Dynamic Obstacle Avoidance Method for Carrier Aircraft Based on Deep Reinforcement Learning

Abstract

<p indent="0mm">Aiming at the obstacle avoidance problem of carrier aircraft in the highly heterogeneous and dynamic aircraft carrier deck operation scene, a deep reinforcement learning obstacle avoidance method combined with a prediction algorithm is proposed. The method includes scene modeling, reward model and trajectory prediction model. First, the aircraft carrier deck scene is modeled based on the agent state and action space. Then the least square method is used to predict the position of dynamic obstacles in the scene in real-time and a deep reinforcement learning algorithm — environmental prediction deep Q network (PDQN) is constructed which includes a path prediction module. Finally, the algorithm is used to achieve dynamic obstacle avoidance in the aircraft carrier deck operation scene. The Python drawing set Matplotlib is used for simulation experiments. The experimental results show that, compared with Q-learning, SARSA, the accuracy of the proposed method is improved by 15%–25%, the path length is shorter by 9%–39%, the average reward value is higher by 30%–100%, the convergence speed is 1–2 times faster, and the standard deviation of the accuracy after training is small by 2%–50%.