Fuzzy Reasoning as a Base for Collision Avoidance Decision Support System
Abstract
Despite the generally high qualifications of seafarers, many maritime accidents are caused by human error; such accidents include capsizing, collision, and fire, and often result in pollution. Enough concern has been generated that researchers around the world have developed the study of the human factor into an independent scientific discipline. A great deal of progress has been made, particularly in the area of artificial intelligence. But since total autonomy is not yet expedient, the decision support systems based on soft computing are proposed to support human navigators and VTS operators in times of crisis as well as during the execution of everyday tasks as a means of reducing risk levels.This paper considers a decision support system based on fuzzy logic integrated into an existing bridge collision avoidance system. The main goal is to determine the appropriate course of avoidance, using fuzzy reasoning.
References
IMO (1972) Convention on the international regulations for preventing collisions at sea (COLREG).
Statheros T, Howells G, McDonald-Maier K. Autonomous Vessel Collision Avoidance Navigation Concepts. Technologies and Techniques. The Journal of Navigation. 2008; 61:129-142
Perera, L.P., Carvalho, J.P., Guedes Soares, C.: Fuzzy logic based decision making system for collision avoidance of ocean navigation under critical collision conditions, Journal of Marine Science and Technology, Vol.16, 2011, pp. 84-99.
Perera, L.P., Carvalho, J.P., Guedes Soares, C.: Intelligent Ocean Navigation and Fuzzy – Bayesian Decision/ Action Formulation, Journal of Oceanic Engineering, Vol. 37, No. 2, 2012, pp. 204-218.
Smierzchalski, R.: Evolutionary-Fuzzy System of Safe Ship Steering in a Collision Situation at Sea, CIMCA–IAWTIC 2005, Vol.1, pp. 893-898, Vienna, Austria, November 2005.
Pietrzykowski, Z.: Multi-stage vessel control in a fuzzy environment. Methods of artificial intelligence and intelligent agents. Enhanced methods in computer security, biometric and artificial intelligence systems. Chapter 3, 2005, pp. 285-299.
Pietrzykowski, Z., Magaj, J., Wolejsza, P. and Chomski, J.: Fuzzy logic in the navigational decision support process onboard a sea–going vessel. ICAISC 2010, Part I, pp. 185-193, Zakopane, Poland, June 2010.
Lee, S., Kwon, K., Joh, J.: A Fuzzy Logic for Autonomous Navigation of Marine Vehicles Satisfying COLREGS Guidelines, International Journal of Control, Automation and Systems, Vol. 2, No. 2, 2004, pp. 171-181.
Szlapczynski, R., Szlapczynska, J.: COLREGS Compliance in Evolutionary Sets of Cooperating Ship Trajectories. Electronic journal of international group on reliability, Reliability: Theory and Applications, Vol. 2, No.1, 2011, pp. 127-137.
Liu, Y., Yang, C., Du, X.: A CBR-Based Approach for Ship Collision Avoidance. New Frontiers in Applied Artificial Intelligence - 21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 687-697, Wroclaw, Poland, June 2008.
Liu, Y., Wang, S., Du, X.: A Multi-Agent Information Fusion Model for Ship Collision Avoidance. Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, pp. 6-11, Kunming, China, July 2008.
Kristiansen, S.: Maritime Transportation: Safety Management and Risk Analysis, Elsevier Butterworth-Heinemann, Oxford, 2004.
Cockcroft, A.N., Lameijer, J. N.F.: A guide to collision avoidance rules, 5th edition, Butterworth-Heinemann Ltd., Oxford, 1996.
Naeem, W., Irwin, G.W.: An Automatic Collision Avoidance Strategy for Unmanned Surface Vehicles, Communications in Computer and Information Science, Vol. 98, Part 4, 2010, pp. 184–191.
Sušanj, J.: Navigacijski radar. Sveučilište u Rijeci. Pomorski fakultet u Rijeci, 2006, (in Croatian).
Goodwin, E.M.: A statistical study of vessel domains. The Journal of Navigation, 28, 1975, pp. 329-341.
Švetak, J.: Estimation of ship domain zone. Promet – Traffic&Transportation, Vol. 21, No. 1, 2009, pp. 1-6.
Wang, N.: An Intelligent Spatial Collision Risk Based on the Quaternion Ship Domain. The Journal of Navigation, 63, 2010, pp. 733-749.
Pietrzykowski, Z., Uriasz, J.: The Ship Domain – A Criterion of Navigational Safety Assessment in an Open Sea Area, The Journal of Navigation, 62, 2009, pp. 93-108.
Wang, N., Meng, X., Xu, Q., Wang, Z.: A Unified Analytical Framework for Vessel Domains. The Journal of Navigation, 62, 2010, pp. 643-655.
Teodorović, D., Vukadinović, K.: Traffic Control and Transport Planning: A Fuzzy Sets and Neural Networks Approach. Boston–Dordrecht–London: Kluwer Academic Publishers, 1998.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
