Optimized dynamic path planning for multi-robot systems: integrating collision detection with deep reinforcement learning

Abstract

Abstract In this paper, we propose a novel dynamic path planning method for multiple mobile robots, named CBS-TLTD3. The method combines the Conflict-Based Search (CBS) algorithm for global path planning and enhances the obstacle avoidance capability of the Twin Delayed Deep Deterministic Policy Gradient (TD3) model through transfer learning, thereby developing the TLTD3 model. Additionally, we introduce an intermediate planner to coordinate these two methods, enabling continuous path planning. This integrated approach enables real-time conflict detection and resolution, ensuring stable and continuous global path planning. Furthermore, the effectiveness of this integrated method is validated through simulations and real-world experiments, providing valuable insights and solutions for multi-robot path planning applications.