Some aspects include a method for operating a robot in a workspace, including: capturing, with an image sensor, image data of the workspace including objects within the workspace as the robot moves within the workspace; identifying, with a processor of the robot, at least one characteristic in the image data, wherein the at least one characteristic comprises one of: an edge, a shape, and a color; determining, with the processor, an object type of an object; and instructing, with the processor, the robot to execute at least one action based on the at least one characteristic, wherein the at least one action comprises one of: driving along a modified path and driving around the object.

The present disclosure provides a moving robot including a body which forms an appearance, a traveler which moves the body with respect to a traveling surface in a traveling area, a sensing unit which acquires environment information of the traveling area, and a controller which sets a parameter tailored to the traveling area according to the environment information and performs pattern traveling of the traveling area. Accordingly, even when information on an environment in which the moving robot is installed is not obtained from a manufacture in advance, the moving robot can directly obtain information on the corresponding environment and set an optimum parameter according to the environment to increase efficiency.

A flexible material handling system for can handle varied loads and placements including operation in varying weather conditions, and integrates safety systems to tolerate pedestrians and manual vehicles in an operating environment. An autonomous vehicle is operable along a vehicle traversal path within a predetermined set of environmental conditions. A GPS base station is operatively in communication with the autonomous vehicle. A supervisor/orchestrator is operatively in communication with the autonomous vehicle and the GPS base station and is operative to coordinate movement of the autonomous vehicle along the vehicle traversal path and assign one or more tasks for the autonomous vehicle to accomplish.

A flexible material handling system for can handle varied loads and placements including operation in varying weather conditions, and integrates safety systems to tolerate pedestrians and manual vehicles in an operating environment. An autonomous vehicle is operable along a vehicle traversal path within a predetermined set of environmental conditions. A GPS base station is operatively in communication with the autonomous vehicle. A supervisor/orchestrator is operatively in communication with the autonomous vehicle and the GPS base station and is operative to coordinate movement of the autonomous vehicle along the vehicle traversal path and assign one or more tasks for the autonomous vehicle to accomplish.

An autonomous driving controller includes a plurality of parallel processors operating on common input data. Each of the plurality of parallel processors includes a general processor, a security processor subsystem (SCS), and a safety subsystem (SMS). The general processors, the SCSs, and the SMSs of the plurality of parallel processors are configured to first, boot the plurality of SCSs from ROM second, boot the plurality of SMSs of the plurality of parallel processors from RAM or ROM, and, third, boot the plurality of general processors of the plurality of parallel processors from RAM. Between booting of the SCSs and the SMSs, at least one of the plurality of SCSs may load SMS boot code into the RAM that is dedicated to the plurality of SMSs.

A computer-implemented method for determining a motion trajectory for a mobile robot based on an occupancy prior indicating probabilities of presence of dynamic objects and/or individuals in a map of an environment. Occupancy priors are determined by a reward function defined by reward function parameters. The determining of the reward function parameters includes: providing semantic maps; providing training trajectories for each of semantic maps; computing a gradient as a difference between an expected mean feature count and an empirical mean feature count depending on each of the semantic maps and on each of the training trajectories, the empirical mean feature count is the average number of features accumulated over the provided training trajectories of the semantic maps, wherein the expected mean feature count is the average number of features accumulated by trajectories generated depending on the current reward function parameters; and updating the reward function parameters depending on the gradient.

An autonomous mobile device (AMD) moves around a physical space while performing tasks. The AMD may have sensors with fields of view (FOVs) that are forward-facing. As the AMD moves forward, a safe region is determined based on data from those forward-facing sensors. The safe region describes a geographical area clear of obstacles during recent travel. Before moving outside of the current FOV, the AMD determines whether a move outside of the current FOV keeps the AMD within the safe region. For example, if a path that is outside the current FOV would result in the AMD moving outside the safe region, the AMD modifies the path until poses associated with the path result in the AMD staying within the safe region. The resulting safe path may then be used by the AMD to safely move outside the current FOV.

An autonomous mobile device (AMD) moves around a physical space while performing tasks. The AMD may have sensors with fields of view (FOVs) that are forward-facing. As the AMD moves forward, a safe region is determined based on data from those forward-facing sensors. The safe region describes a geographical area clear of obstacles during recent travel. Before moving outside of the current FOV, the AMD determines whether a move outside of the current FOV keeps the AMD within the safe region. For example, if a path that is outside the current FOV would result in the AMD moving outside the safe region, the AMD modifies the path until poses associated with the path result in the AMD staying within the safe region. The resulting safe path may then be used by the AMD to safely move outside the current FOV.

A method in a navigational controller includes: obtaining (i) a plurality of task fragments identifying respective sets of sub-regions in a facility, and (ii) an identifier of a task to be performed by a mobile automation apparatus at each of the sets of sub-regions; selecting an active one of the task fragments according to a sequence specifying an order of execution of the task fragments; generating a path including (i) a taxi portion from a current position of the mobile automation apparatus to the sub-regions identified by the active task fragment, and (ii) an execution portion traversing the sub-regions identified by the active task fragment; during travel along the taxi portion, determining, based on a current pose of the mobile automation apparatus, whether to initiate execution of another task fragment; and when the determination is affirmative, updating the sequence to mark the other task fragment as the active task fragment.

A method in a navigational controller includes: obtaining (i) a plurality of task fragments identifying respective sets of sub-regions in a facility, and (ii) an identifier of a task to be performed by a mobile automation apparatus at each of the sets of sub-regions; selecting an active one of the task fragments according to a sequence specifying an order of execution of the task fragments; generating a path including (i) a taxi portion from a current position of the mobile automation apparatus to the sub-regions identified by the active task fragment, and (ii) an execution portion traversing the sub-regions identified by the active task fragment; during travel along the taxi portion, determining, based on a current pose of the mobile automation apparatus, whether to initiate execution of another task fragment; and when the determination is affirmative, updating the sequence to mark the other task fragment as the active task fragment.