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Improved Path Planning and Controller Design Based on PRM
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AI
DOI：10.1109/ACCESS.2025.3548326.png)
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Abstract
En 中文
This paper introduces the application of the Probabilistic Road Map (PRM) method in path planning and designs a path tracking controller based on the Lyapunov function. To enhance the smoothness of the paths generated by the PRM, B & eacute;zier curves are employed. Additionally, to address the path detection challenges encountered by the PRM algorithm in complex environments, this paper introduces normal distribution sampling and adaptive cost factors. By dynamically adjusting the PRM sampling points, the success rate of path detection is improved. The Improved PRM (IPRM) algorithm demonstrates better performance in terms of shorter path generation compared to the original PRM, RRT* and Bi-RRT algorithms. A path tracking controller is designed by integrating the curvature polynomial of the exploration path with the Lyapunov function. In complex mapping scenarios, dynamic obstacle avoidance strategies are incorporated to prevent collisions between the robot and obstacles. Experimental results indicate that the proposed controller achieves faster convergence, smaller tracking errors, and greater stability compared to traditional PD and PI controllers.
Keywords:
Path planning
Robots
Probabilistic logic
Costs
Mobile robots
Lyapunov methods
Heuristic algorithms
Gaussian distribution
Service robots
Mathematical models
PRM
path planning
adaptive cost factor
Lyapunov function
path tracking