Robot path planning in narrow passages based on improved PRM method

Probabilistic roadmap (PRM) method has been shown to perform well in robot path planning. However, its performance degrades when the robot needs to pass through narrow passages. To solve this problem, an improved PRM method with hybrid uniform sampling and Gaussian sampling is proposed in this paper. With the proposed method, the robot can improve the success rate and efficiency of path planning in narrow passages. Firstly, the narrow-passage-aware Gaussian sampling method is developed for narrow passages. Combining uniform sampling globally, the new sampling strategy can increase the sampling density at the narrow passages and reduce the redundancy of the samples in the wide-open regions. Then, we propose to use density-based clustering method to achieve accurate identification of narrow channels by removing the noise points. Next, graph search algorithm is used to search the shortest path from the start point to the goal point. Finally, simulations are carried out to evaluate the validity of the proposed method. Results show that the improved PRM method is more effective for path planning with narrow passages.