Tree-Based Grafting Approach for Bidirectional Motion Planning with Local Subsets Optimization

TL;DR

This paper introduces G3T*, a novel bidirectional motion planning algorithm that grafts invalid edges and dynamically optimizes local subsets to achieve faster convergence and lower costs, outperforming existing planners.

Contribution

G3T* innovatively combines grafting invalid edges with greedy local subset optimization and adaptive sampling to improve bidirectional motion planning efficiency and asymptotic optimality.

Findings

G3T* converges faster than existing planners across multiple dimensions.

It achieves lower solution costs in benchmark tests.

Demonstrated effectiveness in real-world robotic scenarios.

Abstract

Bidirectional motion planning often reduces planning time compared to its unidirectional counterparts. It requires connecting the forward and reverse search trees to form a continuous path. However, this process could fail and restart the asymmetric bidirectional search due to the limitations of lazy-reverse search. To address this challenge, we propose Greedy GuILD Grafting Trees (G3T*), a novel path planner that grafts invalid edge connections at both ends to re-establish tree-based connectivity, enabling rapid path convergence. G3T* employs a greedy approach using the minimum Lebesgue measure of guided incremental local densification (GuILD) subsets to optimize paths efficiently. Furthermore, G3T* dynamically adjusts the sampling distribution between the informed set and GuILD subsets based on historical and current cost improvements, ensuring asymptotic optimality. These features enhance the forward search's growth towards the reverse tree, achieving faster convergence and lower solution costs. Benchmark experiments across dimensions from R^2 to R^8 and real-world robotic evaluations demonstrate G3T*'s superior performance compared to existing single-query sampling-based planners. A video showcasing our experimental results is available at: https://youtu.be/3mfCRL5SQIU