用速度障碍法提升MCTS，让机器人在动态环境中安全高效导航
Monte Carlo Tree Search with Velocity Obstacles for safe and efficient motion planning in dynamic environments
- 结合MCTS与速度障碍法，仅需障碍物位置和最高速度即可规划
- 40个随机移动障碍物下碰撞率更低，计算效率显著提升
- 适合需要实时避障的机器人场景，如人群中的自主导航
在线运动规划在密集动态环境（如人群）中对智能机器人极具挑战。本文提出一种新型最优且安全的在线运动规划方法，仅需障碍物当前位置及其最大速度，无需轨迹或动力学模型信息。该方法融合蒙特卡洛树搜索（MCTS）与速度障碍（VO），通过模拟实现最优路径选择，并利用VO确保避障安全性。在含墙及最多40个随机速度与方向移动障碍物的复杂仿真环境中进行实验。消融研究证实，VO显著提升了MCTS的效率，使搜索树能更安全、更优地选择动作。相比非线性模型预测控制（NMPC）等前沿规划器，本方法在碰撞率、计算开销和任务性能上均表现更优。
原文摘要 · Abstract (English)
Online motion planning is a challenging problem for intelligent robots moving in dense environments with dynamic obstacles, e.g., crowds. In this work, we propose a novel approach for optimal and safe online motion planning with minimal information about dynamic obstacles. Specifically, our approach requires only the current position of the obstacles and their maximum speed, but it does not need any information about their exact trajectories or dynamic model. The proposed methodology combines Monte Carlo Tree Search (MCTS), for online optimal planning via model simulations, with Velocity Obstacles (VO), for obstacle avoidance. We perform experiments in a cluttered simulated environment with walls, and up to 40 dynamic obstacles moving with random velocities and directions. With an ablation study, we show the key contribution of VO in scaling up the efficiency of MCTS, selecting the safest and most rewarding actions in the tree of simulations. Moreover, we show the superiority of our methodology with respect to state-of-the-art planners, including Non-linear Model Predictive Control (NMPC), in terms of improved collision rate, computational and task performance.
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