Particle swarm optimization-based collision avoidance
Yıl: 2019 Cilt: 27 Sayı: 3 Sayfa Aralığı: 2137 - 2155 Metin Dili: İngilizce DOI: 10.3906/elk-1808-63 İndeks Tarihi: 15-05-2020
Particle swarm optimization-based collision avoidance
Öz: Collision risk assessment and collision avoidance of vessels have always been an important topic in oceanengineering. Decision support systems are increasingly becoming the focus of many studies in the maritime industrytoday as vessel accidents are often caused by human error. This study proposes an anticollision decision support systemthat can determine surrounding obstacles by using the information received from radar systems, obtain the position andspeed of obstacles within a certain time period, and suggest possible routes to prevent collisions. In this study we usea neural network to predict the subsequent positions of surrounding vessels, a fuzzy logic system to obtain the risk ofcollision, and a particle swarm optimization algorithm to find the safe and shortest path for collision avoidance.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
- [1] Apostol-Mates R, Barbu A. Human error-the main factor in marine accidents. “Mircea cel Batran” Naval Academy Scientific Bulletin 2016; 19 (2): 451-454. doi: 10.21279/1454-864X-16-I2-068
- [2] Liu Y, Shi C. A fuzzy-neural inference network for ship collision avoidance. In: International Conference on Machine Learning and Cybernetics Conference; Guangzhou, China; 2005. pp. 4754-4759. doi: 10.1109/ICMLC.2005.1527778
- [3] Zhuo Y, Hearn GE. A ship based intelligent anti-collision decision-making support system utilizing trial manoeuvres. In: Chinese Control and Decision Conference; Yantai China; 2008. pp. 3982-3987. doi: 10.1109/CCDC.2008.4598079
- [4] Ahn J, Rhee K, You Y. A study on the collision avoidance of a ship using neural networks and fuzzy logic. Applied Ocean Research 2012; 37: 162-173. doi: 10.1016/j.apor.2012.05.008
- [5] eSimsir U, Amasyali M, Bal M, Celebi U, Ertugrul S. Decision support system for collision avoidance of vessels. Applied Soft Computing 2014; 25: 369-378. doi: 10.1016/j.asoc.2014.08.067
- [6] Perera LP, Carvalho J, Soares G. Fuzzy logic based decision making system for collision avoidance of ocean navigation under critical collision conditions. Marine Science and Technology 2011; 16 (1): 84-99. doi: 10.1007/s00773- 010-0106-x
- [7] Perera LP, Carvalho J, Soares G. Intelligent ocean navigation and fuzzy-Bayesian decision/action formulation. IEEE Journal of Oceanic Engineering 2012; 37 (2): 204-219. doi: 10.1109/JOE.2012.2184949
- [8] Zhou K, Chen J, Liu X. Optimal collision-avoidance manoeuvres to minimise bunker consumption under the twoship crossing situation. Journal of Navigation 2018; 71 (1): 151-168. doi: 10.1017/S0373463317000534
- [9] Smierzchalski R, Michalewicz Z. Modeling of ship trajectory in collision situations by an evolutionary algorithm. IEEE Transactions on Evolutionary Computation 2000; 4 (3): 227-241. doi: 10.1109/4235.873234
- [10] Zeng X, Ito M. Planning a collision avoidance model for ship using genetic algorithm. In: IEEE International Conference on Systems, Man and Cybernetics; Tucson, AZ, USA; 2001. pp. 2355-2360. doi: 10.1109/ICSMC.2001.972909
- [11] Ito M, Zhang F, Yoshida N. Collision avoidance control of ship with genetic algorithm. In: IEEE International Conference on Control Applications; Kohala Coast, HI, USA; 1999. pp. 1791-1796. doi: 10.1109/CCA.1999.801243
- [12] Tsou M, Kao S, Su C. Decision support from genetic algorithms for ship collision avoidance route planning and alerts. Journal of Navigation 2010; 63 (1): 167-182. doi: 10.1017/S037346330999021X
- [13] Smierzchalski R. Evolutionary trajectory planning of ships in navigation traffic areas. Journal of Marine Science and Technology 1999; 4 (1): 1–6. doi: 10.1007/s007730050001
- [14] Tsou M, Hsueh C. The study of ship collision avoidance route planning by ant colony algorithm. Journal of Marine Science and Technology 2010; 18 (5): 746-756.
- [15] Lazarowska A. Ant colony optimization based navigational decision support system. In: 18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems - KES2014; Gdynia, Poland; 2014. pp. 1013-1022. doi: 10.1016/j.procs.2014.08.187
- [16] Chen P, Shi G, Liu S, Zhang Y. Decision support based on artificial fish swarm for ship collision avoidance from AIS data. In: International Conference on Machine Learning and Cybernetics; Chengdu, China; 2018. pp. 31-36. doi: 10.1515/pomr-2016-0058
- [17] Fang M, Tsai K, Fang C. A simplified simulation model of ship navigation for safety and collision avoidance in heavy traffic areas. Journal of Navigation 2018; 71 (4): 837-860. doi: 10.1017/S0373463317000923
- [18] Fossen TI. Mathematical models of ships and underwater vehicles. In: Baillieul J, Samad T (editors). Encyclopedia of Systems and Control. London, UK: Springer London, 2013. pp. 1-9.
- [19] Marquardt DW. An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics 1963; 11 (2): 431-441. doi: 10.1137/0111030
- [20] Zadeh LA. Fuzzy sets. Information and Control 1965; 8 (3): 338-353. doi: 10.1016/S0019-9958(65)90241-X
- [21] Kennedy J, Eberhart R. Particle swarm optimization. Neural Networks 1995; 4: 1942-1948. doi: 10.1109/ICNN.1995.488968
- [22] Mohemmed A, Sahoo N, Geok T. Solving shortest path problem using particle swarm optimization. Applied Soft Computing 2008; 8 (4): 1643-1653. doi: 10.1016/j.asoc.2008.01.002
| APA | inan t, Baba A (2019). Particle swarm optimization-based collision avoidance. , 2137 - 2155. 10.3906/elk-1808-63 |
| Chicago | inan timur,Baba Ahmet fevzi Particle swarm optimization-based collision avoidance. (2019): 2137 - 2155. 10.3906/elk-1808-63 |
| MLA | inan timur,Baba Ahmet fevzi Particle swarm optimization-based collision avoidance. , 2019, ss.2137 - 2155. 10.3906/elk-1808-63 |
| AMA | inan t,Baba A Particle swarm optimization-based collision avoidance. . 2019; 2137 - 2155. 10.3906/elk-1808-63 |
| Vancouver | inan t,Baba A Particle swarm optimization-based collision avoidance. . 2019; 2137 - 2155. 10.3906/elk-1808-63 |
| IEEE | inan t,Baba A "Particle swarm optimization-based collision avoidance." , ss.2137 - 2155, 2019. 10.3906/elk-1808-63 |
| ISNAD | inan, timur - Baba, Ahmet fevzi. "Particle swarm optimization-based collision avoidance". (2019), 2137-2155. https://doi.org/10.3906/elk-1808-63 |
| APA | inan t, Baba A (2019). Particle swarm optimization-based collision avoidance. Turkish Journal of Electrical Engineering and Computer Sciences, 27(3), 2137 - 2155. 10.3906/elk-1808-63 |
| Chicago | inan timur,Baba Ahmet fevzi Particle swarm optimization-based collision avoidance. Turkish Journal of Electrical Engineering and Computer Sciences 27, no.3 (2019): 2137 - 2155. 10.3906/elk-1808-63 |
| MLA | inan timur,Baba Ahmet fevzi Particle swarm optimization-based collision avoidance. Turkish Journal of Electrical Engineering and Computer Sciences, vol.27, no.3, 2019, ss.2137 - 2155. 10.3906/elk-1808-63 |
| AMA | inan t,Baba A Particle swarm optimization-based collision avoidance. Turkish Journal of Electrical Engineering and Computer Sciences. 2019; 27(3): 2137 - 2155. 10.3906/elk-1808-63 |
| Vancouver | inan t,Baba A Particle swarm optimization-based collision avoidance. Turkish Journal of Electrical Engineering and Computer Sciences. 2019; 27(3): 2137 - 2155. 10.3906/elk-1808-63 |
| IEEE | inan t,Baba A "Particle swarm optimization-based collision avoidance." Turkish Journal of Electrical Engineering and Computer Sciences, 27, ss.2137 - 2155, 2019. 10.3906/elk-1808-63 |
| ISNAD | inan, timur - Baba, Ahmet fevzi. "Particle swarm optimization-based collision avoidance". Turkish Journal of Electrical Engineering and Computer Sciences 27/3 (2019), 2137-2155. https://doi.org/10.3906/elk-1808-63 |
