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.

翻译：本文介绍双向紧致知情树（BTIT*），一种渐近最优的基于采样的运动学-动力学运动规划算法。该算法融合了任意时刻双向启发式搜索（Bi-HS），并确保“中间相遇”性质（MMP）及最优性（MM-最优性）。BTIT*是首个利用评估高效的终止条件、在批量采样运动规划中实现实时早期终止的任意时刻MEET类算法。实验表明，在4维双积分器模型和10维线性化四旋翼两个运动学-动力学基准测试中，BTIT*相比典型非惰性知情批量规划器，在首次求解时间与收敛速度上均显著提升。源代码已公开。