提出新型路径规划算法，显著提升复杂环境下的搜索效率与最优性。
Asymptotically Optimal Sampling-Based Path Planning Using Bidirectional Guidance Heuristic
- 采用双向引导与懒惰策略构建隐式双向搜索树
- 在16维空间中初始解生成速度更快，收敛到最优解更高效
- 适用于无人机等实际飞行路径规划任务
本文提出一种新的渐近最优采样路径规划算法——双向引导知情树（BIGIT*）。该算法结合双向启发式搜索的‘中途相遇’特性、懒惰策略和统一代价搜索，在隐式随机几何图（RGG）上构建隐式双向预运动树，有效缩小知情搜索区域，形成可采纳且准确的双向引导启发式。该启发式用于指导在给定RGG上的双向启发式搜索以找到有效路径。实验表明，BIGIT* 在模拟的16维抽象问题中，不仅更快获得初始解，且更高效收敛至最优解；校园环境中无人机飞行路径规划任务也验证了其有效性。
原文摘要 · Abstract (English)
This paper introduces Bidirectional Guidance Informed Trees (BIGIT*),~a new asymptotically optimal sampling-based motion planning algorithm. Capitalizing on the strengths of \emph{meet-in-the-middle} property in bidirectional heuristic search with a new lazy strategy, and uniform-cost search, BIGIT* constructs an implicitly bidirectional preliminary motion tree on an implicit random geometric graph (RGG). This efficiently tightens the informed search region, serving as an admissible and accurate bidirectional guidance heuristic. This heuristic is subsequently utilized to guide a bidirectional heuristic search in finding a valid path on the given RGG. Experiments show that BIGIT* outperforms the existing informed sampling-based motion planners both in faster finding an initial solution and converging to the optimum on simulated abstract problems in $\mathbb{R}^{16}$. Practical drone flight path planning tasks across a campus also verify our results.
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