Amazon Technologies, Inc.

Mohit Deshpande

Roger Robert Webster

James Charles Zamiska

David Allen Fotland

Robert Franklin Ebert

Eric Peter Raeber

Rajasimman Madhivanan

Nikhil Sharma

Jong Jin Park

Chang Young Kim

Rachel Liao

Dhruva Kumar

An autonomous mobile device (AMD) may move around a physical space to perform tasks. The AMD may explore the physical space to determine map data characterizing the physical space. An exploration system maintains a world snapshot that is representative of the AMD with respect to the physical space. A plurality of planning algorithm modules are available, each able to achieve a particular exploration goal or operate under particular conditions such as initial motion, loop closure, frontier exploration, and so forth. One or more planning algorithms are selected and provided with the world snapshot data. These planning algorithms provide responses and may also determine plan data. The resulting plan data is then selected for execution. The execution of the selected plan data operates the AMD to explore the physical space.

System for planning exploration by autonomous mobile device

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

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

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Beavercreek, OH, United States of America

Mohit Deshpande