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Decision Support in Collision Situations at Sea

Published online by Cambridge University Press:  22 November 2016

Zbigniew Pietrzykowski*
Affiliation:
(Maritime University of Szczecin, Poland)
Piotr Wołejsza
Affiliation:
(Maritime University of Szczecin, Poland)
Piotr Borkowski
Affiliation:
(Maritime University of Szczecin, Poland)

Abstract

The known navigational systems in use perform information functions and as such are helpful in the process of safe conduct of a vessel. One of the ways to assist in reducing the number of marine accidents is the development of systems which perform decision support functions, i.e. automatically generate solutions to collision situations. The use of information (and communication) technologies including knowledge engineering allows the generation of proposals for anti-collision manoeuvres taking into account the COLREGs. Demand for further enhancement of navigational safety by limiting human errors has initiated a trend to convert navigational information systems into decision support systems. The implementation of decision support systems will potentially reduce the number of human errors, which translates into a reduction of accidents at sea and their adverse consequences. This paper presents a summary of the research to date on the navigational decision support system NAVDEC. The system has been positively verified in laboratory conditions and in field tests – on a motor ferry and a sailing ship. Challenges associated with the development and implementation of such systems are outlined.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2016 

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References

REFERENCES

Banaś, P., Pietrzykowski, Z. and Wójcik, A. (2013). A Model of Inference Processes in the Automatic Maritime Communication System. Communications in Computer and Information Science, 395, 714.CrossRefGoogle Scholar
Borkowski, P. (2014). Ship course stabilization by feedback linearization with adaptive object model. Polish Maritime Research, 1(81), 1419.Google Scholar
Borkowski, P. and Zwierzewicz, Z. (2011). Ship course-keeping algorithm based on knowledge base. Intelligent Automation & Soft Computing, 17, 149163.CrossRefGoogle Scholar
Fabri, S. and Kadrikamanathan, V. (2001). Functional Adaptive Control. An Intelligent Systems Approach. Springer.Google Scholar
Gucma, L. and Pietrzykowski, Z. (2006). Ship manoeuvring in restricted areas: An attempt to Quantify Dangerous situations Using a Probabilistic-Fuzzy Method, Journal of Navigation, 59, 251262.Google Scholar
Hansen, M., Jensen, T., Lehn-Schiøler, T., Melchild, K., Rasmussen, F. and Ennemark, F. (2013). Empirical Ship Domain Based on AIS Data., The Journal of Navigation, 66, 931940.Google Scholar
HELCOM. (2010). Report on shipping accidents in the Baltic Sea area during 2010, www.helcom.fi Google Scholar
Hwang, C.N. (2002). The Integrated Design of Fuzzy Collision-Avoidance and H[infinity] Autopilots on Ships. Journal of Navigation, 55(1), 117136.Google Scholar
Kokotos, D. and Linardatos, D. (2010). An application of data mining tools for the study of shipping safety in restricted waters. Safety Science, 49(2), 192197.Google Scholar
Lisowski, J. (2013). The sensitivity of computer support game algorithms of a safe ship control, International Journal Applied Mathematics and Computer Science , 23(2), 439446.Google Scholar
MAIB. (1999). Marine Accident Investigation Branch (MAIB) Annual Report. Department of the Environment Transport and Regions, United Kingdom.Google Scholar
Ming-Cheng, T. and Chao-Kuang, H. (2010). The study of ship collision avoidance route planning by ant colony algorithm, Journal of Marine Science and Technology , 18(5), 746756.Google Scholar
Ming-Cheng, T., Sheng-Long, K. and Chien-Min, S. (2010). Decision Support from Genetic Algorithms for Ship Collision Avoidance Route Planning and Alerts. Journal of Navigation, 63(1), 167182.Google Scholar
NAV 59/INF.2. (2013). Development of an e-navigation Strategy Implementation Plan. Report on research project in the field of e-navigation submitted by Poland, IMO.Google Scholar
NCSR 1/6. (2014). Development of an e-Navigation Strategy Implementation Plan. Report of the Correspondence Group on e-navigation submitted by Norway, IMO.Google Scholar
NCSR 2/6. (2015). e-navigation Strategy Implementation Plan. Report of the Correspondence Group on Harmonization of Guidelines related to e-navigation submitted by Australia, IMO.Google Scholar
NCSR 2/INF.10. (2015). e-navigation Strategy Implementation Plan. A study on ship operator centred collision prevention and alarm system submitted by the Republic of Korea, IMO.Google Scholar
Pietrzykowski, Z. (2010). Maritime Intelligent Transport Systems, Communications in Computer and Information Science, 104, 455462.CrossRefGoogle Scholar
Pietrzykowski, Z., Banaś, P., Wójcik, A. and Szewczuk, T. (2013). Communication Automation in Maritime Transport, Monograph Navigational Problems - Marine Navigation and Safety of Sea transportation, 287291.Google Scholar
Pietrzykowski, Z., Banaś, P., Wójcik, A. and Szewczuk, T. (2014a). Information Exchange Automation in Maritime Transport, International Journal on Marine Navigation and Safety of Sea Transportation, 8, 189193.Google Scholar
Pietrzykowski, Z., Borkowski, P. and Wołejsza, P. (2012). Marine integrated navigational decision support system, Communications in Computer and Information Science, 329, 284292.Google Scholar
Pietrzykowski, Z., Magaj, J. and Maka, M. (2014b). Safe Ship Trajectory Determination in the ENC Environment, Communications in Computer and Information Science, 471, 130136.Google Scholar
Pietrzykowski, Z., Magaj, J., Wołejsza, P. and Chomski, J. (2010). Fuzzy logic in the navigational decision support process onboard a sea-going vessel, Lecture Notes in Computer Science, 6113, 185193.Google Scholar
Pietrzykowski, Z. and Uriasz, J. (2009). The ship domain – a criterion of navigational safety assessment in an open sea area. Journal of Navigation, 62, 93108.Google Scholar
Qingyang, X., Chuang, Z. and Ning, W. (2014). Multiobjective Optimization Based Vessel Collision Avoidance Strategy Optimization, Mathematical Problems in Engineering, Volume, Article ID 914689.Google Scholar
Śmierzchalski, R., Kuczkowski, Ł., Kolendo, P. and Jaworski, B. (2013). Distributed Evolutionary Algorithm for Path Planning in Navigation Situation, TransNav, The International Journal on Marine Navigation and Safety of Sea Transportation, 7(2), 293300,CrossRefGoogle Scholar
Śmierzchalski, R. and Michalewicz, Z. (2000). Modelling of a ship trajectory in collision situations at sea by evolutionary algorithm. IEEE Transaction on Evolutionary Computation , 4(3), 227244.Google Scholar
Stateczny, A. and Kazimierski, W. (2008). Determining manoeuvre detection threshold of GRNN filter in the process of tracking in marine navigational radars, Proceedings of the International Radar Symposium, 242–245.Google Scholar
Stawicki, K. (2008). Modelling of last minute manoeuvre in collision situation at sea, Scientific Journal of Gdynia Maritime University, 22, 8091.Google Scholar
Szłapczyńska, J. (2013), Multicriteria Evolutionary Weather Routing Algorithm in Practice. TransNav, The International Journal on Marine Navigation and Safety of Sea Transportation, 7(1), 6165.Google Scholar
Szłapczyński, R. (2011). Evolutionary Sets of Safe Ship Trajectories: A New Approach to Collision Avoidance, Journal of Navigation, 64(1), 169181.Google Scholar
Tzannatos, E. and Kokotos, D. (2009). Analysis of accidents in Greek shipping during the pre-and post-ISM period. Marine Policy, 33(4), 679684.Google Scholar
Wang, Y. and Chin, H-Ch. (2016). An Empirically-Calibrated Ship Domain as a Safety Criterion for Navigation in Confined Waters. Journal of Navigation, 69(2), 257276.Google Scholar
Weintrit, A. (2009). The Electronic Chart Display and Information System (ECDIS): An Operational Handbook, CRC Press.Google Scholar
Wójcik, A., Banaś, P. and Pietrzykowski, Z. (2014). The schema of inference processes in a preliminary identification of navigational situation in maritime transport, Communications in Computer and Information Science, 471, 304312.Google Scholar
Wołejsza, P. (2013). Functionality of navigation decision supporting system – NAVDEC. Navigational Problems, 1, 4346.Google Scholar
Wołejsza, P. (2014). Navigation decision supporting system (NAVDEC) – testing in real condition, Annual of Navigation, 21, 177186.Google Scholar
Wołejsza, P., Magaj, J. and Gralak, R. (2013). Navigation Decision Supporting System (NAVDEC) - testing on full mission simulator. Annual of Navigation, 20, 149162.Google Scholar