Research on Path Planning Strategy of Rescue Robot Based on Reinforcement Learning

Research on Path Planning Strategy of Rescue Robot Based on Reinforcement Learning

How rescue robots reach their destinations quickly and efficiently has become a hot research topic in recent years. Aiming at the complex unstructured environment faced by rescue robots, this paper proposes an artificial potential field algorithm based on reinforcement learning. Firstly, use the traditional artificial potential field method to perform basic path planning for the robot. Secondly, in order to solve the local minimum problem in planning and improve the robot’s adaptive ability, the reinforcement learning algorithm is run by fixing preset parameters on the simulation platform. After intensive training, the robot continuously improves the decision-making ability of crossing typical concave obstacles. Finally, through simulation experiments, it is concluded that the rescue robot can combine the artificial potential field method and reinforcement learning to improve the ability to adapt to the environment, and can reach the destination with the optimal route.