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

Li, Lingyu and Wu, Defeng and Huang, Youqiang and Yuan, Zhi-Ming (2021) A path planning strategy unified with a COLREGS collision avoidance function based on deep reinforcement learning and artificial potential field. Applied Ocean Research, 113. 102759. ISSN 0141-1187

[thumbnail of Li-etal-AOR-2021-A-path-planning-strategy-unified-with-a-COLREGS-collision-avoidance] Text (Li-etal-AOR-2021-A-path-planning-strategy-unified-with-a-COLREGS-collision-avoidance)
Li_etal_AOR_2021_A_path_planning_strategy_uni_ed_with_a_COLREGS_collision_avoidance.pdf
Accepted Author Manuscript
Restricted to Repository staff only until 27 June 2022.
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo

Download (21MB) | Request a copy from the Strathclyde author

    Abstract

    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.

    ORCID iDs

    Li, Lingyu, Wu, Defeng, Huang, Youqiang and Yuan, Zhi-Ming ORCID logoORCID: https://orcid.org/0000-0001-9908-1813;