A Marine Traffic Flow and Collision Avoidance Computer Simulation

Colley, B. A.; Curtis, Robert G.; Stockel, C. T.

doi:10.1017/s0373463300023389

Cited by 23 publications

(19 citation statements)

References 7 publications

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“…For experimental comparison of these methods we have used a set of typical collision situations, which is a standard collection of test cases for navigators (during a course on ARPA) at the Department of Navigation of Gdynia Maritime Academy. 2 For 12 test cases we report the average computational time of the method. 3 We discuss these approaches in turn: Indispensable maneuver: This algorithm [18] prescribes the kinematic maneuver (single change of the course and/or the speed of the own-ship) for avoiding collision in a multitarget encounter.…”

Section: A Methods For Planning Safe Trajectoriesmentioning

confidence: 99%

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Modeling of ship trajectory in collision situations by an evolutionary algorithm

Śmierzchalski

Michalewicz

2000

IEEE Trans. Evol. Computat.

166

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, IEEE senior member Abstract|For a given circumstance (i.e., a collision situation at sea) a decision support system for navigation should help the operator to choose a proper maneuver, teach them good habits, and enhance their general intuition on how to behave in similar situations in the future. By taking into account certain boundaries of the maneuvering region along with information on navigation obstacles and other moving ships, the problem of avoiding collisions is reduced to a dynamic optimization task with static and dynamic constraints. This paper presents experiments with a modied version of the Evolutionary Planner/Navigator (EP/N). Its new version, #EP/N++, is a major component of a such decision support system. This new extension of EP/N computes a safe-optimum path of a ship in given static and dynamic environments. A safe trajectory of the ship in a collision situation is determined on the basis of this algorithm. The introduction of a time parameter, the variable speed of the ship, and time-varying constraints representing movable ships, are the main features of the new system. Sample results of ship trajectories obtained for typical navigation situations are presented.

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Section: A Methods For Planning Safe Trajectoriesmentioning

confidence: 99%

“…This paper uses the following terminology: the term`ownship' means the ship for which the path must be generated, and the terms`strange-ship' or`target' mean other vessels in the environment that must be avoided. This is a standard terminology[1],[2],[6].…”

mentioning

confidence: 99%

Modeling of ship trajectory in collision situations by an evolutionary algorithm

Śmierzchalski

Michalewicz

2000

IEEE Trans. Evol. Computat.

166

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“…According to the dynamic calculation and analysis, the navigation situation was understood on the basis of the closest distance and the encounter situation with an approaching ship. These factors are often used to evaluate the collision risk [14]. Generally, a sailing ship performs an evading action when a target ship within two nautical miles ahead is detected.…”

Section: Analysis Of Encountering Ships and Approaching Distancementioning

confidence: 99%

Elucidation of Latent Risk of Navigation Using an Actual Ship Behavior Analysis

Gao¹,

Hidenari²,

Furusho³

2016

JTTE

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In recent years, maritime transportation has played an important role in global economy development. As a result, ship traffic has become more congested. Moreover, ship navigation is susceptible to weather and environmental conditions, and in some cases, it may become dangerous. Therefore, vessels are subjected to high-risk navigation conditions. To understand the latent risk of ship navigation, this study focused on the actual ship behavior. Thus, an analysis of ship behavior was carried out using historical ship navigation based on automatic identification system data. Consequently, a dynamic analysis of the speed and encounter situation was performed. One of the main results of this work was the understanding of the latent risk involved in ships navigating the Seto Inland Sea, which is one of the most congested routes in Japan. Moreover, the risk areas were obtained, and visualized using a geographical information system. The obtained results can be applied to ensure safe navigation and the development of a safe and efficient navigation model.

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“…Knowledge acquisition refers mainly to the information of the near future (1 sec-1 min) trajectory of possible encounter to the own-ship surface vehicles. A similar study of how to provide suitable navigation information for the mariners and a risk analysis of evasive manoeuvres is proposed (Colley et al, 1984, Lamb, 1985. A similar study of how to provide suitable navigation information for the mariners and a risk analysis of evasive manoeuvres is proposed (Colley et al, 1984, Lamb, 1985.…”

Section: O N T R a S T I N G H U M A N C O G N I T I V E A B I L I mentioning

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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A Marine Traffic Flow and Collision Avoidance Computer Simulation

Cited by 23 publications

References 7 publications

Modeling of ship trajectory in collision situations by an evolutionary algorithm

Modeling of ship trajectory in collision situations by an evolutionary algorithm

Elucidation of Latent Risk of Navigation Using an Actual Ship Behavior Analysis

Autonomous Ship Collision Avoidance Navigation Concepts, Technologies and Techniques

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