chamber

Imaging data or other data captured using a camera may be classified based on data captured using another sensor that is calibrated with the camera and operates in a different modality. Where a digital camera configured to capture visual images is calibrated with another sensor such as a thermal camera, a radiographic camera or an ultraviolet camera, and such sensors capture data simultaneously from a scene, the respectively captured data may be processed to detect one or more objects therein. A probability that data depicts one or more objects of interest may be enhanced based on data captured from calibrated sensors operating in different modalities. Where an object of interest is detected to a sufficient degree of confidence, annotated data from which the object was detected may be used to train one or more classifiers to recognize the object, or similar objects, or for any other purpose.

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Seattle, WA, United States of America

Amir Navot