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

Lingyu Li, Defeng Wu, Youqiang Huang, Zhi-Ming Yuan

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.
Original languageEnglish
Article number102759
Number of pages16
JournalApplied Ocean Research
Volume113
Early online date27 Jun 2021
DOIs
Publication statusPublished - 31 Aug 2021

Keywords

  • deep reinforcement learning
  • path planning
  • artificial potential field
  • COLREGS collision avoidance

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