Abstract—Path planning remains a critical research area in mobile robotics, yet current approaches often suffer from suboptimal path quality, limited sampling efficiency, and inadequate adaptability across diverse operational scenarios. To address these issues, this paper proposes an improved algorithm combining Artificial Potential Field (APF) and Restricted Path Time (RRT*) approaches. This algorithm employs an optimization model that combines dynamic sampling with potential field guidance, constructing a two-stage dynamic sampling mechanism. During sampling, candidate nodes with Gaussian noise are generated along the resultant force direction. Finally, path cost comparison and parent node reselection are performed within the dynamic optimization radius to ensure asymptotic optimality of the path. Experimental results show that in complex maps, path length is reduced by 33.41% and 26.64%, respectively, and planning time is reduced by 21.36% and 86.32%, respectively; in narrow passages, path length is reduced by 49.6% and 49.8%, respectively. The results confirm the effectiveness of the two-stage dynamic sampling mechanism, which not only preserves the probabilistic completeness of the RRT* algorithm but also adaptively adjusts the sampling strategy, improving both planning length and time.

Keywords—artificial potential field, Restricted Path Time (RRT*) algorithm, path planning, dynamic sampling

Cite: Yongyang Zheng, Monsak Pimsarn, Varesa Chuwattanakul, Suriya Chokphoemphun, and Smith Eiamsa-ard, "Path Optimization Using an Improved APF-RRT* Algorithm," International Journal of Mechanical Engineering and Robotics Research, Vol. 15, No. 1, pp. 93-101, 2026. doi: 10.18178/ijmerr.15.1.93-101

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