The present disclosure relates to an autonomous mobile device and its control method and apparatus and a storage medium, the control method includes: an obtaining step configured to obtain point cloud data of the autonomous mobile device in a current work environment; a determination step configured to determine whether there exists an obstacle in the work environment based on the point cloud data; a processing step configured to, when it is determined that there exists an obstacle, recognize a type of the obstacle based on the point cloud data, and execute an obstacle avoidance action corresponding to the type of the obstacle. As such, the robustness of the obstacle avoidance action can be increased.

A method and an apparatus for determining a position of a shelf are provided. The method may include: obtaining a number of automated guided vehicles with shelf scanning devices; determining, based on the number of the automated guided vehicles, a scanning area of a place to which each automated guided vehicle belongs; determining, based on the scanning area, a scanning route of the scanning area to which each automated guided vehicle belongs; transmitting the scanning route of the scanning area to which the automated guided vehicle belongs, to the automated guided vehicle; and determining a position of a shelf in the scanning area to which the automated guided vehicle belongs based on scanning information of a shelf scanning device on the automated guided vehicle and position information of the automated guided vehicle.

A work vehicle capable of autonomously moving in a work area, the work vehicle including a control unit, wherein the control unit starts an alignment in response to a request for the alignment from a manned forklift acquired via a communication unit, determines whether or not a positional relation between the work vehicle and the manned forklift is in a state before starting the alignment, and notifies the manned forklift that a movement direction of the work vehicle and a movement direction of the manned forklift are not orthogonal to each other via the communication unit, when the positional relation between the work vehicle and the manned forklift is not in the state.

In a vehicle remote instruction system, a remote commander issues a remote instruction relating to travel of an autonomous driving vehicle based on sensor information from an external sensor that detects an external environment of the autonomous driving vehicle. The vehicle remote instruction system sets a range of information to be transmitted to the remote commander among the sensor information detected by the external sensor, as a limited information range, based on the external situation or an external situation obtained based on map information and a trajectory of the autonomous driving vehicle.

In a vehicle remote instruction system, a remote commander issues a remote instruction relating to travel of an autonomous driving vehicle based on sensor information from an external sensor that detects an external environment of the autonomous driving vehicle. The vehicle remote instruction system sets a range of information to be transmitted to the remote commander among the sensor information detected by the external sensor, as a limited information range, based on the external situation or an external situation obtained based on map information and a trajectory of the autonomous driving vehicle.

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.

In a vehicle remote instruction system, a remote commander issues a remote instruction relating to travel of an autonomous driving vehicle based on sensor information from an external sensor that detects an external environment of the autonomous driving vehicle. The vehicle remote instruction system sets a range of information to be transmitted to the remote commander among the sensor information detected by the external sensor, as a limited information range, based on the external situation or an external situation obtained based on map information and a trajectory of the autonomous driving vehicle.

Provided is a robot cleaner includes: a sensor; at least one memory storing one or more instructions; and at least one processor, wherein the at least one processor, by executing the one or more instructions, is configured to identify a plurality of objects based on sensing data acquired through the sensor, identify a target object located on a travel surface of the robot cleaner among the plurality of objects, acquire power information indicating remaining power of the robot cleaner, determine whether to avoid the target object based on a result of comparing the power information with a threshold power value, based on the power information being less than the threshold power value, control the robot cleaner to travel to avoid the target object, based on the power information being greater than or equal to the threshold power value, acquire feature information of the target object based on the sensing data, acquire type information of the target object based on the feature information, based on the type information including a predetermined type, control the robot cleaner to travel to avoid the target object, and based on the type information not including the predetermined type, control the robot cleaner to travel based on a movement path for moving the target object to a target location.

A system and method for lateral vehicle detection is disclosed. A particular embodiment can be configured to: receive lateral image data from at least one laterally-facing camera associated with an autonomous vehicle; warp the lateral image data based on a line parallel to a side of the autonomous vehicle; perform object extraction on the warped lateral image data to identify extracted objects in the warped lateral image data; and apply bounding boxes around the extracted objects.

A computer-implemented method of planning a path for a mobile robot in the presence of a set of obstacles comprises: computing for each obstacle a probabilistic obstacle position distribution; computing at least one path-independent function as a combination of the probabilistic obstacle position distributions; and for at least one candidate path, determining an upper bound on the total probability of obstacle collision along that path, by aggregating the path-independent function based on an area defined by the candidate path and a mobile robot shape, wherein the path-independent function is independent of the candidate path.