APF-IBRRT*: A Global Path Planning Algorithm for Obstacle Avoidance Robots With Improved Iterative Search Efficiency

The Rapidly-exploring Random Tree (RRT) algorithm based on random sampling has been widely applied in global path planning for robots due to its collision-free and asymptotically optimal solution capabilities. However, in narrow and dynamic indoor environments, RRT and its optimized algorithms suffer from drawbacks such as multiple iterations and long planning times. While the Artificial Potential Field (APF) method can reduce the number of iterations and make the planned path smoother, it still faces issues such as local optima or inability to reach the target. To address these issues, this paper proposes the APF-IBRRT* algorithm, which combines the advantages of APF and B-RRT*. The algorithm first overcomes the local optimum problem of APF through the optimization of potential field factors and the introduction of virtual obstacles. Then, it designs a target threshold and proposes an adaptive step size search iteration strategy to address the issue of ineffective tree searches. Finally, the APF-IBRRT* algorithm is compared with B-RRT*, VPF-RRT*, and the classic A* algorithm. Simulation and actual experimental results demonstrate that the APF-IBRRT* algorithm not only ensures optimization of path length but also achieves a performance improvement of over 20% in planning time and iteration efficiency compared to the benchmark algorithms.