The disclosure relates to a device and method for creating a dynamic occupancy grid map. Specifically, a dynamic occupancy grid map creation device comprises an object detector calculating a measurement based on a reception signal received from a radar sensor and detecting an object around a host vehicle, a shape estimator estimating a shape of a target vehicle when the target vehicle is detected, an occupancy probability updater ellipse-fitting the shape of the target vehicle to a point cloud and updating an occupancy probability of a grid for the target vehicle of a dynamic occupancy grid map (DOGM) based on the fitted shape, and a compensator compensating for a position of the host vehicle over time, on the dynamic occupancy grid map.

An optimized coverage-path planning method for a single unmanned surface mapping vessel (USMV) is implemented with a system including a computer processor executing a computer program loaded in a storage device and implanting the method. The method includes rasterizing and initializing an environmental map, and an unmanned vessel outputting position data and obstacle data according to the environmental map so that path planning is started to provide a target point to the unmanned vessel. In case of tripping in a local optimum at a current-level map for the target point, the map level is updated in an ascending order until the highest level, in order to identify a map level in which the target point is found.

Systems and methods for autonomous vehicle path planning are described herein. An example vehicle includes an image sensor to obtain an image of a scene of an area surrounding the vehicle. The vehicle also includes navigation system circuitry to: analyze the image and generate a semantically segmented image that identifies one or more types of features in the image; project the semantically segmented image to a two-dimensional (2D) map projection; convert the 2D map projection into a cost map; and determine a path for the vehicle based on the cost map.

A robot positioning method includes: collecting, by a robot, data of laser points by using a laser sensor; obtaining multiple layers of grid maps layer by layer in order from low resolution to high resolution; traversing candidate solutions in a current layer of grid map; controlling a plurality of occupancy probability values obtained correspondingly for the data of laser points at a currently traversed candidate solution to be sequentially summed when the robot determines that the currently traversed candidate solution is a feasible solution; setting, based on a ratio of a resolution of a next layer of grid map to a resolution of the current layer of grid map, the determined feasible solution as a candidate solution of the next layer of grid map; recursively determining feasible solutions in candidate solutions of the layers of grid map in the order from low resolution to high resolution.

This application provides a swimming pool robot, including a robot body, a filter, a control unit, and a moving mechanism. Operating environments of the swimming pool robot at least include a first operating environment and a second operating environment. The moving mechanism at least includes a first moving mechanism configured to drive the swimming pool robot to move in the first operating environment and a second moving mechanism configured to drive the swimming pool robot to move in the second operating environment. The first operating environment is an underwater environment. The second operating environment is a non-underwater environment. The control unit is capable of controlling, based on a current operating environment of the swimming pool robot, a moving mechanism corresponding to the current operating environment of the swimming pool robot to drive the swimming pool robot to move. According to this application, operating efficiency of the robot can be improved.

A method and system for exploring a real-world environment using a mobile robot platform comprising mapping the environment at a first time, to generate a first representation of the environment and identifying an initial location of an object in the first representation of the environment. The environment is mapped at a second time, to generate a second representation of the environment. The mappings are generated based on data obtained from a sensor associated with the mobile robot platform. Based on data obtained from the sensor, a new location of the object is identified in the second representation of the environment, and a difference between the initial location of the object and the new location of the object is determined. Using a manipulator the object is moved to the initial location when it is determined that the initial location of the object differs from the new location.

A computer implemented method for determining information of static occupancy comprises the following steps carried out by computer hardware components: based on a plurality of existing hypotheses for the information of static occupancy, determining a plurality of predicted hypotheses; based on measurements, correcting the plurality of predicted hypotheses to obtain predicted and corrected hypotheses; merging the predicted and corrected hypotheses to obtain merged hypotheses; and pruning at least a portion of the merged hypotheses to obtain final hypotheses; wherein the method further comprises at least one of the following: during pruning, hypotheses with a covariance above a pre-determined covariance threshold are disregarded; and/or during merging, for each of the merged hypotheses, at most two predicted and corrected hypotheses are merged; and/or after pruning, at least one hypothesis is added to the final hypotheses at a location of at least one measurement of the measurements which is not covered by a hypothesis of the hypotheses.

A method comprises: a lawn mower robot driving to set a boundary of a target work area in which at least three anchors are installed on the boundary; the lawn mower robot receiving a signal from the anchors and setting, as a shadow area, an area where the signal cuts off; the lawn mower robot returning to an initial position and storing driving information received from the anchors transmitting, to a simulator, the driving information and information on the shadow area and the target work area; generating, by the simulator, an obstacle map based on the shadow area of each anchor; the simulator overlapping an externally provided map and the obstacle map, and outputting the same on a screen; and recommending, to a user, positions at which the size of the shadow areas identified within the target work area can be minimized.

Disclosed is a method of detecting a dangerous object for an aerial vehicle. The method includes setting an object detection area in air in which the aerial vehicle is in flight using a first sensor, a second sensor, and a third sensor; detecting an object in the set object detection area; generating detailed object information on the detected object; and determining whether the detected object is the dangerous object based on the generated detailed object information.

Systems and methods for detecting, mapping, and route planning around cliffs for robotic devices are disclosed herein. According to at least one non-limiting exemplary embodiment, a robot processing a three-dimensional range sensor scan may utilize pre-computed neighboring points to detect cliffs, navigable ramps, and holes in a plane of a map used by the robotic device to navigate.