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

arXiv: https://arxiv.org/abs/2501.09649 · HTML: https://mlsanity.com/paper/2501.09649/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2025.

## Built on by (most cited first, 3 of 3)

- [Seldonian Reinforcement Learning for Ad Hoc Teamwork](https://mlsanity.com/paper/2503.03885/) (2025)
- [Warm-Starting Collision-Free Model Predictive Control With Object-Centric Diffusion](https://mlsanity.com/paper/2601.02873/) (2026)
- [Robust Scene Transfer for PointGoal Navigation via Privileged Sensor Guided Contrastive Learning](https://mlsanity.com/paper/2606.05506/) (2026)

## Abstract (arXiv)

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.