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Ship's Trajectory Planning for Collision Avoidance at Sea Based on Ant Colony Optimisation

  • Agnieszka Lazarowska (a1)

Abstract

Swarm Intelligence (SI) constitutes a rapidly growing area of research. At the same time trajectory planning in a dynamic environment still constitutes a very challenging research problem. This paper presents a new approach to path planning in dynamic environments based on Ant Colony Optimisation (ACO). Assumptions, a concise description of the method developed and results of real navigational situations (case studies with comments) are included. The developed solution can be applied in decision support systems on board a ship or in an intelligent Obstacle Detection and Avoidance system, which constitutes a component of Unmanned Surface Vehicle (USV) Navigation, Guidance and Control systems.

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References

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Bonabeau, E., Dorigo, M. and Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, Inc.
Brand, M., Masuda, M., Wehner, N. and Xiao-Hua, Yu. (2010). Ant colony optimization algorithm for robot path planning. Proceedings of the International Conference on Computer Design and Applications, 436–440.
Cockcroft, A. and Lameijer, J. (2012). A Guide to the Collision Avoidance Rules. Butterworth-Heinemann.
Dorigo, M. and Stutzle, T. (2004). Ant Colony Optimization. MIT Press Massachusetts Institute of Technology.
Escario, J. B., Jimenez, J. F. and Giron-Sierra, J. M. (2012). Optimisation of autonomous ship manoeuvres applying ant colony optimisation metaheuristic. Expert Systems with Applications, 39(11), 1012010139.
Lee, Joon-Woo, Lee, Ju-Jang (2010). Novel Ant Colony Optimization Algorithm with Path Crossover and Heterogeneous Ants for Path Planning. Proceedings of the IEEE International Conference on Industrial Technology, 559–564.
Lisowski, J. (2010). Sensitivity of Safe Game Ship Control on Base Information from ARPA Radar. Radar Technology, Kouemou, Guy (Ed.), InTech.
Mingxin, Yuan, Sun'an, Wang, Canyang, Wu and Kunpeng, Li (2010). Hybrid ant colony and immune network algorithm based on improved APF for optimal motion planning. Robotica, 28, 833846.
Smierzchalski, R. and Michalewicz, Z. (2000). Modelling of ship trajectory in collision situations by an evolutionary algorithm. IEEE Transactions on Evolutionary Computation, 4, 227241.
Tsou, Ming-Cheng and Hsueh, Chao-Kuang (2010). The study of ship collision avoidance route planning by ant colony algorithm. Journal of Marine Science and Technology, 18(5), 746756.
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The Journal of Navigation
  • ISSN: 0373-4633
  • EISSN: 1469-7785
  • URL: /core/journals/journal-of-navigation
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