Research Article
Optimization of Probabilistic Roadmap Based on Two-Dimensional Static Environment
@INPROCEEDINGS{10.1007/978-3-031-50580-5_35, author={Binpeng Wang and Houqin Huang and Lin Sun and Chao Feng}, title={Optimization of Probabilistic Roadmap Based on Two-Dimensional Static Environment}, proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part IV}, proceedings_a={ICMTEL PART 4}, year={2024}, month={2}, keywords={Path planning Probabilistic Roadmap K-dimensional Tree K-Nearest Neighbor Bezier Curve}, doi={10.1007/978-3-031-50580-5_35} }- Binpeng Wang
Houqin Huang
Lin Sun
Chao Feng
Year: 2024
Optimization of Probabilistic Roadmap Based on Two-Dimensional Static Environment
ICMTEL PART 4
Springer
DOI: 10.1007/978-3-031-50580-5_35
Abstract
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.
