The Improved Informed‐RRT* Algorithm, Which Optimizes the Sampling Strategy and Integrates an Artificial Potential Field

摘要

ABSTRACT This article presents an algorithm for mobile robots that enables autonomous navigation in complex environments. Currently, achieving autonomous navigation for ground mobile robots in intricate and unstructured settings continues to pose significant challenges. To address issues such as dispersed sampling points, low sampling efficiency, and excessive path waypoints encountered in traditional Rapidly‐Exploring Random Trees (RRT) algorithms, this paper proposes an Optimized Sampling Strategy and Artificial Potential Fields Fusion‐based Informed‐RRT* global path planning algorithm. Initially, sampling angles are determined based on the position of the target point, and the workspace is partitioned into regions with varying levels of importance. Subsequently, an improved artificial potential fields algorithm is integrated to further refine the resultant forces acting on the nodes. Finally, cubic spline interpolation is utilized to smooth the generated path. The proposed algorithm was validated through simulation and experimental studies conducted on simple, narrow, and complex maps. The results demonstrated significant reductions in search time, path length, and the number of path waypoints compared to conventional A*, Dijkstra, RRT, RRT*, and Informed‐RRT algorithms. Additionally, the smoothness of the generated paths was notably improved. In the virtual maze experiments and real‐world environment tests, the improved algorithm presented in this paper demonstrates significant advantages over five other algorithms.