Proximal policy optimization based dynamic path planning algorithm for mobile robots

For the scenario where the overall layout is known and the obstacle distribution information is unknown, a dynamic path planning algorithm combining the A* algorithm and the proximal policy optimization (PPO) algorithm is proposed. Simulation experiments show that in all six test environments, the proposed algorithm finds paths that are on average about 2.04% to 5.86% shorter compared to the state-of-the-art algorithms in the literature, and reduces the number of training epochs before stabilization from tens of thousands to about 4000.