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Editor-in-Chief

Associate Editor
Prof. Tomasz Neumann
 

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TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
www http://www.transnav.eu
e-mail transnav@umg.edu.pl
The Machine Learning Method of PIDVCA
ABSTRACT: Building a dynamic collision knowledge base of self-learning is one of the core contents of implementing "personified intelligence" in Personifying Intelligent Decision-making for Vessel Collision Avoidance (short for PIDVCA). In the paper, the machine learning method of PIDVCA combined with offline artificial learning and online machine learning is proposed. The static collision avoidance knowledge is acquired through offline artificial learning, and the isomeric knowledge representation integration method with process knowledge as the carrier is established, and the Dynamic collision avoidance knowledge is acquired through online machine learning guided by inference engine. A large number of simulation results show that the dynamic collision avoidance knowledge base constructed by machine learning can achieve the effect of anthropomorphic intelligent collision avoidance. It is verified by examples that the machine learning method of PIDVCA can realize target perception, target cognition and finally obtain an effective collision avoidance decision-making.
REFERENCES
Chenbo,Wang & Xinyu, Z. et al. 2018.Research on intelligent collision avoidance decision-making of unmanned ship in unknown environments. Chinese ship research13(06):72-77
Zen, Chi. 2016.Research on Autonomous collision avoidance method of Unmanned vehicle based on Machine Learning. Harbin University of Engineering.
Tom M. Michell. Machine Learning. 2005.China Maine press.
Jing, Shen. 2007. Theory and method of hierarchical reinforcement Learning. Harbin Engineering University Press.
Lina,Li.& Conggui,Chen. 1996. Research On Multi-ship Anti-collision Intelligent Mathod (ACIM) At Widely Area. The Proceedings of ANTI-COLLISION'96 CONFERENCE. Chiavari PublishingVol.2:214-220. China:Dalian
Lina, Li. & Yang, S.H. et al. 2009. Study on the Theoretical famework of Personifying Intelligent Decision-making for Vessel Collision Avoidance. Navigation of China, (6):30-34.
Lina, Li. 2002. Determination of the Factors about Safe Distance of Approach and etc On the Research of Ship Automatic Avoidance Collision. Journal of Dalian Maritime University (3): 23-26.
Lina, Li et al. 2002. Method for Building and Optimizing to the Intelligent Decision of Single-ship Anti-collision[J]. Navigation of China (2): 49-52.
Lina, Li et al. 2006. Study of Simulation Platform Construction for Automatic Ship Collision Avoidance and Its Test Method. Navigation of China (3): 47-50.
Lina, Li. & Zhennan,Xiong. et al. 2003.Method for Building and Optimizing to the Decision of Multi-ship Intelligent Anti-collision. Information and Control (2): 189-192.
Lina, Li. & Guoquan,Chen.et al. 2011. Construction of the PIDVCA system and its evaluation standard. Journal of Dalian Marine College. 37(4):1-6
Guoquan, Chen. Yong,Yin. & Lina, Li. Li et al. 2010. Mechanism and Simulation of Personifying Intelligent Decision- making for Vessel Collision Avoidance. The proceedings of 2010 International Conference on Computer Application and System Modeling V 4681-V4686 - doi:10.1109/ICCASM.2010.5620441
Guoquan, Chen Yin Yong, & Lina, Li. Li et al. 2015. Design and realization of the intelligent navigational simulator. Journal of Dalian Maritime University
Citation note:
Li L.N., Wang X.H., Chen G.: The Machine Learning Method of PIDVCA. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 14, No. 3, doi:10.12716/1001.14.03.02, pp. 533-540, 2020

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