Collision probability reduction method for tracking control in automatic docking / berthing using reinforcement learning

no code implementations • 13 Dec 2022 • Kouki Wakita, Youhei Akimoto, Dimas M. Rachman, Yoshiki Miyauchi, Umeda Naoya, Atsuo Maki

This paper proposes a training method based on reinforcement learning for a trajectory tracking controller that reduces the probability of collisions with static obstacles.

On Neural Network Identification for Low-Speed Ship Maneuvering Model

no code implementations • 11 Nov 2021 • Kouki Wakita, Atsuo Maki, Umeda Naoya, Yoshiki Miyauchi, Tohga Shimoji, Dimas M. Rachman, Youhei Akimoto

A new system identification method for generating a low-speed maneuvering model using recurrent neural networks (RNNs) and free running model tests is proposed in this study.