Optimization of Probabilistic Roadmap Based on Two-Dimensional Static Environment

To address the problems of slow planning speed and too many sharp turns in the planned route, this paper focuses on the optimization of the probabilistic roadmap by searching the neighboring nodes in the composition stage, improving its search efficiency using K-dimensional Tree (KD-TREE), smoothing the planned paths, and ensuring the safety of the planned route by expanding the map obstacles. To test the performance of the improved probabilistic roadmap algorithm, it is compared with the traditional PRM algorithm and the PRM based on the common K-Nearest Neighbor (KNN) algorithm. The simulation results show that the optimized algorithm has a significant improvement in the planning time and the final planned path is a smooth path without inflection points, which is more conducive to the actual walking of the mobile robot. The study has a wide range of applications.