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Improved PRM algorithm based on dynamic partitioning and adaptive sampling
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AI
DOI：10.1016/j.birob.2026.100283.png)
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Abstract
En 中文
The Probabilistic Roadmap (PRM) algorithm has been widely employed in robotic manipulator path planning tasks due to its rapid exploration capabilities, particularly in high-dimensional configuration spaces with complex kinematic and environmental constraints. However, the efficiency of PRM is inherently constrained by the distribution of sampling points. In scenarios involving narrow passages, the sparsity of samples within such regions may significantly increase the likelihood of planning failure. In view of this, this paper proposes an improved PRM algorithm that is suitable for narrow channels with obstacles and can significantly improve the efficiency of path planning. First, a non-uniform partitioning strategy based on obstacle density is proposed to dynamically divide the sampling area to reduce the connection of redundant edges. Second, to address the sampling failure often encountered in narrow passages due to insufficient sample points, a weighted sampling adjustment strategy is proposed, which adaptively modifies the sampling density between narrow and open regions based on a comprehensive distance metric. Third, an adaptive variable step-size strategy is developed to dynamically adjust the connection steps between obstacle boundaries and open areas, further enhancing roadmap connectivity. By integrating the aforementioned strategies, the improved PRM algorithm proposed was applied in both two-dimensional and three-dimensional environments. The simulation results demonstrate that the method is capable of finding feasible paths in complex scenarios. Compared to the Lazy PRM and the OBPRM algorithms, the proposed approach achieves reductions of approximately 8.77% and 7.44% in path length and 9.00% and 5.74% in planning time, respectively. Finally, its effectiveness and superiority in robotic manipulator path planning were further validated through application to a 7-DOF manipulator. (c) 2026 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords:
Path planning
Probabilistic roadmap
Partitioning strategy
Adaptive sampling
Journal
B
IF:
5.4
Papers:
28
Citations:
0