Irobot Corporation

Artem Gritsenko

Zacharias Psarakis

Daniele Tamino

John JianShi Wang

Vazgen Karapetyan

Erik Schembor

James R Salvati

Jonathan J Dorich

Steven Kordell

Tadvana S Canakapalli

Benjamin Pheil

Andrea M Okerholm Huttlin

Guanlai Li

Jose Luis Franco Monsalve

Yao Chen

Renaud Moser

Arman Karimian

Robot localization or mapping can be provided without requiring the expense or complexity an 'at-a-distance' sensor, such as a camera, a LIDAR sensor, or the like. Adjacency-derived landmark features can be used and non-unique landmark features can be accommodated. Uncertainty in robot pose can be tracked and compared to an adaptive threshold, and non-dock and dock-based localization behavior can be controlled based on the uncertainty, the adaptive threshold, one or more other thresholds, and the accessibility of available differently oriented landmark features, such as perpendicularly oriented straight wall segments landmark features. Available features can be sorted according to a quality metric, and path planning and navigation techniques are also included for helping obtain successful wall-following and localization observations.

Robot localization and mapping accommodating non-unique landmarks

An autonomous robot is maneuvered around a feature in an environment along a first trajectory. Data characterizing the first trajectory is stored as a trajectory landmark. The autonomous cleaning robot is maneuvered along a second trajectory. Data characterizing the second trajectory is compared to the trajectory landmark. Based on comparing the data characterizing the second trajectory to the trajectory landmark, it is determined that the first trajectory matches the second trajectory. A transform that aligns the first trajectory with the second trajectory is determined. The transform is applied to an estimate of a position of the autonomous cleaning robot as a correction of the estimate.

Trajectory-based localization and mapping

Robot localization or mapping can be provided without requiring the expense or complexity an 'at-a-distance' sensor, such as a camera, a LIDAR sensor, or the like. Adjacency-derived landmark features can be used and non-unique landmark features can be accommodated. Uncertainty in robot pose can be tracked and compared to an adaptive threshold, and non-dock and docks based localization behavior can be controlled based on the uncertainty, the adaptive threshold, one or more other thresholds, and the accessibility of available differently oriented landmark features, such as perpendicularly oriented straight wall segments landmark features. Available features can be sorted according to a quality metric, and path planning and navigation techniques are also included for helping obtain successful wall-following and localization observations.

An autonomous cleaning robot includes a controller configured to execute instructions to perform one or more operations. The one or more operations includes operating a drive system to move the cleaning robot in a forward drive direction along a first obstacle surface with a side surface of the cleaning robot facing the first obstacle surface, then operating the drive system to turn the cleaning robot such that the side surface of the cleaning robot faces a second obstacle surface, then operating the drive system to move the cleaning robot in a rearward drive direction along the second obstacle surface, and then operating the drive system to move the cleaning robot in the forward drive direction along the second obstacle surface.

Navigation of autonomous mobile robots

Growing community of inventors

Malden, MA, United States of America

Artem Gritsenko