A computer-implemented method for operating a mobile agent in an environment on the basis of a pose of the mobile agent. The location of the mobile agent is determined by way of the following steps in order to operate the mobile agent: capturing sensor data regarding walls and/or objects located in an environment, wherein the sensor data indicate the orientation and distance of the walls and/or objects in an agent-based agent coordinate system; establishing the pose of the mobile agent in the environment using a SLAM algorithm while taking account of a Manhattan orientation.

A moving object control system that controls an operation of a moving object obtains information of a sensor configured to recognize a periphery of the moving object, generates a dynamic prediction map including information indicating a position of a static obstacle recognized based on the information of the sensor and information indicating a position of a dynamic obstacle that changes with time and is recognized based on the information of the sensor, and generates a target trajectory for controlling traveling of the moving object by using the generated dynamic prediction map.

A system and method of an adaptive mapping system for semi-autonomous cleaning devices for improved navigation using a randomized dot pattern to represent dynamic areas and ensure precise localization in changing environments. A map is parameterized as an occupancy grid, where each cell is assigned the likelihood that it contains a physical object in the environment. A novel mapping technique is disclosed that intelligently distinguishes between static features (e.g., walls and pillars) and dynamic areas (e.g., places prone to frequent changes). By representing dynamic areas with a randomized dot pattern, an adaptive mapping system maintains high localization confidence for autonomous mobile robots (AMRs). This approach ensures uninterrupted robot operations, significantly reducing or eliminating the need for human intervention due to localization uncertainties and addresses the critical problem of navigating and operating efficiently in environments that undergo frequent changes.

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.

A map generation model building device includes a memory storing a map generation model building program, and a processor configured to execute the program, wherein the program generates embedding data by applying captured images taken by a movement device to an encoder module, generates spatial map data by recording the embedding data in map base data based on location information of the movement device, generates a rendering image based on the location information of the movement device in the spatial map data by using a decoder module, and train the a map generation model by comparing the rendering image with the captured image through a loss function and by updating the encoder module and the decoder module, the map base data includes a plurality of grids in which the embedding data is recorded, the embedding data includes RGB information and depth information for each pixel of the captured image.

Methods are disclosed that allow a robot, equipped with sensors and processors, to develop maps of its surroundings as it moves through a human environment, and to use these maps to detect and identify open doors, which it can then move through. Robots are disclosed to develop and use such maps to detect and identify open doors and move therethrough.

The described positional awareness techniques employing visual-inertial sensory data gathering and analysis hardware with reference to specific example implementations implement improvements in the use of sensors, techniques and hardware design that can enable specific embodiments to provide positional awareness to machines with improved speed and accuracy.

A traveling vehicle system includes: a plurality of traveling vehicles; and a controller that is capable of communicating with the plurality of traveling vehicles and that controls the plurality of traveling vehicles, a traveling region of the traveling vehicles having designated therein a plurality of blocking sections each of which undergoes, when occupied by one of the plurality of traveling vehicles, exclusive control to prohibit another traveling vehicle from moving thereinto. The controller determines, where in a series of operations to be executed by the traveling vehicle, the operation of the traveling vehicle from the start of traveling to stopping before executing a predetermined operation included in the series of operations is demarcated, whether or not to grant the traveling vehicle an occupation permission for the blocking sections to be occupied for the traveling vehicle to execute operations.

Disclosed are a cleaning path planning method based on a pathfinding cost, a chip, and a cleaning robot. The method includes step 1, configuring cleaning lines in a map matching a cleaning area according to map boundaries and obstacle boundaries in different directions in the cleaning area of a mobile robot; step 2, setting a candidate entrance/exit satisfying a preset pathfinding cost condition and found in a current non-cleaned sub-block by the mobile robot at a current position as a cleaning entrance position of the current non-cleaned sub-block; step 3, controlling the mobile robot to move to the cleaning entrance position of the current non-cleaned sub-block; step 4, searching for and planning a cleaning entrance position and a cleaning termination position of a next non-cleaned sub-block by repeating steps 2-3 described above in a case of the mobile robot moves to the cleaning termination position of the current non-cleaned sub-block.

A three dimensional grid map is generated to locate a cross member on a receiving vehicle which is coupled to a following vehicle. The location of the cross member is tracked during an unloading operation so the leading vehicle avoids the cross member when unloading material into the receiving vehicle, and so the fill level is more accurately identified.