A mobile apparatus includes circuitry to control the mobile apparatus to perform teaching travel and autonomous travel, generate; for each of nodes on a travel route independently for external sensors, calculation information for calculating a deviation between a node passed in the teaching travel and a point passed in the autonomous travel; store in the memory the calculation information in association with the node and the external sensor; calculate, for each node independently for the external sensors, the deviation based on the calculation information and a of the extern sensor value in the autonomous travel; determine, for each node independently for the external sensors, the calculated deviation as a position and posture of the node with reference to the position and posture of the mobile apparatus; integrate the positions and postures of the node determined independently for the external sensors; and control the mobile apparatus to autonomously travel.

A mobile cleaning robot configured to clean an environment. The mobile cleaning robot can include a body and a drivetrain operable to move the body within the environment. The robot can include a sensor connected to the body and configured to generate a sensor signal based on interactions between the mobile cleaning robot and the environment. The robot can include an image capture device connected to the body and configured to generate an image stream based on an optical filed of view of the image capture device. The robot can include a controller connected to the body and configured to determine whether the mobile cleaning robot is in a stasis condition based on the image stream and the sensor signal. The controller can also update a map of the environment based on the stasis determination.

A method includes receiving one or more past trajectories navigated by a robotic device in an environment, wherein the one or more past trajectories are associated with initial environmental sensor data and one or more obstacle detection heuristics. The method also includes determining, based at least on subsequent environmental sensor data, one or more updated obstacle detection heuristics. The method further includes determining, based on the one or more updated obstacle detection heuristics and the initial environmental sensor data, one or more predicted drivable areas in the environment. The method additionally includes, based on the one or more predicted drivable areas including the one or more past trajectories, using the one or more updated obstacle detection heuristics to determine future navigation of the robotic device.

A method and apparatus are disclosed for a clutter tidying robot utilizing floor segmentation for its mapping and navigation system, whereby a perception module and navigation module transform lidar and image data from lidar sensors and cameras of a robot sensing system using segmentation and pseudo-laserscan or point cloud transformations to generate global and local maps. The robot pose and maps are transmitted to a robot brain that directs an action module to produce robot action commands controlling the operation of a clutter tidying robot using the pose and map data. In this manner multi-stage planning and sophisticated obstacle avoidance techniques may be incorporated into autonomous robot operations.

The present disclosure provides a method and apparatus for return control of a swimming pool cleaning robot, and an electronic device and a computer storage medium thereof. The method includes: in response to a trigger of a return instruction, acquiring a current position of the swimming pool cleaning robot in a map for a swimming pool; and generating a return path according to a reachable block in the map for the swimming pool, a predetermined return position, and the current position, and controlling the swimming pool cleaning robot to return from the current position to the predetermined return position on the basis of the return path. Therefore, the swimming pool cleaning robot is controlled to automatically return to a designated position of the swimming pool, such that use smartness of the swimming pool cleaning robot is improved, and use experience of a user is enhanced.

The invention discloses a method for robots to improve the accuracy of an obstacle labeling, which comprises: making two positionings according to set moments, and then acquiring positioning poses of the two positionings on a grid map respectively at a first moment and a second moment; defining coverage areas of the first and the second moments according to positions of the two positionings, acquiring confidence coefficients of the two positionings, and processing the coverage areas through the confidence coefficients; interpolating the positioning poses, and constructing a closed graph according to the positioning poses, the pose interpolation, and the processed coverage areas; and obtaining a grid occupied by the closed graph on the grid map and modifying the obstacle labeling according to the grid occupied by the closed graph on the grid map and the area of the closed graph.

The present invention relates to a road crossing method for a mobile robot. The road crossing method comprises the mobile robot approaching a road crossing. Further, the road crossing method comprises estimating, with a data processing unit, a location and time of collision with at least one dynamic object on the road crossing. Further still, the road crossing method comprises generating, with the data processing unit, control commands for the mobile robot to avoid collision with the at least one dynamic object based on the estimated location and time of collision with the at least one dynamic object. In addition, the present invention relates to a mobile robot comprising the data processing unit and configured to carry out the road crossing method. In a further aspect, the present invention relates to a positioning method for a wheeled mobile robot positioned on a sloped terrain, comprising the mobile robot performing at least one maneuver for minimizing a magnitude of an acceleration vector of the mobile robot due to the gravity force acting on the mobile robot. In addition, the present invention relates to a mobile robot configured to carry out the positioning method.

The method of managing unmanned aerial vehicle (UAV) patrols is a method for organizing and managing a fleet of UAVs for patrolling a geographic region for the detection of threats. During patrols, the UAVs visit two types of waypoints: critical waypoints and strategic waypoints. To establish the strategic waypoints, a set of seed points are generated using a beta probability distribution. Voronoi tessellation is applied to produce the set of strategic waypoints within the region of interest by using the set of seed points as an input. Each of the strategic waypoints has a set of coordinates on the three-dimensional occupancy map associated therewith. The set of coordinates associated with each of the strategic waypoints is a centroid of a corresponding Voronoi cell produced by the Voronoi tessellation. A priority score is assigned to each of the critical waypoints and each of the strategic waypoints.

The present invention relates to a method for filtering inputs to a localization method of an autonomous vehicle (10) in an operating environment (U), comprising the steps of: providing a predefined occupancy map which represents known objects (W) present in the operating environment (U); generating an optimized data structure which represents occupied cells in a display of the occupancy map in a coordinate system; detecting the operating environment (U) of the vehicle (10) by means of at least one sensor unit (12) which is configured to detect objects in the operating environment (U) and their distances from the vehicle (10), wherein each detected object is assigned a measured value (M1, M2) in the coordinate system; and for each measured value (M1, M2) searching within a search radius around the measured value (M1, M2) for an occupied cell in the optimized data structure; if an occupied cell is found within the search radius, forwarding the measured value (M1) to a subsequent localization method, and, if no occupied cell is found within the search radius, discarding the measured value (M2).

Systems and methods for vehicle localization are provided for a robotic vehicle, such as an autonomous mobile robot. The vehicle can be configured with multiple localization modes used for localization and/or pose estimation of the vehicle. In some embodiments, the vehicle comprises a first set of exteroceptive sensors and a second set of exteroceptive sensors, each being used for a different localization modality. The vehicle is able to disregard at least one localization modality for a number of different reasons, e.g., the disregarded location modality is adversely affected by the environment, to use less than the full complement of localization modalities to continue to stably localize the vehicle within an electronic map. In some embodiments, a localization modality may be disregarded for pre-planned reasons.