Path planning of autonomous underwater vehicle in unknown environment based on improved deep reinforcement learning

Abstract

In order to improve the autonomous navigation capability of autonomous underwater vehicles (AUV), this paper applies Deep Reinforcement Learning (DRL) to realize autonomous path planning. In response to the limited detection range and difficult tracking DRL policy in three-dimensional unknown environments, this paper proposes an improved deep reinforcement learning method. Firstly, performing AUV modeling and processing obstacle detection information. Second, under a framework of reinforcement learning with the control system, an improved environmental interaction method was designed by analyzing the motion characteristics and controller performance of the AUV. Finally, based on this method, a policy network that can generate paths that satisfy obstacle avoidance and navigation is trained in combination with a twin delayed deterministic policy gradient (TD3). The simulation results show that the proposed method can achieve AUV path planning in a three-dimensional unknown environment. The proposed framework can ensure that the planned path meets the dynamics constraints and is trackable. The improved algorithm can enhance the convergence of training and the attitude stability of AUV.