Method and device for deterministic sampling-based motion planning
AssigneeRobert Bosch GmbH
InventorsLeonard Bruns, Kai Oliver Arras, Luigi Palmieri
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Abstract
A computer-implemented method for planning an optimized motion path. The optimized motion path is determined applying a sampling-based motion-planning algorithm depending on a sample node set including sample nodes. The sample nodes in the sample node set are deterministically selected from a configuration node set including all obstacle free nodes. The sample nodes are selected to optimize a given dispersion criterion. The dispersion criterion selects the sample nodes so that the largest uncovered area/space within the configuration node set is as small as possible.
Claims8
A computer-implemented method for planning an optimized motion path, the method comprising the following steps:
- determining the optimized motion path is determined by applying a sampling-based motion-planning algorithm depending on a sample node set including sample nodes, wherein the sample nodes in the sample node set are deterministically selected from a configuration node set including only obstacle free nodes, and the sample nodes are selected to optimize a given dispersion criterion, wherein the given dispersion criterion selects the sample nodes so that a largest uncovered area/space within the configuration node set is minimized, wherein the sample nodes are chosen based on a dispersion matrix including dispersion values for each of the obstacle free nodes of the configuration node set, wherein the obstacle free nodes with the highest dispersion value are selected as the sample nodes.
A method for operating an agent, comprising the following steps:
- determining an optimized motion path is determined by applying a sampling-based motion-planning algorithm depending on a sample node set including sample nodes, wherein the sample nodes in the sample node set are deterministically selected from a configuration node set including all obstacle free nodes, and the sample nodes are selected to optimize a given dispersion criterion, wherein the given dispersion criterion selects the sample nodes so that a largest uncovered area/space within the configuration node set is minimized;
- and
- operating the agent using the optimized motion path, wherein the sample nodes are chosen based on a dispersion matrix including dispersion values for each of the obstacle free nodes of the configuration node set, wherein the obstacle free nodes with the highest dispersion value are selected as the sample nodes.
A device for planning an optimized motion path, wherein the device is configured to:
- determine the optimized motion path by applying a sampling-based motion-planning algorithm depending on a sample node set including sample nodes, wherein the sample nodes in the sample node set are deterministically selected from a configuration node set including all obstacle free nodes;
- and
- select the sample nodes by optimizing a dispersion criterion, wherein the dispersion criterion selects the sample nodes so that a largest uncovered area/space within the configuration node set is minimized, wherein the sample nodes are chosen based on a dispersion matrix including dispersion values for each of the obstacle free nodes of the configuration node set, wherein the obstacle free nodes with the highest dispersion value are selected as the sample nodes.
A non-transitory computer readable medium on which is stored a computer program for planning an optimized motion path, the computer program, when executed by a computer, causing the computer to perform the following steps:
- determining the optimized motion path is determined by applying a sampling-based motion-planning algorithm depending on a sample node set including sample nodes, wherein the sample nodes in the sample node set are deterministically selected from a configuration node set including all obstacle free nodes, and the sample nodes are selected to optimize a given dispersion criterion, wherein the given dispersion criterion selects the sample nodes so that a largest uncovered area/space within the configuration node set is minimized, wherein the sample nodes are chosen based on a dispersion matrix including dispersion values for each of the obstacle free nodes of the configuration node set, wherein the obstacle free nodes with the highest dispersion value are selected as the sample nodes.
Dependencies are inferred from the claim text.