Amazon Technologies, Inc.

Roger Robert Webster

David Allen Fotland

Adam Barry Fineberg

Robert Franklin Ebert

Andrew Jay Roths

Rohan Mutagi

Charles Louis Ward

Stanislaw Ignacy Pasko

Robert Vaughan Williams

Mark Aran Aiken

Rajasimman Madhivanan

Olusanya Temitope Soyannwo

Chang Young Kim

Philip Alexander Lee

Makarand Damle

Swetha Bijoy

Amy Solenberg

Anthony George Robson

Amir Frenkel

Swati S Rao

Anil Kumar Katta

Mohit Deshpande

Vibhav Hemant Salgaonkar

Eric Peter Raeber

Nikhil Sharma

Amin Hani Atrash

James Charles Zamiska

Madan Jayaprakasam

Arif Dikici

Rachel Liao

Movement of an autonomously motile device may be controlled by a user device. The user device may display image data captured by a camera of the autonomously motile device; a user may provide input, such as a touch gesture on a display screen, indicating a command for the autonomously motile device to move to a location indicated by the input. The autonomously motile device determines a coordinate of the input and a time of the touch input; the autonomously motile device then determines a direction and distance of a corresponding movement.

Autonomously motile device with remote control

A hub is configured to provide voice control without assistance from a remote system, which allows the hub to provide a user with the ability to control second devices in an environment by issuing voice commands, even when the hub is unable to communicate with the remote system over a wide area network (e.g., the Internet). The hub is also configured to execute rules without assistance from the remote system, which allows the hub to execute rules, even when the hub is unable to communicate with the remote system over a wide area network (e.g., the Internet).

Architecture for a hub configured to control a second device while a connection to a remote system is unavailable

An autonomous mobile device (AMD) uses sensors to explore a physical space and determine the locations of obstacles. Simultaneous localization and mapping (SLAM) techniques are used by the AMD to designate as keyframes some images and their associated descriptors of features in the space. Each keyframe indicates a location and orientation of the AMD relative to those features. Anchors are specified relative to keyframes. A marker is specified relative to one or more anchors. Because markers are associated with features in the physical space, they maintain their association with the physical space through various processes such as SLAM loop closures. Markers may specify locations in the physical space, such as navigation waypoints, navigation destinations such as a goal pose for exploring an unexplored area, as an observation target to facilitate exploration, and so forth. Markers may also be used to specify block listed locations to be avoided during exploration.

System for automated exploration by an autonomous mobile device using markers based on image features

An autonomous mobile device (AMD) moves through a physical space without human intervention. Data from sensors on the AMD are used to determine an occupancy map indicative of obstacles and their locations in the physical space. A navigation map is created from the occupancy map by inflating the apparent size of the obstacles indicated by the occupancy map. In one implementation the inflation process dynamically skips some portions of the occupancy map based on the presence of an obstacle and a designated inflation distance. The extent of the inflation is based on a size of the AMD. The navigation map is then used for path planning.

System for obstacle avoidance by autonomous mobile device

A robot comprises a plurality of sensors on a mobile chassis and may move about an environment in which people may be expected to follow particular cultural conventions. Movement of the robot may be constrained to honor these cultural conventions that are appropriate for the people in the environment. For example, when the robot is used within the United States, the robot tends to move along the right-hand side of a hallway. In comparison, when the robot is used within Japan the robot tends to move along the left-hand side of the hallway. The cultural conventions may be implemented as one-way gates present at specified locations in an occupancy map of the environment. The gates have no physical presence in the environment, but have the effect of placing a constraint on where the robot is permitted to move.

System for robot movement that is informed by cultural conventions

An autonomous mobile device (AMD) moves through a physical space without human intervention. Data from sensors on the AMD is used to determine an occupancy map indicative of obstacles and their locations in the physical space. Obstacles may be static, variable, or dynamic. For example, walls are static, doors which open and close are variable, and people moving through the environment are dynamic. Regions in the occupancy map that the AMD has visited but which are shown by the occupancy map as containing obstacles are identified as being variable regions. To determine possible paths through the physical space during autonomous movement, the variable regions are considered to be unoccupied by obstacles and traversable. Information about how often the variable region transitions between traversable and impassible states may also be used to determine the possible paths.

System for autonomous mobile device navigation in dynamic physical space

A robot has a component that is oriented relative to a chassis of the robot. For example, a display screen is mounted to the robot using a pan and tilt arrangement. During operation, trajectory data indicative of a trajectory is determined that specifies where the robot is intended to travel as it moves through an environment. This trajectory data is used to dynamically orient the component. For example, as the robot moves through the environment, the display screen would be oriented such that it appears to be 'facing' a point in space that is associated with a point on the trajectory.

Dynamic trajectory-based orientation of autonomous mobile device component

A point cloud or map of an environment is generated by determining sets of landmark points from multiple images, such as through use of a Simultaneous Localization and Mapping (SLAM) algorithm. Images acquired using a depth camera are used to determine depth points indicative of at least one object not represented by the landmark points. A combined map is generated to include both the landmark points and the depth points. The set of depth points is mapped to a corresponding set of landmark points based on proximity of the points, similarity of the camera poses, or times that the images were acquired. The relationship between the depth and landmark points may be determined. When the landmark points are moved, such as to account for error in the SLAM algorithm, the depth points may be moved to a modified location relative to the landmark points so that the relationship remains constant.

System for generating a navigational map of an environment

A robotic assistant comprises a plurality of sensors on a mobile chassis. An occupancy map includes obstacle cost values for particular areas or cells of the map. These obstacle cost values may be based on whether an obstacle is detected and the confidence value of that detection, estimated precision of the location of the detected object, and so forth. A set of rules for preferred locations of robot movement is used to assess the occupancy map and to determine constraint cost value for the particular areas. For example, the area in a center of a hallway may be associated with a relatively high constraint cost value. An aggregate cost value may be calculated by summing the obstacle cost value and the constraint cost value for the particular area. The aggregate cost value may be used to determine a maximum permissible speed for the robot while traversing the particular area.

Robot movement constraint system

This application describes techniques for determining a constituency and a layout of a group of devices within an environment. By determining the devices that form the group, as well as the positions of these devices relative to one another, the group of devices may be used to more intelligently output content within the environment.

Device-layout determinations

In some cases, a provisioned electronic device may receive a request for configuration information from an unprovisioned device. In response, the provisioned device may determine configuration information to send to the unprovisioned device based at least in part on the contents of the request. Examples of configuration information may include network credentials for connecting to a wireless local area network, a registration token that identifies the unprovisioned device to a fulfillment service, and user preferences associated with a user of the provisioned device. Further, the provisioned device may send the configuration information to the unprovisioned device. Upon receipt of the configuration information, the unprovisioned device may utilize the configuration information to order products from the fulfillment service. In some instances, the provisioned device may obtain the configuration information from the fulfillment service.

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Los Altos, CA, United States of America

Roger Robert Webster