Journal is indexed in following databases:

2023 Journal Impact Factor - 0.7
2023 CiteScore - 1.4




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




Associate Editor
Prof. Tomasz Neumann

Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
DSSA+: Distributed Collision Avoidance Algorithm in an Environment where Both Course and Speed Changes are Allowed
1 Kobe University, Kobe, Japan
Times cited (SCOPUS): 10
ABSTRACT: Distributed Stochastic Search Algorithm (DSSA) is one of state-of-the-art distributed algorithms for the ship collision avoidance problem. In DSSA, whenever a ship encounters with any number of other ships (neighboring ships), she will select her course with a minimum cost after coordinating their decisions with her neighboring ships. The original DSSA assumes that ships can change only their courses while keeping their speed considering kinematic properties of ships in general. However, considering future possibilities to address more complex situations that may cause ship collision or to deal with collision of other vehicles (such as mobile robots or drones), the options of speed changes are necessary for DSSA to make itself more flexible and extensive. In this paper, we present DSSA+, as a generalization of DSSA, in which speed change are naturally incorporated as decision variables in the original DSSA. Experimental evaluations are provided to show how powerful this generalization is.
Kim, D., Hirayama, K., & Park, G. 2014. Collision Avoidance in Multiple-ship Situations by Distributed Local Search. Journal of Advanced Computational Intelligence and Intelligent Informatics 18(5): 839-848. - doi:10.20965/jaciii.2014.p0839
Kim, D., Hirayama, K., & Okimoto, T. 2017. Distributed Stochastic Search Algorithm for Multi-ship Encounter Situations. The Journal of Navigation 70(4): 699-718. - doi:10.1017/S037346331700008X
Lamb, W.G.P. & Hunt, J.M. 1995. Multiple Crossing Encounters. The Journal of Navigation 48(1): 105-113. - doi:10.1017/S0373463300012546
Lamb, W.G.P. & Hunt, J.M. 2000. Multiple Encounter Avoidance Manoeuvres. The Journal of Navigation, 53(1): 181-186. - doi:10.1017/S0373463399008565
Lazarowska, A. 2015. Ship’s Trajectory Planning for Collision Avoidance at Sea Based on Ant Colony Optimisation. The Journal of Navigation 68(2): 291-307. - doi:10.1017/S0373463314000708
Lee, S., Kwon, K. & Joh, J. 2004. A Fuzzy Logic for Autonomous Navigation of Marine Vehicles Satisfying COLREG Guidelines. International Journal of Control, Automation and Systems, 2(2): 171-181.
Szlapczynski, R. 2011. Evolutionary Sets of Safe Ship Trajectories: A New Approach to Collision Avoidance. The Journal of Navigation 64(1): 169-181. - doi:10.1017/S0373463310000238
Szlapczynski, R. 2015. Evolutionary Planning of Safe Ship Tracks in Restricted Visibility. The Journal of Navigation 68(1): 39-51. - doi:10.1017/S0373463314000587
Tsou, M. & Hsueh, C. 2010. The Study of Ship Collision Avoidance Route Planning by Ant Colony Algorithm. Journal of Marine Science and Technology 18(5): 746-756.
Tsou, M., Kao, S. & Su, C. 2010. Decision Support from Genetic Algorithms for Ship Collision Avoidance Route Planning and Alerts. The Journal of Navigation 63(1): 167-182. - doi:10.1017/S037346330999021X
Zhang, W., Wand G., Xing, Z. & Wittenburg, L. 2005. Distributed Stochastic Search and Distributed Breakout: Properties, Comparison and Applications to Constraint Optimization Problem in Sensor Networks. Artificial Intelligence 161(1–2): 55–87. - doi:10.1016/j.artint.2004.10.004
Citation note:
Hirayama K., Miyake K., Shiota T., Okimoto T.: DSSA+: Distributed Collision Avoidance Algorithm in an Environment where Both Course and Speed Changes are Allowed. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 13, No. 1, doi:10.12716/1001.13.01.11, pp. 117-123, 2019

Other publications of authors:

File downloaded 528 times

Important: cookie usage
The 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 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