Safe Ship Control Method with the Use of Ant Colony Optimization

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Abstract:

Nowadays Integrated Bridge Systems are applied on board a ship to increase safety of navigation. These systems consist of many electronic devices such as radar, ECDIS and autopilot, which aid the deck officer in the process of conducting navigation. Despite that, ship accidents caused by human error still occur. The paper presents new method of safe ship control in collision situations. Ant Colony Optimization is applied to determine safe ship trajectory. Developed algorithm is applicable for situations in restricted waters, where most of collision situations occur. International Regulations for Preventing Collisions at Sea (COLREGs) are taken into consideration in the process of solution construction. The task of collision avoidance at sea is defined as dynamic optimization problem with the use of static and dynamic constraints. Static constraints are represented by lands, canals, shallows, fairways, while other ships constitute dynamic constraints. Described method was implemented in MATLAB programming language. Performed simulation tests results of encounter situations with one target ship as well as with many target vessels are presented. Received solutions confirm successful application of this method to the problem of ships collisions avoidance. Developed algorithm deals also with more complex situations. This new algorithm is planned to be implemented in anti-collision decision support system on board a ship, what would contribute to enhance safety of maritime transport.

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Solid State Phenomena (Volume 210)

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234-244

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October 2013

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Allianz Global Corporate & Specialty AG: Safety and Shipping 1912 -2012 From Titanic to Costa Concordia (2012).

Google Scholar

[2] European Maritime Safety Agency: Annual Report (2011).

Google Scholar

[3] Maritime Knowledge Centre, International Maritime Organization: International Shipping Facts and Figures – Information Resources on Trade, Safety, Security, Environment (2012).

Google Scholar

[4] A.M. Rothblum: Human Error and Marine Safety, in US Coastguard Research and Development Centre, pp.1-10 (2000).

Google Scholar

[5] A.M. Rothblum: Keys to successful incident inquiry, in Human Factors in Incident Investigation and Analysis, 2nd International Workshop on Human Factors in Offshore Operations (HFW2002), Houston, TX (2002).

Google Scholar

[6] J. Lisowski: The Dynamic Game Models of Safe Navigation, TransNav - International Journal of Marine Navigation and Safety of Sea Transportation, Vol. 1, No. 1, pp.11-18 (2007).

DOI: 10.12716/1001.12.03.11

Google Scholar

[7] J. Lisowski: Computational intelligence methods in the safe ship control process, Polish Maritime Research, No. 1, Vol. 8, pp.18-24 (2001).

Google Scholar

[8] J. Lisowski: Game control methods in avoidance of ships collisions, Polish Maritime Research, Vol. 19, No74, pp.3-10 (2012).

DOI: 10.2478/v10012-012-0016-4

Google Scholar

[9] R. Śmierzchalski, Z. Michalewicz: Path Planning in Dynamic Environments, Innovations in Robot Mobility and Control , pp.135-153 (2005).

DOI: 10.1007/10992388_4

Google Scholar

[10] Ch. Tam, R. Bucknall: Path-planning algorithm for ships in close-range encounters, Journal of Marine Science and Technology, Vol. 15, Issue 4, pp.395-407 (2010).

DOI: 10.1007/s00773-010-0094-x

Google Scholar

[11] S. Campbell, Naeem W., G.W. Irwin: A Review on Improving the Autonomy of Unmanned Surface Vehicles through Intelligent Collision Avoidance Manoeuvres, IFAC Journal of Annual Reviews in Control, Vol. 36, Issue 2, pp.267-283 (2012).

DOI: 10.1016/j.arcontrol.2012.09.008

Google Scholar

[12] D. D. Guzman, S. Dellosa, P. Inventado, P. Lao, M. Suarez: A*Path Planning in Real-time Dynamic Environments, Proceedings of the 5th Philippine Computing Science Congress, pp.186-189 (2005).

Google Scholar

[13] J. -H. Ahn, K. -P. Rhee, Y. -J. You: A study on the collision avoidance of a ship using neural networks and fuzzy logic, Applied Ocean Research, Vol. 37, p.162–173 (2012).

DOI: 10.1016/j.apor.2012.05.008

Google Scholar

[14] L. P. Perera, J.P. Carvalho, C. Guedes Soares: Fuzzy logic based decision making system for collision avoidance of ocean navigation under critical collision conditions, Journal of Marine Science and Technology, Vol. 16, No. 1, pp.84-99 (2011).

DOI: 10.1007/s00773-010-0106-x

Google Scholar

[15] J. Lisowski, M.M. Seghir: The safe ship control with minimum risk of collision, Transactions on Ecology and the Environment, Vol. 24, WIT Press (1998).

Google Scholar

[16] Z. Pietrzykowski: Ship's Fuzzy Domain - a Criterion for Navigational Safety in Narrow Fairways, Journal of Navigation, pp.499-514 (2008).

DOI: 10.1017/s0373463308004682

Google Scholar

[17] E. M. Goodwin: A Statistical Study of Ship Domains, Journal of Navigation, 28, pp.328-344 (1975).

Google Scholar

[18] J. Lisowski, A. Rak, W. Czechowicz: Neural network classifier for ship domain assessment, Mathematics and Computers in simulation 51, pp.399-406 (2000).

DOI: 10.1016/s0378-4754(99)00132-9

Google Scholar

[19] R. Szłapczyński: Evolutionary Sets of Cooperating Trajectories in multi-Ship Encounter Situations - Use Cases, TransNav - International Journal of Marine Navigation and Safety of Sea Transportation, Vol. 4, No 2, pp.191-196 (2010).

DOI: 10.1201/9780203869345.ch77

Google Scholar

[20] M. -Ch. Tsou, Ch. -K. Hsueh: The Study of Ship Collision Avoidance Route Planning by Ant Colony Algorithm, Journal of Marine Science and Technology, Vol. 18, No. 5, pp.746-756 (2010).

DOI: 10.51400/2709-6998.1929

Google Scholar

[21] M. Dorigo, M. Montes de Oca, S. Oliveira., T. Stützle 2011: Ant Colony Optimization , in J. J. Cochran, editor, Wiley Encyclopedia of Operations Research and Management Science, pp.114-125, John Wiley & Sons (2011).

DOI: 10.1002/9780470400531.eorms0030

Google Scholar

[22] R. Śmierzchalski: An Evolutionary Method of Ship's Trajectory Planning, Electrotechnical Review, No. 4, pp.358-362 (2004).

Google Scholar

[23] J. Lisowski, A. Lazarowska: The radar data transmission to computer support system of ship safety, Solid State Phenomena, Vol. 196, pp.95-101, Trans Tech Publications, Switzerland (2013).

DOI: 10.4028/www.scientific.net/ssp.196.95

Google Scholar

[24] A. Lazarowska: Decision support system for collision avoidance at sea, Polish Maritime Research, Vol. 19, No 74, pp.19-24 (2012).

DOI: 10.2478/v10012-012-0018-2

Google Scholar

[25] M. Dorigo, T. Stützle: The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances, Handbook of Metaheuristics, International Series in Operations Research & Management Science, Vol. 57, pp.250-285 (2003).

DOI: 10.1007/0-306-48056-5_9

Google Scholar

[26] M. Dorigo, T. Stützle: Ant Colony Optimization: Overview and Recent Advances, in M. Gendreau and J. -Y. Potvin, editors, Handbook of Metaheuristics, volume 146 of International Series in Operations Research & Management Science, chapter 8, pp.227-263. Springer, New York (2010).

DOI: 10.1007/978-1-4419-1665-5_8

Google Scholar

[27] A. Lazarowska: Application of Ant Colony Optimization in Ship's Navigational Decision Support System, in Marine Navigation and Safety of Sea Transportation: Navigational Problems, CRC Press, p.53–62 (2013).

DOI: 10.1201/b14962-12

Google Scholar

[28] A. Lazarowska: Ant algorithms for ship route planning, Scientific Journals Gdynia Maritime University, No. 78, pp.43-52 (2013).

Google Scholar

[29] A. N. Cockcroft, J.N.F. Lameijer: A Guide to the Collision Avoidance Rules, Butterworth-Heinemann Ltd (2012).

Google Scholar

[30] Z. Pietrzykowski, P. Borkowski, P. Wołejsza: NAVDEC – navigational decision support system on a sea-going vessel, Scientific Journals of Szczecin Maritime University, No. 30 , pp.102-108 (2012).

Google Scholar