Journal is indexed in following databases:



2023 Journal Impact Factor - 0.7
2023 CiteScore - 1.4



HomePage
 




 


 

ISSN 2083-6473
ISSN 2083-6481 (electronic version)
 

 

 

Editor-in-Chief

Associate Editor
Prof. Tomasz Neumann
 

Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
www http://www.transnav.eu
e-mail transnav@umg.edu.pl
A Nature Inspired Collision Avoidance Algorithm for Ships
1 Gdynia Maritime University, Gdynia, Poland
Times cited (SCOPUS): 1
ABSTRACT: Nature inspired algorithms are regarded as a powerful tool for solving real life problems. They do not guarantee to find the globally optimal solution, but can find a suboptimal, robust solution with an acceptable computational cost. The paper introduces an approach to the development of collision avoidance algorithms for ships based on the firefly algorithm, classified to the swarm intelligence methods. Such algorithms are inspired by the swarming behaviour of animals, such as e.g. birds, fish, ants, bees, fireflies. The description of the developed algorithm is followed by the presentation of simulation results, which show, that it might be regarded as an efficient method of solving the collision avoidance problem. Such algorithm is intended for use in the Decision Support System or in the Collision Avoidance Module of the Autonomous Navigation System for Maritime Autonomous Surface Ships.
REFERENCES
M. Dorigo and L. M. Gambardella, "Ant colonies for the travelling salesman problem," Biosystems, Vol. 43 (2), pp. 73-81, 1997, doi: 10.1016/S0303-2647(97)01708-5. - doi:10.1016/S0303-2647(97)01708-5
H. Hernández and C. Blum, "Distributed graph coloring: an approach based on the calling behavior of Japanese tree frogs," Swarm Intelligence, vol. 6, pp. 117–150, 2012, doi: 10.1007/s11721-012-0067-2. - doi:10.1007/s11721-012-0067-2
D. Karaboga and B. Gorkemli, "A combinatorial Artificial Bee Colony algorithm for traveling salesman problem," 2011 International Symposium on Innovations in Intelligent Systems and Applications, Istanbul, Turkey, 2011, pp. 50-53, doi: 10.1109/INISTA.2011.5946125. - doi:10.1109/INISTA.2011.5946125
C. Blum, M. Yabar and M. J. Blesa, "An ant colony optimization algorithm for DNA sequencing by hybridization," Computers & Operations Research, Vol. 35, No. 11, pp. 3620–3635, 2008, doi: 10.1016/j.cor.2007.03.007. - doi:10.1016/j.cor.2007.03.007
T.T. Mac, C. Copot, D.T. Tran, R. De Keyser, "Heuristic approaches in robot path planning: A survey," Robotics and Autonomous Systems, Vol. 86, pp. 13-28, 2016, doi: 10.1016/j.robot.2016.08.001. - doi:10.1016/j.robot.2016.08.001
M. Sugisaka and X. Fan, "An effective search method for NN-based face detection using PSO," SICE 2004 Annual Conference, Sapporo, Japan, 2004, pp. 2742-2745, vol. 3.
R. Xu, G. C. Anagnostopoulos and D. C. Wunsch, "Multiclass cancer classification using semi-supervised ellipsoid art-map and particle swarm optimization with gene expression data," in The IEEE/ACM Transactions on Computational Biology and Bioinformatics, pp. 65 – 77, 2007. - doi:10.1109/TCBB.2007.1009
A. Kaveh, T. Bakhshpoori and M. Ashoory, "An efficient optimization procedure based on cuckoo search algorithm for practical design of steel structures," International Journal of Optimization in Civil Engineering, Vol. 2, pp. 1-14, 2012.
X.-S. Yang, Z. Cui, R. Xiao, A. H. Gandomi and M. Karamanoglu, Eds., Swarm Intelligence and Bio-Inspired Computation: Theory and Applications (1st. ed.). Netherlands: Elsevier Science Publishers B. V., 2013.
M. Tomera, "Ant colony optimization algorithm applied to ship steering control," Procedia Computer Science, Vol. 35, pp. 83-92, Proceedings of the 18th Annual Conference on Knowledge-Based and Intelligent Information & Engineering Systems, KES-2014 Gdynia, Poland, September 15-17, 2014, doi: 10.1016/j.procs.2014.08.087. - doi:10.1016/j.procs.2014.08.087
M. C. Tsou and C. K. Hsueh, "The study of ship collision avoidance route planning by ant colony algorithm," Journal of Marine Science and Technology, Vol. 18 (5), pp. 746–756, 2010, doi: 10.51400/2709-6998.1929. - doi:10.51400/2709-6998.1929
A. Lazarowska, "Ship’s trajectory planning for collision avoidance at sea based on ant colony optimisation," Journal of Navigation, Vol. 68, pp. 291–307, 2015, doi: 10.1017/S0373463314000708. - doi:10.1017/S0373463314000708
C. Li-Jia and H. Li-Wen, "Ship Collision Avoidance Path Planning by PSO Based on Maneuvering Equation," in Future Wireless Networks and Information Systems, Lecture Notes in Electrical Engineering, Vol. 144, Y. Zhang, Ed., Springer, Berlin, Heidelberg, 2012, doi:10.1007/978-3-642-27326-1_87. - doi:10.1007/978-3-642-27326-1_87
M. Tomera, "Swarm intelligence applied to identification of nonlinear ship steering model," 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), Gdynia, Poland, 2015, pp. 133-139, doi: 10.1109/CYBConf.2015.7175920. - doi:10.1109/CYBConf.2015.7175920
G. Wu, T. Xu, Y. Sun and J. Zhang, "Review of multiple unmanned surface vessels collaborative search and hunting based on swarm intelligence," International Journal of Advanced Robotic Systems, Vol. 19(2), 2022, doi:10.1177/17298806221091885. - doi:10.1177/17298806221091885
J. Lisowski, "Review of Ship Collision Avoidance Guidance Algorithms Using Remote Sensing and Game Control," Remote Sensing, Vol. 14, pp. 4928, 2022, doi:10.3390/rs14194928. - doi:10.3390/rs14194928
M. Mohamed-Seghir, K. Kula and A. Kouzou, "Artificial Intelligence-Based Methods for Decision Support to Avoid Collisions at Sea," Electronics, Vol. 10(19), pp. 2360, 2021, doi:10.3390/electronics10192360. - doi:10.3390/electronics10192360
J. Lisowski, "Artificial Intelligence Methods in Safe Ship Control Based on Marine Environment Remote Sensing," Remote Sensing, Vol. 15, pp. 203, 2023, doi:10.3390/rs15010203. - doi:10.3390/rs15010203
R. Szłapczyński and H. Ghaemi, "Framework of an Evolutionary Multi-Objective Optimisation Method for Planning a Safe Trajectory for a Marine Autonomous Surface Ship," Polish Maritime Research, Vol.26 (Issue 4), pp. 69-79, 2019, doi:10.2478/pomr-2019-0068. - doi:10.2478/pomr-2019-0068
G. Wei and W. Kuo, "COLREGs-Compliant Multi-Ship Collision Avoidance Based on Multi-Agent Reinforcement Learning Technique," Journal of Marine Science and Engineering, Vol. 10(10), pp. 1431, 2022, doi:10.3390/jmse10101431. - doi:10.3390/jmse10101431
L. Jiang, L. An, X. Zhang, C. Wang and X. Wang, "A human-like collision avoidance method for autonomous ship with attention-based deep reinforcement learning," Ocean Engineering, Vol. 264, pp. 112378, 2022, doi: 10.1016/j.oceaneng.2022.112378. - doi:10.1016/j.oceaneng.2022.112378
Z. Zhu, H. Lyu, J. Zhang and Y. Yin, "An Efficient Ship Automatic Collision Avoidance Method Based on Modified Artificial Potential Field," Journal of Marine Science and Engineering, Vol. 10(1), pp. 3, 2022, doi: 10.3390/jmse10010003. - doi:10.3390/jmse10010003
Y. Cho, J. Han and J. Kim, "Efficient COLREG-Compliant Collision Avoidance in Multi-Ship Encounter Situations," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 1899-1911, March 2022, doi: 10.1109/TITS.2020.3029279. - doi:10.1109/TITS.2020.3029279
M. Rybczak and K. Podgórski, "Pareto Effect of LMI for Ship Propulsion," Applied Sciences, Vol. 11(16), pp. 7297, 2021, doi:10.3390/app11167297. - doi:10.3390/app11167297
M. Tomera, "Path Controller for Ships with Switching Approach," in Advanced, Contemporary Control. Advances in Intelligent Systems and Computing, Vol. 1196, A. Bartoszewicz, J. Kabziński, J. Kacprzyk, Eds., Springer, Cham, 2020, doi:10.1007/978-3-030-50936-1_126. - doi:10.1007/978-3-030-50936-1_126
H. N. Esfahani and R. Szlapczynski, "Robust-adaptive dynamic programming-based time-delay control of autonomous ships under stochastic disturbances using an actor-critic learning algorithm, " Journal of Marine Science and Technology, Vol. 26, pp. 1262–1279, 2021, doi:10.1007/s00773-021-00813-1. - doi:10.1007/s00773-021-00813-1
A. Witkowska and T. Rynkiewicz, "Dynamically Positioned Ship Steering Making Use of Backstepping Method and Artificial Neural Networks," Polish Maritime Research, Vol. 25(4) pp. 5-12, 2018, doi:10.2478/pomr-2018-0126. - doi:10.2478/pomr-2018-0126
M. Dorigo and T. Stützle, Ant Colony Optimization. Cambridge, Massachusetts, London, England: The MIT Press, 2004. - doi:10.7551/mitpress/1290.001.0001
XS. Yang, "Firefly Algorithms for Multimodal Optimization," in Stochastic Algorithms: Foundations and Applications, SAGA 2009, Lecture Notes in Computer Science, Vol. 5792, O. Watanabe, T. Zeugmann, Eds., Springer, Berlin, Heidelberg, 2009, doi:10.1007/978-3-642-04944-6_14. - doi:10.1007/978-3-642-04944-6_14
A. Lazarowska, "Ship's Trajectory Planning for Collision Avoidance at Sea Based on Ant Colony Optimisation," The Journal of Navigation, Vol. 68(2), pp. 291-307, 2015, doi:10.1017/S0373463314000708. - doi:10.1017/S0373463314000708
Citation note:
Lazarowska A.: A Nature Inspired Collision Avoidance Algorithm for Ships. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 17, No. 2, doi:10.12716/1001.17.02.10, pp. 341-346, 2023
Authors in other databases:

Other publications of authors:


File downloaded 128 times








Important: TransNav.eu cookie usage
The TransNav.eu website uses certain cookies. A cookie is a text-only string of information that the TransNav.EU website transfers to the cookie file of the browser on your computer. Cookies allow the TransNav.eu website to perform properly and remember your browsing history. Cookies also help a website to arrange content to match your preferred interests more quickly. Cookies alone cannot be used to identify you.
Akceptuję pliki cookies z tej strony