A method of operating a robotic vehicle is provided. The method includes generating, using a prediction model, first scores, each of the first scores associated with a possible action of the robotic vehicle. The prediction model generates the first scores based, at least in part, on a predicted probability of the robotic vehicle encountering a dynamic object. The method further includes generating, using an analytical model, second scores, each of the second scores associated with a possible action of the robotic vehicle. The analytical model generates the second scores based, at least in part, on the information on the static objects. The method also includes combining the first scores with the second scores to generate combined scores, and selecting an action for the robotic vehicle based, at least in part, on the combined scores. A motion planning module and a robotic vehicle implementing the method are also disclosed.

A grid map-based robot pathfinding method includes obtaining a first grid map and a second grid map, wherein the second grid map is generated by merging grids in the first grid map, and the resolution of the second grid map is lower than that of the first grid map; planning a travel path of a robot from a current position to a preset target point according to the second grid map; and determining an obstacle position if the travel path is impassable, determining a first path of the robot from the current position to the obstacle position based on the second grid map, and determining a second path of the robot from the preset target point to the obstacle position based on the second grid map; planning a transition path from an endpoint of the first path to an endpoint of the second path according to the first grid map; and obtaining a target path of the robot according to the first path, the second path, and the transition path. Related apparatus, robots, and readable non-transitory storage medium are disclosed.

A computer-implemented method for driving assistance in a vehicle. The method includes generating, based on radar point sensor data of an environment of the vehicle, a three-dimensional occupancy grid map (3D OGM). The method includes generating, based on the radar point sensor data, a number of feature grid maps (FGMs). A respective feature dimension of each of the FGMs corresponds to a feature of the radar point sensor data. The method includes generating, based on the 3D OGM and the number of FGMs, a refined occupancy grid (OGM). The method includes providing the refined OGM for usage by an assistance system of the vehicle.

The disclosure generally relates to a method and a system for heterogeneous autonomous mobile robots for coverage path planning. The method may include receiving sensor data from one or more sensor devices. The sensor data includes information corresponding to one or more robots in a predefined region. The method may further include generating a map for the one or more robots based on the received sensor data. The map includes a probable occupancy of each of the plurality of cells by the one or more robots in the predefined region. The method further includes determining a set of poses of the one or more robots based on the generated map and an optimal set of poses from the set of poses based on the visibility matrix. The method may further include generating a coverage path plan for each of the one or more robots based on the determined optimal set of poses.

A computer-implemented system and method relate a mobile robot. State data is generated using sensor data from at least one sensor. A current confident zone is identified on a unified confident zone map using the state data. The unified confident zone map includes confident zones. Each confident zone is indicative of a given confidence level of given state data of a selected sensor modality for a given location. Assessment data is generated that indicates whether the current confident zone is deemed a failure zone. A mobile robot is controlled based on a control command. The control command relates to a recovery plan of moving the mobile robot out of the current confident zone when the assessment data indicates that the current confident zone is the failure zone. The control command relates to another plan when the assessment data indicates that the current confident zone is not the failure zone.

Disclosed herein are devices, methods, and systems for map regeneration of an environment. The system includes a map manager configured to maintain a global map of the environment. The system also includes a robot configured to determine a correspondence between a mapped subarea of the environment and a global map of the environment and, based on the correspondence, transmit map data of the mapped subarea to a map manager. The map manager is configured to determine a quality metric of the map data of the mapped subarea with respect to the global map and update the global map with the map data of the mapped subarea based on the quality metric.

Approaches for environment reconstruction and path planning for autonomous machine systems and applications are described. An iterative volumetric mapping function for an ego-machine may compute a distance field, and from the distance field derive a cost map representing a volumetric reconstruction of the physical environment around the ego-machine. The cost map may be used for collision avoidance and path planning. The iterative volumetric mapping function may also optionally compute a color integration map and visualization mesh from the distance field that can be used for visualization of the physical environment around the ego-machine. The cost map may be computed as a Euclidean Signed Distance Field (ESDF) and the distance field from which the cost map is computed may include a Truncated Signed Distance Field (TSDF). The distance field, cost map, color integration map and visualization mesh may each be stored in memory as maps of a plurality of map layers.

The present invention relates to a system and method for use in navigating a vehicle within an environment, and in one particular example, to a system and method for navigation planning in an unstructured environment including unobservable features, such as negative obstacles and/or occluded regions.

The apparatus for detecting and removing a dynamic obstacle of a robot and the operating method thereof according to a predetermined exemplary embodiment detect and remove the dynamic obstacle while simultaneously performing the mapping and the localizing using the simultaneous localization and mapping (SLAM) technique to efficiently detect and remove a dynamic obstacle even in a situation in which a dynamic change of surrounding environment is severe and an environment to be localized is large.

A method and an apparatus for controlling a robot, an electronic device, and a computer-readable storage medium. The control method includes: acquiring an environmental map obtained by detecting a current environment; detecting the environmental map to acquire at least one right angle corner point in the environmental map; determining a coordinate system in which the right angle corner point is located and using the right angle corner point of which the coordinate system satisfies a preset condition as a target right angle corner point; and controlling the robot to clean the target right angle corner point.