新算法让机器人更快找到安全优路径，尤其擅长复杂操作场景。
Just in time Informed Trees: Manipulability-Aware Asymptotically Optimized Motion Planning
- 动态调整采样与边连接，加速初始路径发现
- 结合操作性能优化，降低奇异点与碰撞风险
- 适用于机械臂多维运动规划，实测效果领先
在高维机器人路径规划中，传统采样方法在复杂多障碍环境中难以高效找到可行且最优的路径。这一挑战在机械臂任务中尤为突出，因存在运动学奇点和自碰撞风险，进一步影响运动效率与安全性。为此，本文提出即时知情树（JIT*）算法，基于努力知情树（EIT*）改进，包含两个核心模块：即时模块通过动态优化边连接与瓶颈区域采样密度，加快初始路径发现；运动性能模块通过动态切换策略，平衡操作能力与轨迹成本，提升运动控制并降低奇点风险。对比实验表明，JIT* 在 $\b{R}^4$ 至 $\b{R}^{16}$ 维空间中均显著优于传统采样规划器，在单臂与双臂操作任务中表现优异，相关实验视频可访问 https://youtu.be/nL1BMHpMR7c。
原文摘要 · Abstract (English)
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.
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