SwarmNav: Swarm Robotics Navigation in Dynamic and Dense Environments Via Reinforcement Learning

Abstract

Collision avoidance and navigation in dynamic and dense environments remain highly challenging for swarm robotics. To address this, we propose SwarmNav, a novel goal-region amplification navigation policy that leverages LiDAR-based position data to generate velocity commands guiding robots toward their goals while actively avoiding obstacles. SwarmNav is trained within a deep reinforcement learning actor-critic framework. In this framework, the reward function integrates a goal-region amplification term with the reciprocal velocity obstacles formulation, enabling goal-directed navigation under dynamic obstacle uncertainty. Extensive simulations demonstrate that SwarmNav significantly outperforms state-of-the-art approaches, including both reinforcement learning-based and traditional velocity obstacle-based methods, in terms of success rate and computational efficiency. Real-world experiments across diverse scenarios further confirm its effectiveness in dynamic and dense environments.