A path planning strategy unified with a COLREGS collision avoidance function based on deep reinforcement learning and artificial potential field

Improving the autopilot capability of ships is particularly important to ensure the safety of maritime navigation. The unmanned surface vessel (USV) with autopilot capability is a development trend of the ship of the future. The objective of this paper is to investigate the path planning problem of USVs in uncertain environments, and a path planning strategy unified with a collision avoidance function based on deep reinforcement learning (DRL) is proposed. A Deep Q-learning network (DQN) is used to continuously interact with the visually simulated environment to obtain experience data, so that the agent learns the best action strategies in the visual simulated environment. To solve the collision avoidance problems that may occur during USV navigation, the location of the obstacle ship is divided into four collision avoidance zones according to the International Regulations for Preventing Collisions at Sea (COLREGS). To obtain an improved DRL algorithm, the artificial potential field (APF) algorithm is utilized to improve the action space and reward function of the DQN algorithm. A simulation experiments is utilized to test the effects of our method in various situations. It is also shown that the enhanced DRL can effectively realize autonomous collision avoidance path planning.

Automated summary

This study proposes a path planning strategy for collision avoidance of unmanned surface vessels in uncertain environments using deep reinforcement learning, with the artificial potential field algorithm improving the action space and reward function of the algorithm to effectively address collision avoidance issues. Simulation experiments demonstrate the effectiveness of the enhanced deep reinforcement learning in autonomous collision avoidance path planning.