A mobile cleaning robot includes a cleaning head configured to clean a floor surface in an environment, and at least one camera having a field of view that extends above the floor surface. The at least one camera is configured to capture images that include portions of the environment above the floor surface. The robot includes a recognition module is configured to recognize objects in the environment based on the images captured by the at least one camera, in which the recognition module is trained at least in part using the images captured by the at least one camera. The robot includes a storage device is configured to store a map of the environment. The robot includes a control module configured to control the mobile cleaning robot to navigate in the environment using the map and operate the cleaning head to perform cleaning tasks taking into account of the objects recognized by the recognition module.

A mobile cleaning robot includes a cleaning head configured to clean a floor surface in an environment, and at least one camera having a field of view that extends above the floor surface. The at least one camera is configured to capture images that include portions of the environment above the floor surface. The robot includes a recognition module is configured to recognize objects in the environment based on the images captured by the at least one camera, in which the recognition module is trained at least in part using the images captured by the at least one camera. The robot includes a storage device is configured to store a map of the environment. The robot includes a control module configured to control the mobile cleaning robot to navigate in the environment using the map and operate the cleaning head to perform cleaning tasks taking into account of the objects recognized by the recognition module.

This disclosure relates generally to systems and methods for object detection using a geometric semantic map based robot navigation using an architecture to empower a robot to navigate an indoor environment with logical decision making at each intermediate stage. The decision making is further enhanced by knowledge on actuation capability of the robots and that of scenes, objects and their relations maintained in an ontological form. The robot navigates based on a Geometric Semantic map which is a relational combination of geometric and semantic map. In comparison to traditional approaches, the robot's primary task here is not to map the environment, but to reach a target object. Thus, a goal given to the robot is to find an object in an unknown environment with no navigational map and only egocentric RGB camera perception.

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.

A movable platform control method and device, a movable platform, a control system, and a computer-readable storage medium are provided. The method comprises: determining semantic information of different objects located on a movement path; determining different safe execution distances for the different objects based on the semantic information of the different objects; and controlling the movable platform to execute a cleaning task and/or obstacle avoidance task according to the different safe execution distances of the different objects, where the semantic information of the different objects allows differentiation between obstacles and objects to be cleaned.

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.

A cleaning method includes: determining a target cleaning region, generating a cleaning trajectory in the target cleaning region in response to a path planning instruction, and performing a cleaning action along the cleaning trajectory. The present disclosure also discloses a cleaning apparatus, a cleaning device, and a storage medium.

A robotic work tool system (200) comprising a robotic work tool (100) comprising a controller (110), the controller (110) being configured to determine an area locality (310) associated with a hindrance; determine a classifier (C) associated with the area locality (310); and determine an action for the robotic work tool (100), wherein the action is based on the classifier (C).

A computer-implemented method of assisting in the operation of a vehicle is disclosed, the method comprises the steps of: with at least one sensor, sensing an environment of the vehicle thereby obtaining sensor data, and deriving spatial information of the environment and semantic information from the sensor data. Furthermore, generating a dynamic occupancy grid model, in which the sensed environment is represented as a grid consisting of a plurality of grid cells, the grid cells comprising occupying information and a dynamic state represented by a set of particles, and assigning the grid cells and the particles semantic information derived from the sensor data, wherein the semantic information is represented by a set of categories. The method further comprising the steps of predicting new particle positions on the grid; determining for the grid cells predicted semantic information based on combining the semantic information assigned to the grid cells with the semantic information assigned to the particles based on their predicted new particle positions; obtaining new sensor data, and updating the predicted semantic information assigned to the grid cells and the semantic information assigned to the particles from the new sensor data, and deriving an automated driving action based on the determined new semantic information and the dynamic state of the one or more grid cells. Also disclosed is a system for performing the computer-implemented method of assisting operation of a vehicle and the vehicle comprising said system.

To control a mobile object such as a robot by using data including a map in which class attributes for classes corresponding to rooms are recorded. Feature information about a class or mobile object control information about the class is recorded as class attributes in association with the class serving as segmented areas in a map of the traveling area of the mobile object such as a robot, for example, a semantic map. The map is a map that allows identification of a room type and the border between rooms, and the presence or absence of a person in a room or information indicating whether the entry of the robot is allowed is recorded as class attributes for the classes corresponding to each room. Travel control for the robot is performed using a map or data in which the class attributes are recorded.