Dual-Replanning Tree: Fast Multi-Query Path Planning in Dynamic Environments

This paper presents Dual-Replanning Tree (DRT), a real-time multi-query path planning algorithm that integrates local and global path generation, multi-query planning, and dynamic obstacle avoidance. Existing algorithms, such as FAT, which fix the tree root at the destination, exhibit high replanning efficiency but struggle with multi-query path planning. On the other hand, algorithms like RT-RRT*, which maintain the tree root near the robot, are advantageous for multi-query path planning but are significantly affected by newly detected obstacles, limiting their performance in dynamic environments. Our method innovatively improves the replanning strategy of algorithms that keep the tree root near the robot by introducing a reference path mechanism, enabling efficient node rewiring and expansion. This reference path is obtained through local small-scale rewiring, resulting in low computational overhead. Based on this reference path, the proposed algorithm can achieve higher replanning efficiency than algorithms that fix the tree root at the destination while retaining the dynamic adjustment of the tree root to adapt to multi-query path planning. Experimental results demonstrate that under various environmental conditions, the DRT algorithm outperforms the FAT algorithm in two key metrics: execution cost and arrival time.