提出一种可随时终止的高效运动规划算法，比现有方法更快找到可行路径。
Optimal Kinodynamic Motion Planning Through Anytime Bidirectional Heuristic Search with Tight Termination Condition
- 采用双向启发式搜索与紧致终止条件，实现路径最优性
- 在4维和10维模型上首次解出路径速度提升显著
- 适合需要快速响应的无人机等实时运动规划场景
本文提出一种渐进最优的基于采样的动力学运动规划算法——双向紧致知情树（BTIT*），融合了任意时间双向启发式搜索（Bi-HS），确保了‘中途相遇’性质（MMP）与最优性（MM-optimality）。BTIT* 是首个利用高效可评估终止条件、支持批量采样中在线提前终止的任意时间 MEET 类算法。实验表明，在两个动力学基准测试中——4维双积分器模型与10维线性化四旋翼模型——该算法在首次解出路径的时间上显著更优，且收敛性能优于代表性非懒惰知情批处理规划器。源代码已公开。
原文摘要 · Abstract (English)
This paper introduces Bidirectional Tight Informed Trees (BTIT*), an asymptotically optimal kinodynamic sampling-based motion planning algorithm that integrates an anytime bidirectional heuristic search (Bi-HS) and ensures the \emph{meet-in-the-middle} property (MMP) and optimality (MM-optimality). BTIT* is the first anytime MEET-style algorithm to utilize termination conditions that are efficient to evaluate and enable early termination \emph{on-the-fly} in batch-wise sampling-based motion planning. Experiments show that BTIT* achieves strongly faster time-to-first-solution and improved convergence than representative \emph{non-lazy} informed batch planners on two kinodynamic benchmarks: a 4D double-integrator model and a 10D linearized Quadrotor. The source code is available here.
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