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.

翻译：本文提出了一种新的渐近最优采样运动规划算法——双向引导启发式树（BIGIT*）。该算法结合了双向启发式搜索中"双向汇合"特性的优势、一种新的惰性策略以及均匀代价搜索，在隐式随机几何图（RGG）上构建了一个隐式双向初步运动树。该方法能有效压缩启发式搜索区域，形成一个既满足可采纳性又具备高精度的双向引导启发式机制。该启发式机制随后被用于引导双向启发式搜索，以在给定的RGG上寻找有效路径。实验表明，在$\mathbb{R}^{16}$空间的模拟抽象问题上，BIGIT*在初始解发现速度与收敛至最优解两方面均优于现有的启发式采样运动规划器。校园环境下的实际无人机航迹规划任务也验证了我们的结论。