Dual-Layer Path Planning for Unmanned Ground Vehicles Based on Probabilistic Roadmap and Proximal Policy Optimization

Addressing the crucial challenge of autonomous navigation for unmanned ground vehicles (UGVs), this paper presents a dual-layer path planning method integrating Probabilistic Roadmap (PRM) and Proximal Policy Optimization (PPO). Combining global guidance with local optimization, this approach effectively mitigates the shortcomings of traditional path planning methods such as blindness and local optimality, thus enhancing the efficiency and feasibility of path planning. Specifically, we propose a PRM-RL dual-layer path planning framework that employs the PRM algorithm to generate sub-goals for guiding reinforcement learning exploration, thereby improving training efficiency. Simultaneously, we utilize the PPO algorithm to optimize paths, considering vehicle kinematics and introducing soft constraints to ensure smoother paths adaptable to diverse application scenarios. The superiority and practicality of our method are validated through ablation experiments and comparative experiments, offering a reliable path planning solution for autonomous navigation of UGVs.