Hostname: page-component-745bb68f8f-mzp66 Total loading time: 0 Render date: 2025-01-08T01:27:54.443Z Has data issue: false hasContentIssue false
  • English
  • Français

Evolutionary Planning of Safe Ship Tracks in Restricted Visibility

Published online by Cambridge University Press:  26 September 2014

Rafal Szlapczynski
Rafal Szlapczynski*
Affiliation:
(Gdansk University of Technology, Poland)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

The paper presents the continuation of the author's research on ship track planning by means of Evolutionary Algorithms (EA). The presented method uses EA to search for an optimal set of safe tracks for all ships involved in an encounter. Until now the method assumed good visibility – compliance with standard rules of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS, 1972). However, in restricted visibility, when Rule 19 applies instead of Rules 11 to 18, the problem is a different one. Therefore this paper introduces the extended method, with a focus on compliance with Rule 19 and its implications. It includes descriptions of detecting, penalizing and eliminating violations of Rule 19. The method has been implemented and the paper contains sample results of computer simulation tests carried out for ship encounters in restricted visibility in both open and restricted waters. They confirm the effectiveness of the chosen approach and suggest that the method could be applied in on board decision support systems.

Keywords

Restricted visibilityEvolutionary algorithmsCollision avoidance manoeuvresTrack planning
Type
Research Article
Information
The Journal of Navigation , Volume 68 , Issue 1 , January 2015 , pp. 39 - 51
Copyright
Copyright © The Royal Institute of Navigation 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Chang, K. Y., Jan, G.E. and Parberry, I. (2003). A Method for Searching Optimal Routes with Collision Avoidance on Raster Charts. The Journal of Navigation, 56, 371–384.CrossRefGoogle Scholar
Cheng, X. and Liu, Z. (2007). Trajectory Optimization for Ship Navigation Safety Using Genetic Annealing Algorithm. Proceedings of ICNC 2007 Third International Conference on Natural Computation, 4, 385–392.Google Scholar
Cockcroft, A.N. and Lameijer, J.N.F. (2011). A Guide to Collision Avoidance Rules. Butterworth-Heinemann.Google Scholar
Coldwell, T.G. (1983). Marine Traffic Behaviour in Restricted Waters. The Journal of Navigation, 36, 431–444.CrossRefGoogle Scholar
COLREGS. (1972) (with amendments adopted from December 2009). Convention on the International Regulations for Preventing Collisions at Sea. International Maritime Organization, London.Google Scholar
Ito, M., Feifei, Z. and Yoshida, N. (1999). Collision avoidance control of ship with genetic algorithm. Proceedings of the 1999 IEEE International Conference on Control Applications, Vol. 2, 1791–1796.CrossRefGoogle Scholar
Lisowski, J. (2007). The Dynamic Game Models of Safe Navigation, International Journal on Marine Navigation and Safety of Sea Transportation, 1, no. 1.Google Scholar
Lisowski, J. (2012). The Sensitivity of Safe Ship Control in Restricted Visibility at Sea, International Journal on Marine Navigation and Safety of Sea Transportation, 6, no. 1.Google Scholar
Michalewicz, Z. and Fogel, D.B. (2004). How To Solve It: Modern Heuristics. Springer-Verlag.Google Scholar
Smierzchalski, R. and Michalewicz, Z. (2000). Modelling of a Ship Trajectory in Collision Situations at Sea by Evolutionary Algorithm, IEEE Transactions on Evolutionary Computation, 3(4), 227–241.Google Scholar
Statheros, T., Howells, G. and McDonald-Maier, K. (2008). Autonomous Ship Collision Avoidance Navigation Concepts, Technologies and Techniques, The Journal of Navigation, 61, 129–142.CrossRefGoogle Scholar
Szlapczynski, R. (2012). Evolutionary Sets of Safe Ship Trajectories Within Traffic Separation Schemes. The Journal of Navigation, 66, 65–81.Google Scholar
Tam, C.K. and Bucknall, R. (2010). Path-Planning Algorithm for Ships in Close-Range Encounters. Journal of Marine Science Technology, 15, 395–407.Google Scholar
Tsou, M. C. and Hsueh, C. K. (2010a). The Study of Ship Collision Avoidance Route Planning by Ant Colony Algorithm. Journal of Marine Science and Technology, 18(5), 746–756.CrossRefGoogle Scholar
Tsou, M. C., Kao, S.-L. and Su, C.-M. (2010b). Decision Support from Genetic Algorithms for Ship Collision Avoidance Route Planning and Alerts. The Journal of Navigation, 63, 167–182.Google Scholar
Xue, Y., Lee, B.S. and Han, D. (2009). Automatic Collision Avoidance of Ships. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 33–46.Google Scholar
Yang, L.L., Cao, S.- H. and Li, B.Z. (2006). A Summary of Studies on the Automation of Ship Collision Avoidance Intelligence. Journal of Jimei University (Natural Science), 2.Google Scholar
Zeng, X. (2003). Evolution of the Safe Path for Ship Navigation. Applied Artificial Intelligence, 17, 87–104.Google Scholar