Collision avoidance of moving obstacles for ship with genetic algorithm

Zeng, Xiaoming; Ito, Mai; Shimizu, Etsuro

doi:10.1109/amc.2000.862927

Cited by 12 publications

(6 citation statements)

References 3 publications

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“…a, b, c, d are the level of danger, length of path, straightness of the route and energy loss, respectively. This route planning method is further improved in [131] by including a traffic navigation rule term in the cost function, namely, own ship must pass from the right of other moving ships. In [132] a noise term modelling the influence of tide, wind, and wave is introduced into the gene structure to achieve a better prediction while the ship sails in bad weather condition.…”

Section: Evolutionary Algorithm Methodsmentioning

confidence: 99%

Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology

Zhang

Rachmawati³

et al. 2018

IEEE Trans. Intell. Transport. Syst.

315

141

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The Automatic Identification System (AIS) tracks vessel movement by means of electronic exchange of navigation data between vessels, with onboard transceiver, terrestrial and/or satellite base stations. The gathered data contains a wealth of information useful for maritime safety, security and efficiency. This paper surveys AIS data sources and relevant aspects of navigation in which such data is or could be exploited for safety of seafaring, namely traffic anomaly detection, route estimation, collision prediction and path planning.• Real time anomaly detection can identify potential se-1 Operating costs usually include crew, stores and lubes, maintenance and repair, insurance costs and overhead costs and are often distinguished from voyage costs such as fuel and bunkering cost.2 It should be mentioned that there are also other types of data (such as radar, video etc.) that can be used for these applications, but their corresponding algorithms and mechanisms are quite different from that of AIS based and thus are out of the scope of this paper.

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Section: Evolutionary Algorithm Methodsmentioning

confidence: 99%

Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology

Zhang

Rachmawati³

et al. 2018

IEEE Trans. Intell. Transport. Syst.

315

141

View full text Add to dashboard Cite

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“…Kreutzmann et al 2013 provided a formalisation system to enable a machine to determine COLREG situations correctly and exemplarily implemented this for rule 12. Other approaches have been made by Zeng 2000or Perera et al 2009 which used genetic algorithms and fuzzy logic respectively to determine the give-way obligation for two-vessel situations. In contrast, Liu et al 2006& Xue et al 2008 do also provide counter measures, in the case of Xue et al 2008 even for multiple encounters.…”

Section: Approachmentioning

confidence: 99%

Interaction of Harsh Weather Operation and Collision Avoidance in Autonomous Navigation

Burmeister¹,

Bruhn²,

Walther³

2015

TransNav

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Taking into account the autonomous navigation system design and today's state of the art navigation with regards to weather and collision avoidance this paper presents the architecture of the integrated approach, its links to existing rules and regulations and the test scenarios. These demonstrate how safe and efficient navigation of autonomous vessels can be achieved by showing the moduleʹs interaction and validating the feasibility of the approach. These analyses will be based on historical traffic data sets as well as simulation results. http://www.transnav.eu the International Journal on Marine Navigation and Safety of Sea Transportation Volume 9 Number 1

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“…Nowadays, maritime aid technologies include Global Positioning System (GPS), Radar, Automatic Radar Plotting Aid (ARPA) (Zeng, 2003, Pedersen, 1999) and atmospheric and water weather monitoring instruments. A considerable number of the algorithms for ship autonomous collision avoidance consider that the ships are equipped with GPS and ARPA (Zeng et al ., 2000, Xiao-Ming and Ito, 2001). But none of these algorithms take into consideration all the instrumentation, navigational data and communication for collision avoidance that most of the modern ships have available.…”

Section: Contrasting Human Cognitive Abilities and Intelligent Algorimentioning

confidence: 99%

Autonomous Ship Collision Avoidance Navigation Concepts, Technologies and Techniques

2007

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This study provides both a spherical understanding about autonomous ship navigation for collision avoidance (CA) and a theoretical background of the reviewed work. Additionally, the human cognitive abilities and the collision avoidance regulations (COLREGs) for ship navigation are examined together with water based collision avoidance algorithms. The requirements for autonomous ship navigation are addressed in conjunction with the factors influencing ship collision avoidance. Humans are able to appreciate these factors and also perform ship navigation at a satisfactory level, but their critical decisions are highly subjective and can lead to error and potentially, to ship collision. The research for autonomous ship navigation may be grouped into the classical and soft computing based categories. Classical techniques are based on mathematical models and algorithms while soft-computing techniques are based on Artificial Intelligence (AI). The areas of AI for autonomous ship collision avoidance are examined in this paper are evolutionary algorithms, fuzzy logic, expert systems, and neural networks (NN), as well as a combination of them (hybrid system).

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Collision avoidance of moving obstacles for ship with genetic algorithm

Cited by 12 publications

References 3 publications

Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology

Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology

Interaction of Harsh Weather Operation and Collision Avoidance in Autonomous Navigation

Autonomous Ship Collision Avoidance Navigation Concepts, Technologies and Techniques

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