Journal is indexed in following databases:

2022 Journal Impact Factor - 0.6
2022 CiteScore - 1.7




ISSN 2083-6473
ISSN 2083-6481 (electronic version)




Associate Editor
Prof. Tomasz Neumann

Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
Neuroevolutionary Autonomous Surface Vehicle Simulation in Restricted Waters
Times cited (SCOPUS): 5
ABSTRACT: Safe, accurate, and predictable autonomous systems in marine vehicles are paramount. An understanding of an intelligent system fitted inside a ship is critical to ensure an autonomous ship is safe to be operated. Although the use of artificial intelligence in the design of the road-based vehicle has arrived at the self-driving level, there exists a significant gap within the research of autonomous ship to operate in restricted water (riverine and ports). Hence, this article shall discuss the relevant works of literature to set a preliminary guiding principle for the design of an autonomous ship. We present a simple illustrative framework as a starting point for ship designers to begin working in a simulated environment, which can be used as a foundation before the physical autonomous-ships are constructed and tested in a real-world situation. The framework consists of a virtual 3D environment and a surface vehicle with distance sensors, controlled by a neuroevolution-based autonomous piloting system. In this work, two scenarios will be presented: navigation in restricted waters, and obstacle avoidance capability of an autonomous ship. Results show that the resulting autonomous surface vehicle (ASV) is also capable of performing obstacle avoidance in the test track, albeit not being trained to do so in the training track. The work demonstrated in this paper is useful to the ship designers and can be extended for scenario-based planning for autonomous ship design.
Y. A. Ahmed and K. Hasegawa, “Implementation of automatic ship berthing using artificial neural network for free running experiment,” in IFAC Proceedings Volumes (IFAC-PapersOnline), 2013, doi: 10.3182/20130918-4-JP-3022.00036. - doi:10.3182/20130918-4-JP-3022.00036
Y. A. Ahmed and K. Hasegawa, “Artificial neural network based automatic ship berthing combining PD controlled side thrusters - A combined controller for final approaching to berth,” in 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, 2014, doi: 10.1109/ICARCV.2014.7064504. - doi:10.1109/ICARCV.2014.7064504
K. Priandana, B. Kusumoputro, and E. T. Rahardjo, “The design of ISM-band radar antenna for small boat’s trajectory tracking,” in QiR 2017 - 2017 15th International Conference on Quality in Research (QiR): International Symposium on Electrical and Computer Engineering, 2017, doi: 10.1109/QIR.2017.8168460. - doi:10.1109/QIR.2017.8168460
A. Dabrowski, S. Busch, and R. Stelzer, “A Digital Interface for Imagery and Control of a Navico/Lowrance Broadband Radar,” in Robotic Sailing, 2011. - doi:10.1007/978-3-642-22836-0_12
T. Wu, Y. Dong, Z. Dong, A. Singa, X. Chen, and Y. Zhang, “Testing Artificial Intelligence System Towards Safety and Robustness: State of the Art,” IAENG Int. J. Comput. Sci., vol. 47, no. 3, 2020.
N. Uddin, “A Two-Wheeled Robot Trajectory Tracking Control System Design Based on Poles Domination Approach,” IAENG Int. J. Comput. Sci., vol. 47, no. 2, 2020.
H. Noshahri, T. J. A. De Vries, and J. Van Amerongen, “Towards Automatic Steering of Underactuated Ships,” in IFAC-PapersOnLine, 2019, doi: 10.1016/j.ifacol.2019.12.293. - doi:10.1016/j.ifacol.2019.12.293
S.-D. Lee, C.-Y. Tzeng, and W.-W. Huang, “Ship Steering Autopilot Based on ANFIS Framework and Conditional Tuning Scheme,” Mar. Eng. Front., 2013.
L. Moreira and C. Guedes Soares, “Recursive neural network model of catamaran manoeuvring,” Trans. R. Inst. Nav. Archit. Part A Int. J. Marit. Eng., 2012, doi: 10.3940/rina.ijme.2012.a3.232.
W. Naeem, S. C. Henrique, and L. Hu, “A Reactive COLREGs-Compliant Navigation Strategy for Autonomous Maritime Navigation,” IFAC-PapersOnLine, 2016, doi: 10.1016/j.ifacol.2016.10.344. - doi:10.1016/j.ifacol.2016.10.344
M. Kurowski, H. Korte, and B. P. Lampe, “AGaPaS - A new approach for search-and-rescue-operations at sea,” in IFAC Proceedings Volumes (IFAC-PapersOnline), 2012, doi: 10.3182/20120919-3-IT-2046.00013. - doi:10.3182/20120919-3-IT-2046.00013
M. Kurowski and B. P. Lampe, “AGaPaS: A new approach for search-and-rescue-operations at sea,” Proc. Inst. Mech. Eng. Part M J. Eng. Marit. Environ., 2014, doi: 10.1177/1475090213504392. - doi:10.1177/1475090213504392
Q. Zhang, N. Jiang, Y. Hu, and D. Pan, “Design of Course-Keeping Controller for a Ship Based on Backstepping and Neural Networks,” Int. J. e-Navigation Marit. Econ., 2017, doi: 10.1016/j.enavi.2017.06.004. - doi:10.1016/j.enavi.2017.06.004
Y. Wang, S. Chai, and H. D. Nguyen, “Experimental and numerical study of autopilot using Extended Kalman Filter trained neural networks for surface vessels,” Int. J. Nav. Archit. Ocean Eng., 2020, doi: 10.1016/j.ijnaoe.2019.11.004. - doi:10.1016/j.ijnaoe.2019.11.004
M. C. Tsou and C. K. Hsueh, “The study of ship collision avoidance route planning by ant colony algorithm,” J. Mar. Sci. Technol., 2010.
Y. A. Ahmed and K. Hasegawa, “Automatic ship berthing using artificial neural network based on virtual window concept in wind condition,” in IFAC Proceedings Volumes (IFAC-PapersOnline), 2012, doi: 10.3182/20120912-3-BG-2031.00059. - doi:10.3182/20120912-3-BG-2031.00059
K. Hasegawa, “From Ship manoeuvrability, controllability, captain’s model, traffic model to accident and tsunami analysis,” in International Workshop on Nautical Traffic Models, 2013.
M. Łącki, “Neuroevolutionary Ship Maneuvering Prediction System,” in Information, Communication and Environment, 2015. - doi:10.1201/b18514-17
V. L. Tran and N. Im, “A study on ship automatic berthing with assistance of auxiliary devices,” Int. J. Nav. Archit. Ocean Eng., 2012, doi: 10.3744/JNAOE.2012.4.3.199. - doi:10.3744/JNAOE.2012.4.3.199
M. Łącki, “Ship course-keeping with neuroevolutionary algorithms,” Zesz. Nauk. Akad. Morskiej w Szczecinie, 2018, doi: 10.17402/287.
C. Chen, X. Q. Chen, F. Ma, X. J. Zeng, and J. Wang, “A knowledge-free path planning approach for smart ships based on reinforcement learning,” Ocean Eng., 2019, doi: 10.1016/j.oceaneng.2019.106299. - doi:10.1016/j.oceaneng.2019.106299
A. Lazarowska, “Research on algorithms for autonomous navigation of ships,” WMU J. Marit. Aff., 2019, doi: 10.1007/s13437-019-00172-0. - doi:10.1007/s13437-019-00172-0
D. Looije and Y. Koldenhof, “Unmanned ship simulation with real-time dynamic risk index,” Zesz. Nauk. Akad. Morskiej w Szczecinie, 2015.
D. A. Oskin, A. A. Dyda, and V. E. Markin, “Neural network identification of marine ship dynamics,” in IFAC Proceedings Volumes (IFAC-PapersOnline), 2013, doi: 10.3182/20130918-4-JP-3022.00018. - doi:10.3182/20130918-4-JP-3022.00018
Y. Xue, D. Clelland, B. S. Lee, and D. Han, “Automatic simulation of ship navigation,” Ocean Eng., 2011, doi: 10.1016/j.oceaneng.2011.10.011. - doi:10.1016/j.oceaneng.2011.10.011
R. Skulstad, G. Li, H. Zhang, and T. I. Fossen, “A Neural Network Approach to Control Allocation of Ships for Dynamic Positioning,” IFAC-PapersOnLine, 2018, doi: 10.1016/j.ifacol.2018.09.481. - doi:10.1016/j.ifacol.2018.09.481
L. P. Perera, “Autonomous ship navigation under deep learning and the challenges in colregs,” in Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, 2018, doi: 10.1115/OMAE2018-77672. - doi:10.1115/OMAE2018-77672
T. Statheros, G. Howells, and K. McDonald-Maier, “Autonomous ship collision avoidance navigation concepts, technologies and techniques,” J. Navig., 2008, doi: 10.1017/S037346330700447X. - doi:10.1017/S037346330700447X
Y. Cheng and W. Zhang, “Concise deep reinforcement learning obstacle avoidance for underactuated unmanned marine vessels,” Neurocomputing, 2018, doi: 10.1016/j.neucom.2017.06.066. - doi:10.1016/j.neucom.2017.06.066
A. I. Kozynchenko and S. A. Kozynchenko, “Applying the dynamic predictive guidance to ship collision avoidance: Crossing case study simulation,” Ocean Eng., 2018, doi: 10.1016/j.oceaneng.2018.07.012. - doi:10.1016/j.oceaneng.2018.07.012
X. D. Cheng, Z. Y. Liu, and X. T. Zhang, “Trajectory optimization for ship collision avoidance system using genetic algorithm,” in OCEANS 2006 - Asia Pacific, 2006, doi: 10.1109/OCEANSAP.2006.4393976. - doi:10.1109/OCEANSAP.2006.4393976
J. D. Schaffer, D. Whitley, and L. J. Eshelman, “Combinations of genetic algorithms and neural networks: A survey of the state of the art,” in COGANN 1992 - International Workshop on Combinations of Genetic Algorithms and Neural Networks, 1992, doi: 10.1109/COGANN.1992.273950. - doi:10.1109/COGANN.1992.273950
X. Yao, “Evolving artificial neural networks,” Proc. IEEE, 1999, doi: 10.1109/5.784219. - doi:10.1109/5.784219
D. Floreano, P. Dürr, and C. Mattiussi, “Neuroevolution: From architectures to learning,” Evolutionary Intelligence. 2008, doi: 10.1007/s12065-007-0002-4. - doi:10.1007/s12065-007-0002-4
L. Harris, “Unmanned Marine Systems - ASVs, USVs & Autonomous Boat Control System,” 2020. [Online]. Available:
A. Rescec, “Dynamic Water Physics 2,” Unity Assets Store, 2020. [Online]. Available:
Citation note:
Ayob A.F., Jalal N.I., Hassri M.H., Rahman S.A., Jamaludin S.: Neuroevolutionary Autonomous Surface Vehicle Simulation in Restricted Waters. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 14, No. 4, doi:10.12716/1001.14.04.11, pp. 865-873, 2020

File downloaded 282 times

Important: cookie usage
The website uses certain cookies. A cookie is a text-only string of information that the TransNav.EU website transfers to the cookie file of the browser on your computer. Cookies allow the website to perform properly and remember your browsing history. Cookies also help a website to arrange content to match your preferred interests more quickly. Cookies alone cannot be used to identify you.
Akceptuję pliki cookies z tej strony