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ISHIBASHI Atsushi
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Research Areas 【 display / non-display 】
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Frontier Technology (Aerospace Engineering, Marine and Maritime Engineering) / Marine engineering
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Social Infrastructure (Civil Engineering, Architecture, Disaster Prevention) / Social systems engineering
Papers 【 display / non-display 】
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3D Environmental Map for Navigational Safety in Autonomous Ship Operations
Ayong Yang, Atsushi Ishibashi, Ryota Imai, Tsuyoshi MIYASHITA, Tadasuke FURUYA , 2025.02
Springer Proceedings of Tenth International Congress on Information and Communication Technology ICICT 2025, London, Volume 3
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Design and Implementation of Ship Navigational Information System using Smartphone Sensor Data
Ryota IMAI, Atsushi Ishibashi,Ayoung YANG,Tsuyoshi MIYASHITA,Tadasuke FURUYA , 2025.02
Springer Proceedings of Tenth International Congress on Information and Communication Technology ICICT 2025, London, Volume 3
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Development of an Autonomous Navigation System for Small Vessels Using Remote Control
Ayong Yang, Atsushi Ishibashi, Ryota Imai, Tadasuke FURUYA , 2024.07
IEEE Xplorer
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Interface Design for Small Vessels in Autonomous Navigation Systems
Ryota Imai, Atsushi Ishibashi, Takahiro Takemoto, Ayong Yang, Tadasuke FURUYA , 2024.03
MLHMI 2025 CPS Conference Proceedings
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船舶運航者の操船特性に基づいた着桟操船支援システムの開発に関する研究
石橋 篤 , 2020.07
大阪大学 学位論文
Grant-in-Aid for Scientific Research 【 display / non-display 】
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Research on Establlshment of Safety Evaluation Methods for Autonomous Navigation System
Project Period (FY)： 2025/04 - 2028/03 Investigator(s): 古川 芳孝
Grant-in-Aid for Scientific Research(B) Co-Investigator 25K01437
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Development of Prediction Model of Ship Manoeuvring Motion using Recurrent Neural Network
Project Period (FY)： 2021/04 - 2024/03 Investigator(s): Furukawa Yoshitaka
Grant-in-Aid for Scientific Research(B) Co-Investigator 21H01550
Accurate simulations of ship manoeuvring motions are essential to develop control systems for autonomous ships. However, mathematical model of hydrodynamic forces acting on a ship hull using hydrodynamic coefficients cannot be applied to the manoeuvring motion of a ship navigating at low speed in restricted water area. In this research, instead of the conventional prediction method of ship manoeuvring motion by solving the equations of motion of a ship with hydrodynamic coefficients, a recurrent neural network which is a type of deep learning technology is applied to the time series data of manoeuvring motion to predict ship manoeuvring motion based on input such as rudder angle.
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Development of Algorithm for Ship Handling Decision using Deep Reinforcement Learning
Project Period (FY)： 2018/04 - 2021/03 Investigator(s): Furukawa Yoshitaka
Grant-in-Aid for Scientific Research(B) Co-Investigator 18H01642
In order to realize autonomous ships, it is necessary to develop an algorithm to properly evaluate circumstance around an own ship such as weather conditions, states of relative ships and so on and to make a decision to navigate the own ship safely changing her course and speed. In this research, an algorithm which can make a ship possible to navigate autonomously considering various complicated conditions around a ship by introducing deep reinforcement learning were developed. Furthermore, a model ship control system which can be used to evaluate the performance of the developed navigation algorithm was also developed. The manoeuvring motion of a model ship can be controlled based on information such as model ship’s position, heading angle, speed, yaw rate and so on.
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Project Period (FY)： 2015/10 - 2018/03
Grant-in-Aid for Scientific Research(C) Principal Investigator 15K01223
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移動体オペレータの行動特性から観た船舶運航安全性評価に関する研究
Project Period (FY)： 2011/05 - 2013/03
Grant-in-Aid for Scientific Research(C) Principal Investigator 23510199
Lesson Subject 【 display / non-display 】
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Lesson Subject（Undergraduate）
商船科指導法Ⅰ
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Kinetics of floating body motion
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浮体運動論
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短艇実習
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Human Resource Management
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組織管理論
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Ship operation and Ship Manoeuvrability
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船舶運航論