Multi-AGV Path Planning Method via Reinforcement Learning and Particle Filters

The Reinforcement Learning (RL) algorithm, renowned for its robust learning capability and search stability, has garnered significant attention and found extensive application in Automated Guided Vehicle (AGV) path planning. However, RL planning algorithms encounter challenges stemming from the substantial variance of neural networks caused by environmental instability and significant fluctuations in system structure. These challenges manifest in slow convergence speed and low learning efficiency. To tackle this issue, this paper presents the Particle Filter-Double Deep Q-Network (PF-DDQN) approach, which incorporates the Particle Filter (PF) into multi-AGV reinforcement learning path planning. The PF-DDQN method leverages the imprecise weight values of the network as state values to formulate the state space equation. Through the iterative fusion process of neural networks and particle filters, the DDQN model is optimized to acquire the optimal true weight values, thus enhancing the algorithm's efficiency. The proposed method's effectiveness and superiority are validated through numerical simulations. Overall, the simulation results demonstrate that the proposed algorithm surpasses the traditional DDQN algorithm in terms of path planning superiority and training time indicators by 92.62% and 76.88%, respectively. In conclusion, the PF-DDQN method addresses the challenges encountered by RL planning algorithms in AGV path planning. By integrating the Particle Filter and optimizing the DDQN model, the proposed method achieves enhanced efficiency and outperforms the traditional DDQN algorithm in terms of path planning superiority and training time indicators.