Monte Carlo Tree Search with Velocity Obstacles for safe and efficient motion planning in dynamic environments

TL;DR

This paper introduces a novel online motion planning method combining Monte Carlo Tree Search and Velocity Obstacles, enabling safe and efficient navigation for robots in dynamic, cluttered environments with minimal obstacle information.

Contribution

The paper presents a new approach that integrates MCTS with VO for safe, efficient motion planning using limited obstacle data, outperforming existing methods.

Findings

VO improves MCTS efficiency in dynamic environments

Method reduces collision rate compared to NMPC

Approach achieves better computational and task performance

Abstract

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.