Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-10T13:57:16.551Z Has data issue: false hasContentIssue false

Decision Support from Genetic Algorithms for Ship Collision Avoidance Route Planning and Alerts

Published online by Cambridge University Press:  01 December 2009

Ming-Cheng Tsou*
Affiliation:
(National Taiwan Ocean University, Department of Transportation and Navigation Science)
Sheng-Long Kao
Affiliation:
(National Taiwan Ocean University, Department of Transportation and Navigation Science)
Chien-Min Su
Affiliation:
(National Taiwan Ocean University, Department of Electrical Engineering)

Abstract

When an officer of the watch (OOW) faces complicated marine traffic, a suitable decision support tool could be employed in support of collision avoidance decisions, to reduce the burden and greatly improve the safety of marine traffic. Decisions on routes to avoid collisions could also consider economy as well as safety. Through simulating the biological evolution model, this research adopts the genetic algorithm used in artificial intelligence to find a theoretically safety-critical recommendation for the shortest route of collision avoidance from an economic viewpoint, combining the international regulations for preventing collisions at sea (COLREGS) and the safety domain of a ship. Based on this recommendation, an optimal safe avoidance turning angle, navigation restoration time and navigational restoration angle will also be provided. A Geographic Information System (GIS) will be used as the platform for display and operation. In order to achieve advance notice of alerts and due preparation for collision avoidance, a Vessel Traffic Services (VTS) operator and the OOW can use this system as a reference to assess collision avoidance at present location.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2009

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

Back, T. (1996). Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford University Press.CrossRefGoogle Scholar
Beaubouef, T. and Breckenridge, J. (2000). Real-world issues and applications for real-time geographic information systems (RT-GIS). The Journal of Navigation, 53, 124131.CrossRefGoogle Scholar
Cockroft, A. N. (1984). Collision at Sea. Safety at Sea, 1719.Google Scholar
Colley, B. A. (1984). A marine traffic flow and collision avoidance computer simulation. The Journal of Navigation, 37(2), 232250.CrossRefGoogle Scholar
Davis, P. V.Dove, M. J. and Stockel, C. (1980). A computer simulation of marine traffic using domains and arenas. The Journal of Navigation, 33(2), 215222.CrossRefGoogle Scholar
Davis, P. V., Dove, M. J. and Stockel, C. T. (1982). A computer simulation of multi-ship encounters. The Journal of Navigation, 35(2), 347352.CrossRefGoogle Scholar
Goodwin, E. M. (1975). A statistical study of ship domains. The Journal of Navigation, 28(3), 328344.CrossRefGoogle Scholar
Ito, M., Zhang, F. and Yoshida, N. (1999). Collision avoidance of ship with genetic algorithm. Proceedings of 1999 IEEE International Conference on Control Applications, 17911796.CrossRefGoogle Scholar
Jones, K. D. (1978). Decision Making when using collision avoidance system. The Journal of Navigation, 31(2), 173180.Google Scholar
Kao, S.-L., Lee, K.-T., Chang, K.-Y. and Ko, M.-D. (2007). A fuzzy logic method for collision avoidance in vessel traffic service. The Journal of Navigation, 60, 1731.CrossRefGoogle Scholar
Lamb, W. G. P. (1985). The calculation of marine collision danger. The Journal of Navigation, 38(3), 173180.CrossRefGoogle Scholar
Lee, S. M., Kwon, K. Y. and Joh, J. (2004). A fuzzy logic for autonomous navigation of marine vehicles satisfying COLREG guidelines. International Journal Of Control Automation And Systems, 2, 171181.Google Scholar
Li, L.-N., Yang, S.-H., Cao, B.-G. and Li, Z.-F. (2006). A Summary of Studies on the Automation of Ship Collision Avoidance Intelligence (in Chinese). Journal of Jimei University, China, 11(2), 188192.Google Scholar
Lin, H. S., J, X. and Michalewicz, Z. (1994). Evolutionary algorithm for path planning in mobile robot environment. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference, 211216.Google Scholar
Liu, D., Wu, Z. and Jia, C. (2004). Decision making model of dCPA, tCPA and object's movement parameter (in Chinese). Journal of Dalian Maritime University, China, 30(1), 2225.Google Scholar
Liu, Y.-A., Liu, J., Wu, J. and Zou, X.-H. (2007). Application of simulated annealing algorithm in turning angle to avoid collision between ships (in Chinese). Shipbuilding of China, China, 48(4), 5357.Google Scholar
Smierzchalski, R. and Michalewicz, Z. (2000). Modeling of ship trajectory in collision situations by an evolutionary algorithm. IEEE Transactions On Evolutionary Computation, 4, 227241.CrossRefGoogle Scholar
Statheros, T., Howells, G. and McDonald-Maier, K. (2008). Autonomous ship collision avoidance navigation concepts, technologies and techniques. The Journal of Navigation, 61, 129142.CrossRefGoogle Scholar
Zhu, X., Xu, H. and Lin, J. (2001). Domain and its model based on neural networks. The Journal of Navigation, 54(1), 97–103.CrossRefGoogle Scholar
Zou, X.-H. and Ni, T.-Q. (2006). Applied research of genetic algorithm in the amplitude decision of ship steering and collision avoidance (in Chinese). Shipboard Electronic Countermeasure, China, 29(3), 6669.Google Scholar