Robust Path Planning Using Adaptive Reinforcement Learning in Simulation Environment

As autonomous driving gains widespread attention, extensive research is being conducted to enable robots to safely reach their destinations independently. Before the rise of advanced robotic artificial intelligence, much research focused on path planning for route exploration and planning. However, these methods have limitations in responding to dynamic environmental changes. To overcome this, efforts have been made to integrate reinforcement learning. This study proposes an adaptive path planning reinforcement learning algorithm, combining the efficiency of path planning with the adaptability of reinforcement learning to various environmental changes. The proposed method effectively integrates traditional path planning algorithms with reinforcement learning. We utilize the TD3 network as the backbone and apply the Artificial Potential Field algorithm, known for its robustness to obstacles. Experimental results demonstrate that our approach achieved approximately 14 points higher average rewards and similar maximum rewards compared to existing networks, indicating higher average success rates. Additionally, results specified above indicate faster or comparable convergence speeds relative to traditional networks.