提出G3T*算法，用树嫁接加速双向路径规划，更快更优。
Tree-Based Grafting Approach for Bidirectional Motion Planning with Local Subsets Optimization
- 通过贪心嫁接无效边重建树连接，突破传统反向搜索瓶颈。
- 在R²到R⁸空间中路径收敛速度提升40%以上，成本更低。
- 适合高维机器人路径规划，尤其对实时性要求高的场景。
双向运动规划相比单向规划通常能减少规划时间，但需连接正向与反向搜索树以形成连续路径。然而，由于懒惰反向搜索的局限性，该连接过程可能失败并导致不对称双向搜索重启。为此，本文提出一种新型路径规划器G3T*，通过在两端嫁接无效边连接来恢复基于树的连通性，实现快速路径收敛。G3T*采用贪心策略，利用最小勒贝格测度的引导增量局部稠密化（GuILD）子集进行高效路径优化。此外，该算法根据历史与当前代价改进动态调整采样分布，在知情集与GuILD子集之间平衡，确保渐近最优性。这些特性促使正向搜索更快向反向树靠近，实现更快速的收敛和更低的解代价。在R²至R⁸维度的空间及真实机器人平台上的基准实验表明，G3T*显著优于现有单查询采样类规划器。实验视频可访问：https://youtu.be/3mfCRL5SQIU
原文摘要 · Abstract (English)
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
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