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Topological discrimination and lazy collision-Based multistage optimization of probabilistic roadmap algorithm for path planning

Published online by Cambridge University Press:  16 May 2025

Wenbin Gong
Affiliation:
Chongqing Key Laboratory of Geological Environmental Monitoring and Disaster Early Warning in the Three Gorges Reservoir Area, Chongqing Three Gorges University, Chongqing 404120, China
Hua Luo*
Affiliation:
Internet of Things and Intelligent Control Technology Chongqing Engineering Research Center, Chongqing Three Gorges University, Chongqing, 404120, China
Yu Su
Affiliation:
Chongqing Key Laboratory of Geological Environmental Monitoring and Disaster Early Warning in the Three Gorges Reservoir Area, Chongqing Three Gorges University, Chongqing 404120, China
Yu Gu
Affiliation:
Chongqing Key Laboratory of Geological Environmental Monitoring and Disaster Early Warning in the Three Gorges Reservoir Area, Chongqing Three Gorges University, Chongqing 404120, China
HongHao Chen
Affiliation:
Chongqing Key Laboratory of Geological Environmental Monitoring and Disaster Early Warning in the Three Gorges Reservoir Area, Chongqing Three Gorges University, Chongqing 404120, China
Yutao Jiang
Affiliation:
Chongqing Key Laboratory of Geological Environmental Monitoring and Disaster Early Warning in the Three Gorges Reservoir Area, Chongqing Three Gorges University, Chongqing 404120, China
Tangju Yuan
Affiliation:
Chongqing Key Laboratory of Geological Environmental Monitoring and Disaster Early Warning in the Three Gorges Reservoir Area, Chongqing Three Gorges University, Chongqing 404120, China
Hongbing Li
Affiliation:
Internet of Things and Intelligent Control Technology Chongqing Engineering Research Center, Chongqing Three Gorges University, Chongqing, 404120, China Chongqing Municipal Key Laboratory of Intelligent Information Processing and Control, Chongqing Three Gorges University, Chongqing 404120, China
*
Corresponding author: Hua Luo; Email: sxxylhb@163.com

Abstract

The selection of random sampling points is crucial for the path quality generated by probabilistic roadmap (PRM) algorithm. Increasing the number of sampling points can enhance path quality. However, it may also lead to extended convergence time and reduced computational efficiency. Therefore, an improved probabilistic roadmap algorithm (TL-PRM) is proposed based on topological discrimination and lazy collision. TL-PRM algorithm first generates a circular grid area among start and goal points. Then, it constructs topological nodes. Subsequently, elliptical sampling areas are created between each pair of adjacent topological nodes. Random sampling points are generated within these areas. These sampling points are interconnected using a layer connection strategy. An initial path is generated using a delayed collision strategy. The path is then adjusted by modifying the nodes on the convex outer edges to avoid obstacles. Finally, a reconnection strategy is employed to optimize the path. This reduces the number of path waypoints. In dynamic environments, TL-PRM algorithm employs pose adjustment strategies for semi-static and dynamic obstacles. It can use either the same or opposite pose adjustments to avoid dynamic obstacles. Experimental results indicate that TL-PRM algorithm reduces the average number of generated sampling points by 70.9% and average computation time by 62.1% compared with PRM* and PRM-Astar algorithms. In winding and narrow passage maps, TL-PRM algorithm significantly decreases the number of sampling points and shortens convergence time. In dynamic environments, the algorithm can adjust its pose orientation in real time. This allows it to safely reach the goal point. TL-PRM algorithm provides an effective solution for reducing the generation of sampling points in PRM algorithm.

Information

Type
Research Article
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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