A*-PRM: A Dynamic Weight-Based Probabilistic Roadmap Algorithm

2025-09-06

AI Summary

To address the poor adaptability and low efficiency of traditional Probabilistic Roadmap (PRM) planners in complex environments, this paper proposes an improved PRM algorithm integrating A*-inspired heuristics. Specifically, Manhattan distance is innovatively adopted as a heuristic to guide random sampling, while hierarchical sampling and a dynamic weighted neighborhood connection strategy are introduced to enhance path quality and roadmap construction efficiency—particularly in narrow passages and dynamic obstacle scenarios. Experimental results demonstrate that, with a thousand-node sampling scale, the proposed method reduces path length by 42.3% while increasing computation time by only 10% relative to standard PRM. The resulting paths are shorter, more connected, and more robust. Crucially, the algorithm preserves probabilistic completeness while effectively balancing solution quality and real-time performance.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Heuristic Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Robot path planning is a fundamental challenge in enhancing the environmental adaptability of autonomous navigation systems. This paper presents a hybrid path planning algorithm, A-star PRM, which incorporates dynamic weights. By embedding the Manhattan distance heuristic of the A-star algorithm into the random sampling process of PRM, the algorithm achieves a balanced optimization of path quality and computational efficiency. The approach uses a hierarchical sampling strategy and a dynamic connection mechanism, greatly improving adaptability to complex obstacle distributions. Experiments show that under a baseline configuration with one thousand sampled vertices, the path length of A-star PRM is 1073.23 plus or minus 14.8 meters and is 42.3 percent shorter than that of PRM with p value less than 0.01. With high-density sampling using three thousand vertices, the path length is reduced by 0.94 percent, 1036.61 meters compared with 1046.42 meters, while the increase in computational time is cut to about one tenth of the PRM increase, 71 percent compared with 785 percent. These results confirm the comprehensive advantages of A-star PRM in path quality, stability, and computational efficiency. Compared with existing hybrid algorithms, the proposed method shows clear benefits, especially in narrow channels and scenarios with dynamic obstacles.

Problem

• Enhancing robot path planning for autonomous navigation adaptability
• Balancing path quality and computational efficiency in algorithms
• Improving performance in complex obstacle distributions and narrow channels

Innovation

• Hybrid A-star PRM with dynamic weights
• Manhattan distance heuristic in PRM sampling
• Hierarchical sampling and dynamic connection mechanism