[2N6-GS-10] AI application

[2N6-GS-10-02] Automatic Berthing and Collision Avoidance by Domain Randomization

〇Koki Kawakami^1,3, Yoshiki Miyauchi², Atsuo Maki², Youhei Akimoto^1,3 (1. Univ. of Tsukuba, 2. Univ. of Osaka, 3. RIKEN Center for Advanced Intelligence Project)

Keywords:Reinforcement learning, Domain randomization, Automatic berthing

Various studies have been conducted on automatic ship maneuvering in bays, but few studies have specifically addressed obstacle avoidance. This study aims to develop a control law that combines obstacle avoidance and docking control using reinforcement learning. Since the location of obstacles is uncertain during policy training, it is necessary to generalize the policies for different obstacle configurations. This work proposes a method that generates the distribution of the initial states of the obstacles, which is used in domain randomization to obtain a berthing control law that can handle obstacles in unspecified locations and achieve berthing control at the target state. Simulation results show that the proposed approach has a high success rate in avoiding obstacles and achieving berthing control for the ship in the majority of trials.