Provided is a medium storing instructions that when executed by one or more processors of a robot effectuate operations including: obtaining, with a processor, first data indicative of a position of the robot in a workspace; actuating, with the processor, the robot to drive within the workspace to form a map including mapped perimeters that correspond with physical perimeters of the workspace while obtaining, with the processor, second data indicative of displacement of the robot as the robot drives within the workspace; and forming, with the processor, the map of the workspace based on at least some of the first data; wherein: the map of the workspace expands as new first data of the workspace are obtained with the processor; and the robot is paired with an application of a communication device.

Provided is a process, including: obtaining, with one or more processors, first depth data, wherein: the first depth data indicates a first distance from a robot at a first position to a surface of, or in, a workspace in which the robot is disposed, the first depth data indicates a first direction in which the first distance is measured, the first depth data indicates the first distance and the first direction in a frame of reference of the robot, and the frame of reference of the robot is different from a frame of reference of the workspace; translating, with one or more processors, the first depth data into translated first depth data that is in the frame of reference of the workspace; and storing, with one or more processors, the translated first depth data in memory.

The application provides a structured light module and an autonomous mobile device. The structured light module includes a first camera and line laser emitters for collecting a first environmental image containing laser stripes generated when the line laser encounters an object. The structured light module can also capture a visible light image through a second environmental image that does not contain laser stripes. Both the first and second environmental images can help to detect more accurate and richer environmental information, expanding the application range of laser sensors.

Provided is a process that includes: obtaining a first version of a map of a workspace; selecting a first undiscovered area of the workspace; in response to selecting the first undiscovered area, causing the robot to move to a position and orientation to sense data in at least part of the first undiscovered area; and obtaining an updated version of the map mapping a larger area of the workspace than the first version.

Provided is a process executed by a robot, including: traversing, to a first position, a first distance in a backward direction; after traversing the first distance, rotating 180 degrees in a first rotation; after the first rotation, traversing, to a second position, a second distance in the second direction; and after traversing the second distance, rotating 180 degrees in a second rotation such that the field of view of the sensor points in the first direction.

Provided is a process, including: obtaining, with one or more processors, first depth data, wherein: the first depth data indicates a first distance from a robot at a first position to a surface of, or in, a workspace in which the robot is disposed, the first depth data indicates a first direction in which the first distance is measured, the first depth data indicates the first distance and the first direction in a frame of reference of the robot, and the frame of reference of the robot is different from a frame of reference of the workspace; translating, with one or more processors, the first depth data into translated first depth data that is in the frame of reference of the workspace; and storing, with one or more processors, the translated first depth data in memory.

Provided is a system including a robot and an application of a communication device. The robot includes a medium storing instructions that when executed by a processor of the robot effectuate operations including: obtaining first data indicative of a relative position of the robot in a workspace; actuating the robot to drive within the workspace to form a map including mapped perimeters that correspond with physical perimeters of the workspace while obtaining second data indicative of movement of the robot; and forming the map of the workspace based on at least some of the first data, wherein the map of the workspace expands as new first data are obtained, until all perimeters of the workspace are included in the map. The application is configured to display information, such as the map, and receive user input.

Provided is a system including a robot and an application of a communication device. The robot includes a medium storing instructions that when executed by a processor of the robot effectuate operations including: obtaining first data indicative of a relative position of the robot in a workspace; actuating the robot to drive within the workspace to form a map including mapped perimeters that correspond with physical perimeters of the workspace while obtaining second data indicative of movement of the robot; and forming the map of the workspace based on at least some of the first data, wherein the map of the workspace expands as new first data are obtained, until all perimeters of the workspace are included in the map. The application is configured to display information, such as the map, and receive user input.

This disclosure relates generally to a method and system for task feasibility analysis with explanation for robotic task execution. Conventional methods for task feasibility analysis does not utilize an ontology for task capability understanding. The present disclosure uses an explainable semantic approach for checking task feasibility in a real world. The method creates scene graphs which is further used for generating a global knowledge graph and a semantic map. These are used for task feasibility analysis for an input task instruction received from a user. When the user provides the task instruction the method checks whether it is feasible or not. This helps in avoiding dead end tasks and provides the user to alter the task instruction towards feasible task. The disclosed method is used for robotic task execution in an environment.

Aspects of the present disclosure relate to systems and methods for mobile device control using language input.