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Warianty tytułu
Języki publikacji
Abstrakty
The paper presents a new approach for solving a path planning problem for ships in the environment with static and dynamic obstacles. The algorithm utilizes a heuristic method, classified to the group of Swarm Intelligence approaches, called the Ant Colony Optimization. The method is inspired by a collective behaviour of ant colonies. A group of agents - artificial ants searches through the solution space in order to find a safe, optimal trajectory for a ship. The problem is considered as a multi-criteria optimization task. The criteria taken into account during problem solving are: path safety, path length, the International Regulations for Preventing Collisions at Sea (COLREGs) compliance and path smoothness. The paper includes the description of the new multi-criteria ACO-based algorithm along with the presentation and discussion of simulation tests results.
Rocznik
Tom
Strony
31--36
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
autor
- Gdynia Maritime University, Gdynia, Poland
Bibliografia
- 1 Ahn J.H., Rhee K.P., You Y.J. 2012: A study on the collision avoidance of a ship using neural networks and fuzzy logic, Applied Ocean Research, Vol. 37, pp. 162–173.
- 2 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, Vol. 39 (11), pp. 10120–10139.
- 3 Fossen T.I. 2011: Handbook of Marine Craft Hydrodynamics and Motion Control, 1st ed., John Wiley & Sons, Ltd
- 4 Hornauer S., Hahn A. 2013: Towards Marine Collision Avoidance Based on Automatic Route Exchange, 9th IFAC Conference on Control Applications in Marine Systems, Vol. 46 (33), pp. 103–107.
- 5 Lazarowska A. 2013: Application of Ant Colony Optimization in Shipʹs Navigational Decision Support System, In: A. Weintrit (ed.), Navigational Problems, Marine Navigation and Safety of Sea Transportation, CRC Press/Balkema, London, UK, pp. 53–62.
- 6 Lisowski J. 2016a: Analysis of methods of determining the safe ship trajectory, TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 10, No. 2, pp. 223–228.
- 7 Lisowski J. 2016b: The sensitivity of state differential game vessel traffic model, Polish Maritime Research, Vol. 23, Issue 2, pp. 14–18.
- 8 Liu Y., Bucknall R. 2015: Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment, Ocean Engineering, Vol. 97, pp. 126–144.
- 9 Mohamed‐Seghir M. 2016: Computational Intelligence Method for Ship Trajectory Planning, Proceedings of the 21st International Conference on Methods and Models in Automation and Robotics, pp. 1–6
- 10 Naeem, W., Irwin, G., Yang, A. 2012: Colregs‐based collision avoidance strategies for unmanned surface vehicles, Mechatronics, Vol. 22, pp. 669–678
- 11 Perera L., J. Carvalho J., Guedes Soares C. 2011: Fuzzy logic based decision making system for collision avoidance of ocean navigation under critical collision conditions, Journal of Marine Science and Technology, Vol. 16 (1), pp. 84–99
- 12 Simsir U., Amasyali M.F., Bal M., Celebi U.B., Ertugrul S. 2014: Decision support system for collision avoidance of vessels, Applied Soft Computing, Vol. 25, pp. 369–378.
- 13 Szłapczyńska J. 2015: Data Acquisition in a Manoeuver Auto‐negotiation System, TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 9, No. 3, pp. 343–348.
- 14 Szłapczyński R., Szłapczyńska J. 2012: Evolutionary Sets of Safe Ship Trajectories: Evaluation of Individuals, TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 6, No. 3, pp. 345–353.
- 15 Śmierzchalski R., Kuczkowski Ł., Kolendo P., Jaworski B. 2013: Distributed Evolutionary Algorithm for Path Planning in Navigation Situation, TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 7, No. 2, pp. 293–300.
- 16 Tam, C., Bucknall, R. 2010: Path‐planning algorithm for ships in close‐range encounters, Journal of Marine Science and Technology, Vol. 15, pp. 395–407.
- 17 Tam, C., Bucknall, R. 2013: Cooperative path planning algorithm for marine surface vessels, Ocean Engineering, Vol. 57, pp. 25–33.
- 18 Tsou, M., Hsueh, C. 2010: The study of ship collision avoidance route planning by ant colony algorithm, Journal of Marine Science and Technology‐TAIWAN, Vol. 18, pp. 746–756.
- 19 Tsou, M., Kao, S., Su, C. 2010: Decision support from genetic algorithms for ship collision avoidance route planning and alerts, Journal of Navigation, Vol. 63, pp. 167–182.
- 20 Xue, Y., Clelland, D., Lee, B., Han, D. 2011: Automatic simulation of ship navigation, Ocean Engineering, Vol. 38, pp. 2290–2305.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)
Typ dokumentu
Bibliografia
Identyfikator YADDA
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