DSSA+: distributed collision avoidance algorithm in an environment where both course and speed changes are allowed

• Autorzy: Hirayama K. , Miyake K. , Shiotani T. , Okimoto T.

Identyfikatory

DOI

10.12716/1001.13.01.11

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

collision avoidance; collision avoidance algorithm; vessel course changes; vessel speed changes; Distributed Stochastic Search Algorithm (DSSA); colregs; ARPA; Distributed Constraint Optimization Problem (DCOP)

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2019
• Tom: Vol. 13, no. 1
• Strony: 117--123
• Opis fizyczny: Bibliogr. 13 poz., rys., tab.

Twórcy

autor

Hirayama K.

• Kobe University, Kobe, Japan

autor

Miyake K.

• Kobe University, Kobe, Japan

autor

Shiotani T.

• Kobe University, Kobe, Japan

autor

Okimoto T.

• Kobe University, Kobe, Japan

Bibliografia

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• 3. Kim D., Hirayama K., Okimoto T.: Ship Collision Avoidance by Distributed Tabu Search. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 9, No. 1, doi:10.12716/1001.09.01.03, pp. 23-29, 2015
• 4. 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
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• 13. 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

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).