This document describes motion planning with variable grid resolution for graph-search-based planning. An example system includes a processor that obtains an initial pose, a goal pose, and an obstacle map for an environment. The processor uses a motion-planning algorithm to determine a path or trajectory using two or more grid resolutions for a graph-based search. The path includes a series of waypoints, including two-dimensional positional coordinates (and time coordinates if a trajectory), to navigate from the initial pose towards the goal pose. Operation of the host vehicle is then controlled to maneuver along the path using an assisted-driving or autonomous-driving system. In this way, motion planning is performed for the entire path but uses a coarser grid resolution for the portion nearer the goal pose. This allows motion planning for autonomous parking, especially in environments that include static and dynamic objects, to be handled in a more computationally-efficient manner.

A pathfinding apparatus acquires vehicle information, map information, obstacle information. The vehicle information includes a current location and a goal location for multiple vehicles. The map information includes a map of a target space. The obstacle information includes history of locations of one or more moving obstacles. The pathfinding apparatus generates one or more obstacle path for each moving obstacle during a target time window, and generates multiple candidate path sets each of which includes a vehicle path during the target time window for each vehicle. The vehicle path is conflict-free with the other vehicle paths and the obstacle paths. The pathfinding apparatus evaluates the candidate path sets through a heuristic search of continuations of the vehicles paths in the candidate path sets, selects one of the candidate path sets based their evaluations, and outputs the selected candidate path set.

A pathfinding apparatus acquires vehicle information, map information, obstacle information. The vehicle information includes a current location and a goal location for multiple vehicles. The map information includes a map of a target space. The obstacle information includes history of locations of one or more moving obstacles. The pathfinding apparatus generates one or more obstacle path for each moving obstacle during a target time window, and generates multiple candidate path sets each of which includes a vehicle path during the target time window for each vehicle. The vehicle path is conflict-free with the other vehicle paths and the obstacle paths. The pathfinding apparatus evaluates the candidate path sets through a heuristic search of continuations of the vehicles paths in the candidate path sets, selects one of the candidate path sets based their evaluations, and outputs the selected candidate path set.

A method for training a migration scene-based trajectory prediction model is provided, a first trajectory prediction model and a plurality of candidate training samples are obtained; for any candidate training sample, a reference value corresponding to the candidate training sample is determined according to at least one of a trajectory feature corresponding to the candidate training sample or a prediction result of the first trajectory prediction model for the candidate training sample; target training samples are selected from the plurality of candidate training samples according to the reference values corresponding to the plurality of candidate training samples; and the first trajectory prediction model is trained according to the target training samples, to obtain a second trajectory prediction model, where the second trajectory prediction model is configured to predict traveling trajectories of obstacles in a migration scene.

A method for training a migration scene-based trajectory prediction model is provided, a first trajectory prediction model and a plurality of candidate training samples are obtained; for any candidate training sample, a reference value corresponding to the candidate training sample is determined according to at least one of a trajectory feature corresponding to the candidate training sample or a prediction result of the first trajectory prediction model for the candidate training sample; target training samples are selected from the plurality of candidate training samples according to the reference values corresponding to the plurality of candidate training samples; and the first trajectory prediction model is trained according to the target training samples, to obtain a second trajectory prediction model, where the second trajectory prediction model is configured to predict traveling trajectories of obstacles in a migration scene.

An indoor mobile industrial robot system is configured to provide a weight to a detected object within an operating environment, where the weight relates to how static the feature is. The indoor mobile industrial robot system includes a mechanism configured to translate reflected light energy and positional information into a set of data points representing the detected object having at least one of Cartesian and/or polar coordinates, and an intensity. If any discrete data point within the set of data points representing the detected object has an intensity at or above a defined threshold the entire set of data points is converted into a weight and potentially classified representing a static feature, otherwise such set of data points is classified as representing a dynamic feature having a lower weight.

An autonomous mobile robot and an operating method thereof are provided. The autonomous mobile robot includes a movement module, a detection module, a control module and an interaction module. The control module includes a determination unit and a navigation unit. The determination unit determines whether there is an obstacle near or on a predetermined path of the autonomous mobile robot according to the environment information. When the obstacle is on the predetermined path, the navigation unit decides an obstacle avoidance strategy according to the environment information and the type of the obstacle. The obstacle avoidance strategy at least includes moving along a side path, stopping aside to yield, moving backward and stopping at a yielding point to yield, and detouring. When the obstacle is near or on the predetermined path, the interaction module performs an interaction action according to the obstacle avoidance strategy and the type of the obstacle.

An autonomous mobile robot and an operating method thereof are provided. The autonomous mobile robot includes a movement module, a detection module, a control module and an interaction module. The control module includes a determination unit and a navigation unit. The determination unit determines whether there is an obstacle near or on a predetermined path of the autonomous mobile robot according to the environment information. When the obstacle is on the predetermined path, the navigation unit decides an obstacle avoidance strategy according to the environment information and the type of the obstacle. The obstacle avoidance strategy at least includes moving along a side path, stopping aside to yield, moving backward and stopping at a yielding point to yield, and detouring. When the obstacle is near or on the predetermined path, the interaction module performs an interaction action according to the obstacle avoidance strategy and the type of the obstacle.

The mobile object control device includes: a prescribed path information acquisition unit which acquires prescribed path information; an obstacle information acquisition unit which acquires obstacle information; a position orientation information acquisition unit which acquires position orientation information; and a traveling path determination unit which judges whether or not a vehicle is traveling on a prescribed path, and judges whether or not there is a possibility that the vehicle collides with an obstacle, and then, if it is judged that there is the possibility of the collision, determines whether or not to cause the vehicle to travel on a prescribed-path-outside traveling path.

The mobile object control device includes: a prescribed path information acquisition unit which acquires prescribed path information; an obstacle information acquisition unit which acquires obstacle information; a position orientation information acquisition unit which acquires position orientation information; and a traveling path determination unit which judges whether or not a vehicle is traveling on a prescribed path, and judges whether or not there is a possibility that the vehicle collides with an obstacle, and then, if it is judged that there is the possibility of the collision, determines whether or not to cause the vehicle to travel on a prescribed-path-outside traveling path.