Research on 2D Obstacle Avoidance Model Based on Field Rescue

This paper presents a novel 2D obstacle avoidance model tailored for field search and rescue robots. Leveraging advanced deep reinforcement learning techniques, the proposed model aims to enhance the robots’ navigation capabilities in dynamic and unpredictable environments. We evaluated four distinct reinforcement learning models—Deep Q-Learning (DQL), Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C), and Deep Q-Network (DQN)—to identify the most effective approach for real-time obstacle avoidance. Experimental results demon- strata that the DQL model outperforms the others in terms of success rate, average reward, and stability, making it particularly suitable for complex field operations. This research contributes to the development of robust and efficient navigation strategies for autonomous search and rescue missions, with potential implications for broader applications in robotics.