In high-dimensional robotic path planning, traditional sampling-based methods often struggle to efficiently identify both feasible and optimal paths in complex, multi-obstacle environments. This challenge is intensified in robotic manipulators, where the risk of kinematic singularities and self-collisions further complicates motion efficiency and safety. To address these issues, we introduce the Just-in-Time Informed Trees (JIT*) algorithm, an enhancement over Effort Informed Trees (EIT*), designed to improve path planning through two core modules: the Just-in-Time module and the Motion Performance module. The Just-in-Time module includes "Just-in-Time Edge," which dynamically refines edge connectivity, and "Just-in-Time Sample," which adjusts sampling density in bottleneck areas to enable faster initial path discovery. The Motion Performance module balances manipulability and trajectory cost through dynamic switching, optimizing motion control while reducing the risk of singularities. Comparative analysis shows that JIT* consistently outperforms traditional sampling-based planners across $\mathbb{R}^4$ to $\mathbb{R}^{16}$ dimensions. Its effectiveness is further demonstrated in single-arm and dual-arm manipulation tasks, with experimental results available in a video at https://youtu.be/nL1BMHpMR7c.

翻译：在高维机器人路径规划中，传统的基于采样的方法往往难以在复杂多障碍环境中高效识别出既可行又最优的路径。这一挑战在机器人机械臂中尤为突出，因为运动学奇点和自碰撞的风险进一步加剧了运动效率与安全性的复杂性。为解决这些问题，我们提出了适时知情树算法，该算法是对努力知情树的增强，旨在通过两个核心模块——适时模块和运动性能模块——来改进路径规划。适时模块包含“适时边”和“适时采样”：“适时边”动态优化边的连接性，“适时采样”则在瓶颈区域调整采样密度，以实现更快的初始路径发现。运动性能模块通过动态切换来平衡可操纵性与轨迹成本，在优化运动控制的同时降低奇点风险。对比分析表明，在 $\mathbb{R}^4$ 到 $\mathbb{R}^{16}$ 维度上，JIT* 算法始终优于传统的基于采样的规划器。其在单臂和双臂操作任务中的有效性得到了进一步验证，实验结果可见于视频 https://youtu.be/nL1BMHpMR7c。