Determination of a safe trajectory with the use of an ant algorithm and consideration of the ship’s maneuvering characteristic
Keywords:
ant algorithm, maneuver time, safe trajectory, collision avoidanceAbstract
The article presents a proposal for solving the problem of determining a ship’s safe trajectory using one of the stochastic optimization methods, which is an ant algorithm. In the process of ship’s safe path planning all of the most important requirements and limitations were taken into account, which include the International Regulations for Preventing Collisions at Sea (COLREGs), static (lands, shoals) and dynamic (target ships) obstacles, a safe distance between ships, weather conditions (visibility) and dynamic properties of the ship. The dynamics of an own ship were taken into account in the form of maneuver time, the value of which is indicated by the maneuvering characteristic of a vessel.References
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.
[2] Fossen, T.I., 2011, Handbook of Marine Craft Hydrodynamics and Motion Control, John Wiley & Sons, Ltd
[3] 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), Osaka, Japan.
[4] Kuczkowski, Ł., Śmierzchalski, R., 2014, Comparison of Single and Multi-population Evolutionary Algorithm for Path Planning in Navigation Situation, Solid State Phenomena, vol. 210.
[5] Lazarowska, A., 2015, Ship's Trajectory Planning for Collision Avoidance at Sea Based on Ant Colony Optimisation, Journal of Navigation, vol. 68(02).
[6] Lisowski, J., 2010, Optimization Decision Support System for Safe Ship Control, [w:] Brebbia, C.A, Brooks C.N., (eds.) Risk Analysis VII, WIT Transactions on Information and Communication Technologies, vol. 43, WIT Press.
[7] Lisowski, J., 2014, Comparison of Dynamic Games in Application to Safe Ship Control, Polish Maritime Research, vol. 21, 3(83).
[8] Mohamed-Seghir, M., 2012, The Branch-and-Bound Method and Genetic Algorithm in Avoidance of Ships Collisions in Fuzzy Environment, Polish Maritime Research, vol. 19, S1(74).
[9] Perera, L., 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).
[10] Szłapczyńska, J., 2015, Data Acquisition in a Manoeuver Auto-negotiation System, TransNav, International Journal on Marine Navigation and Safety of Sea Transportation, vol. 9, no. 3.
[11] Szłapczyński, R., Szłapczyńska, J., 2012, Evolutionary Sets of Safe Ship Trajectories: Evaluation of Individuals, TransNav, International Journal on Marine Navigation and Safety of Sea Transportation, vol. 6, no. 3.
[12] Tsou, M.-Ch., Hsueh, Ch.-K., 2010, The Study of Ship Collision Avoidance Route Planning by Ant Colony Algorithm, Journal of Marine Science and Technology, vol. 18, no. 5.
Remove [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.
[2] Fossen, T.I., 2011, Handbook of Marine Craft Hydrodynamics and Motion Control, John Wiley & Sons, Ltd
[3] 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), Osaka, Japan.
[4] Kuczkowski, Ł., Śmierzchalski, R., 2014, Comparison of Single and Multi-population Evolutionary Algorithm for Path Planning in Navigation Situation, Solid State Phenomena, vol. 210.
[5] Lazarowska, A., 2015, Ship's Trajectory Planning for Collision Avoidance at Sea Based on Ant Colony Optimisation, Journal of Navigation, vol. 68(02).
[6] Lisowski, J., 2010, Optimization Decision Support System for Safe Ship Control, [w:] Brebbia, C.A, Brooks C.N., (eds.) Risk Analysis VII, WIT Transactions on Information and Communication Technologies, vol. 43, WIT Press.
[7] Lisowski, J., 2014, Comparison of Dynamic Games in Application to Safe Ship Control, Polish Maritime Research, vol. 21, 3(83).
[8] Mohamed-Seghir, M., 2012, The Branch-and-Bound Method and Genetic Algorithm in Avoidance of Ships Collisions in Fuzzy Environment, Polish Maritime Research, vol. 19, S1(74).
[9] Perera, L., 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).
[10] Szłapczyńska, J., 2015, Data Acquisition in a Manoeuver Auto-negotiation System, TransNav, International Journal on Marine Navigation and Safety of Sea Transportation, vol. 9, no. 3.
[11] Szłapczyński, R., Szłapczyńska, J., 2012, Evolutionary Sets of Safe Ship Trajectories: Evaluation of Individuals, TransNav, International Journal on Marine Navigation and Safety of Sea Transportation, vol. 6, no. 3.
[12] Tsou, M.-Ch., Hsueh, Ch.-K., 2010, The Study of Ship Collision Avoidance Route Planning by Ant Colony Algorithm, Journal of Marine Science and Technology, vol. 18, no. 5.
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