Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-07T22:33:55.214Z Has data issue: false hasContentIssue false
  • English
  • Français

Evolutionary Sets Of Safe Ship Trajectories: A New Approach To Collision Avoidance

Published online by Cambridge University Press:  26 November 2010

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

Abstract

The paper introduces a new method of solving multi-ship encounter situations for both open waters and restricted water regions. The method, called evolutionary sets of safe trajectories, combines some of the assumptions of game theory with evolutionary programming and aims to find optimal sets of safe trajectories of all ships involved in an encounter situation. In a two-ship encounter situation it enables the operator of an onboard collision-avoidance system to predict the most probable behaviour of a target and to plan the own manoeuvres in advance. In a multi-ship encounter the method may be used to help an operator of a VTS system to coordinate the manoeuvres of all ships. The paper contains a detailed description of collision-avoidance operators used by the evolutionary method and simulation examples of the method's results for digital maps.

Keywords

Marine navigationCollision avoidanceOptimal ship trajectoriesEvolutionary algorithms
Type
Research Article
Information
The Journal of Navigation , Volume 64 , Issue 1 , January 2011 , pp. 169 - 181
Copyright
Copyright © The Royal Institute of Navigation 2010

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

Cockroft, A.N., Lameijer, J.N.F. (1993): A Guide to Collision Avoidance Rules, Butterworth-Heinemann Ltd.Google Scholar
Coldwell, T.G. (1982): Marine Traffic Behaviour in restricted Waters, The Journal of Navigation, 36, 431–444.Google Scholar
Davis, P.V., Dove, M.J., Stockel, C.T. (1982). A Computer Simulation of multi-Ship Encounters. The Journal of Navigation, 35, 347–352.Google Scholar
Fuji, J., Tanaka, K. (1971). Traffic Capacity. The Journal of Navigation, 24, 543–552.Google Scholar
Goodwin, E. M. (1975). A Statistical Study of Ship Domains. The Journal of Navigation, 28, 329–341.Google Scholar
Lisowski, J. (2005): Dynamic games methods in navigator decision support system for safety navigation, Proceedings of the European Safety and Reliability Conference, vol. 2, 1285–1292.Google Scholar
Szlapczynski, R. (2006a). A new method of ship routing on raster grids, with turn penalties and collision avoidance, The Journal of Navigation, 59, 27–42.Google Scholar
Szlapczynski, R. (2006b). A unified measure of collision risk derived from the concept of a ship domain, The Journal of Navigation, 59, 477–490.Google Scholar
Szlapczynski, R. (2008): A new method of planning collision avoidance manoeuvres for multi target encounter situations, The Journal of Navigation, 61, 307–321.Google Scholar
Szlapczynski, R. (2009). Solving multi-ship encounter situations by evolutionary sets of cooperating trajectories, Marine Navigation and Safety of Sea Transportation, CRC Press/Taylor & Francis Group/Balkema, 437–442.Google Scholar
Smierzchalski, R., Michalewicz, Z. (2000). Modeling of ship trajectory in collision situations by an evolutionary algorithm. IEEE Transactions On Evolutionary Computation, 4, 227–241.Google Scholar
Tsou, M.-C., Kao, S.-L., Su, C.-M. (2010). Decision Support from Genetic Algorithms for Ship Collision Avoidance Route Planning and Alerts, The Journal of Navigation, 63, 167–182.Google Scholar
Zeng, X. (2003). Evolution of the safe path for ship navigation. Applied Artificial Intelligence, 17, 87–104.CrossRefGoogle Scholar