Search Results for author: Atsuo Maki

Found 11 papers, 0 papers with code

A Practical and Online Trajectory Planner for Autonomous Ships' Berthing, Incorporating Speed Control

no code implementations14 Feb 2024 Agnes Ngina Mwange, Dimas Maulana Rachman, Rin Suyama, Atsuo Maki

Autonomous ships are essentially designed and equipped to perceive their internal and external environment and subsequently perform appropriate actions depending on the predetermined objective(s) without human intervention.

Collision Avoidance Trajectory Planning

Parameter fine-tuning method for MMG model using real-scale ship data

no code implementations7 Dec 2023 Rin Suyama, Rintaro Matsushita, Ryo Kakuta, Kouki Wakita, Atsuo Maki

The results show that, in all cases, the accuracy of the maneuvering simulation is improved by applying the tuned parameters to the MMG model, and the validity of the proposed parameter fine-tuning method is confirmed.

Time Series

Nonlinear steering control under input magnitude and rate constraints with exponential convergence

no code implementations25 Oct 2023 Rin Suyama, Satoshi Satoh, Atsuo Maki

To derive this system, hyperbolic tangent ($\tanh$) function and auxiliary variables are introduced to deal with the input constraints.

Steering Control

Data Augmentation Methods of Parameter Identification of a Dynamic Model for Harbor Maneuvers

no code implementations30 May 2023 Kouki Wakita, Yoshiki Miyauchi, Youhei Akimoto, Atsuo Maki

In this paper, we improve the generalization performance of the dynamic model for the automatic berthing and unberthing controller by introducing data augmentation.

Data Augmentation

Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min--Max Optimization and its Application to Berthing Control Tasks

no code implementations28 Mar 2023 Atsuhiro Miyagi, Yoshiki Miyauchi, Atsuo Maki, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto

In this study, we consider a continuous min--max optimization problem $\min_{x \in \mathbb{X} \max_{y \in \mathbb{Y}}}f(x, y)$ whose objective function is a black-box.

Experimental Low-speed Positioning System with VecTwin Rudder for Automatic Docking (Berthing)

no code implementations19 Dec 2022 Dimas M. Rachman, Yusuke Aoki, Yoshiki Miyauchi, Naoya Umeda, Atsuo Maki

It is designed upon an assumption that the forces due to the interaction between the rudders, the propeller, and the hull are linear with the rudder angles within a range around the hover rudder angle.

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

no code implementations13 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.

Warm-started Semionline Trajectory Planner for Ship's Automatic Docking (Berthing)

no code implementations22 Dec 2021 Dimas M. Rachman, Atsuo Maki, Yoshiki Miyauchi, Naoya Umeda

This article demonstrates that the balance between the feasibility of the reference trajectory and the computational time can be achieved for an underactuated vessel in a disturbed and restricted environment.

System Parameter Exploration of Ship Maneuvering Model for Automatic Docking / Berthing using CMA-ES

no code implementations11 Nov 2021 Yoshiki Miyauchi, Atsuo Maki, Naoya Umeda, Dimas M. Rachman, Youhei Akimoto

The main contributions of this study are as follows: (i) construct the system-based mathematical model on berthing by optimizing system parameters with a reduced amount of model tests than the CMT-based scheme; (ii) Find the favorable choice of objective function and type of training data for optimization.

On Neural Network Identification for Low-Speed Ship Maneuvering Model

no code implementations11 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.

Saddle Point Optimization with Approximate Minimization Oracle and its Application to Robust Berthing Control

no code implementations25 May 2021 Youhei Akimoto, Yoshiki Miyauchi, Atsuo Maki

We propose an approach to saddle point optimization relying only on oracles that solve minimization problems approximately.

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