Improved probabilistic roadmap method based on obstacle distance sampling

Sampling-based algorithms are widely used to solve path planning problems, and PRM is one of the most popular ones. Random Sampling PRM is the classic version of the PRM series, which has the advantages of simplicity and robustness, but also suffers from low success rate, long collision detection time and low path quality in narrow environments. To address these issues, an improved PRM algorithm based on obstacle distance sampling is proposed. A sampling strategy based on obstacle distance is used in this algorithm, so that more sampling points appear in narrow areas, thereby improving the success rate of the algorithm in finding paths in narrow areas. In addition, a collision detection method based on obstacle distance is proposed in this paper, which reduces the time required for collision detection and improves the efficiency of the algorithm. Further, in order to improve the quality of the path, the proposed algorithm uses triangle inequality and dichotomy to optimize the path. Finally, the proposed algorithm is compared with Random Sampling PRM, MSAPRM and Gaussian PRM through simulation experiments, and it is verified that the algorithm has certain advantages in the success rate, time and path length of finding paths in narrow environments.