Path Planning of Mobile Robot Based on A Star Algorithm Combining DQN and DWA in Complex Environment

The path planning algorithm not only ensures that the mobile robot can avoid obstacles to reach the target point at a safe speed but also ensures that the mobile robot can quickly adapt to the complex changing environment. In this paper, the existing path planning algorithms of mobile robots are analyzed, and then the fusion path planning algorithm is studied. The main work is summarized as follows: A* algorithm is used to complete global path planning and path smoothing, the basic principle of dynamic window method (DWA) is studied, and the dynamic constraints of mobile robots are discussed. The shortcomings of the dynamic window method, i.e., that the dynamic window method does not have the ability to self-learn and self-adapt in the dynamic and unknown environment, are analyzed through simulation experiments. In addition, by studying the basic principle of deep reinforcement learning, the essence and characteristics of DWA algorithm and DQN algorithm are analyzed, which provides ideas for the fusion path planning algorithm based on DQN and DWA. Finally, to cope with the complex and changeable environment and improve the real-time obstacle avoidance ability of mobile robots, a fusion path planning algorithm based on DQN and DWA is proposed. First, the dynamic window method is used to limit the driving of the mobile robot directly to the velocity space. Then, a deep Q network is designed and trained to approximate the state-action value function of the mobile robot, then dynamically interact with the environment to adjust the robot’s moving trajectory in real time, and finally, find the optimal path for the robot. The simulation results show that the fusion path planning algorithm proposed in this paper ensures that the mobile robot has strong generalization ability and robustness under the complex, variable, and dynamic unknown environment. Compared with the existing DWA and DQN algorithms, the proposed fusion path planning algorithm achieves better path planning performance with less training times, shorter computation time, and faster convergence speed.