RSA-RRT: A path planning algorithm based on restricted sampling area

Abstract

The rapidly-exploring random tree (RRT) algorithm has a wide range of applications in the field of path planning. However, conventional RRT algorithm suffers from low planning efficiency and long path length, making it unable to handle complex environments. In response to the above problems, this paper proposes an improved RRT algorithm based on restricted sampling area (RSA-RRT). Firstly, to address the problem of low efficiency, a restricted sampling area strategy is proposed. By dynamically restricting the sampling area, the number of invalid sampling points is reduced, thus improving planning efficiency. Then, for the path planning problem in narrow areas, a fixed-angle sampling strategy is proposed, which improves the planning efficiency in narrow areas by conducting larger step size sampling with a fixed angle. Finally, a multi-triangle optimization strategy is proposed to address the problem of longer and more tortuous paths. The effectiveness of RSA-RRT algorithm is verified through improved strategy performance verification and ablation experiments. Comparing with other algorithms in different environments, the results show that RSA-RRT algorithm can obtain shorter paths while taking less time, effectively balancing the path quality and planning speed, and it can be applied in complex real-world environments.