Boston Dynamics, Inc.

Matthew Jacob Klingensmith

Marco Da Silva

Eric Cary Whitman

Alfred Anthony Rizzi

Brian Yamauchi

Christopher Stathis

Samuel Seifert

Dom Jonak

Dion Gonano

The operations of a computer-implemented method include obtaining a topological map of an environment including a series of waypoints and a series of edges. Each edge topologically connects a corresponding pair of adjacent waypoints. The edges represent traversable routes for a robot. The operations include determining, using the topological map and sensor data captured by the robot, one or more candidate alternate edges. Each candidate alternate edge potentially connects a corresponding pair of waypoints that are not connected by one of the edges. For each respective candidate alternate edge, the operations include determining, using the sensor data, whether the robot can traverse the respective candidate alternate edge without colliding with an obstacle and, when the robot can traverse the respective candidate alternate edge, confirming the respective candidate alternate edge as a respective alternate edge. The operations include updating, using nonlinear optimization and the confirmed alternate edges, the topological map.

Topology processing for waypoint-based navigation maps

Methods and apparatus for navigating a robot along a route through an environment, the route being associated with a mission, are provided. The method comprises identifying, based on sensor data received by one or more sensors of the robot, a set of potential obstacles in the environment, determining, based at least in part on stored data indicating a set of footfall locations of the robot during a previous execution of the mission, that at least one of the potential obstacles in the set is an obstacle, and navigating the robot to avoid stepping on the obstacle.

Ground clutter avoidance for a mobile robot

A method includes receiving, while a robot traverses a building environment, sensor data captured by one or more sensors of the robot. The method includes receiving a building information model (BIM) for the environment that includes semantic information identifying one or more permanent objects within the environment. The method includes generating a plurality of localization candidates for a localization map of the environment. Each localization candidate corresponds to a feature of the environment identified by the sensor data and represents a potential localization reference point. The localization map is configured to localize the robot within the environment when the robot moves throughout the environment. For each localization candidate, the method includes determining whether the respective feature corresponding to the respective localization candidate is a permanent object in the environment and generating the respective localization candidate as a localization reference point in the localization map for the robot.

Semantic models for robot autonomy on dynamic sites

A method of localizing a robot includes receiving odometry information plotting locations of the robot and sensor data of the environment about the robot. The method also includes obtaining a series of odometry information members, each including a respective odometry measurement at a respective time. The method also includes obtaining a series of sensor data members, each including a respective sensor measurement at the respective time. The method also includes, for each sensor data member of the series of sensor data members, (i) determining a localization of the robot at the respective time based on the respective sensor data, and (ii) determining an offset of the localization relative to the odometry measurement at the respective time. The method also includes determining whether a variance of the offsets determined for the localizations exceeds a threshold variance. When the variance among the offsets exceeds the threshold variance, a signal is generated.

Robot localization using variance sampling

A computer-implemented method when executed by data processing hardware of a legged robot causes the data processing hardware to perform operations including receiving sensor data corresponding to an area including at least a portion of a docking station. The operations include determining an estimated pose for the docking station based on an initial pose of the legged robot relative to the docking station. The operations include identifying one or more docking station features from the received sensor data. The operations include matching the one or more identified docking station features to one or more known docking station features. The operations include adjusting the estimated pose for the docking station to a corrected pose for the docking station based on an orientation of the one or more identified docking station features that match the one or more known docking station features.

Robust docking of robots with imperfect sensing

The disclosure provides a method for generating a joint command. The method includes receiving a maneuver script including a plurality of maneuvers for a legged robot to perform where each maneuver is associated with a cost. The method further includes identifying that two or more maneuvers of the plurality of maneuvers of the maneuver script occur at the same time instance. The method also includes determining a combined maneuver for the legged robot to perform at the time instance based on the two or more maneuvers and the costs associated with the two or more maneuvers. The method additionally includes generating a joint command to control motion of the legged robot at the time instance where the joint command commands a set of joints of the legged robot. Here, the set of joints correspond to the combined maneuver.

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Somerville, MA, United States of America

Matthew Jacob Klingensmith