Multiobjective Optimization Based Vessel Collision Avoidance Strategy Optimization

Xu, Qingyang; Zhang, Chuang; Wang, Weining

doi:10.1155/2014/914689

Cited by 13 publications

(6 citation statements)

References 31 publications

(45 reference statements)

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“…CRA parameters are not limited only to TCPA and DCPA, although these are the most commonly used ones. Other papers also consider parameters such as the distance of the last-minute avoidance, distance to the target vessel, ratio of speed, relative bearing, safe passage circle, and distance of adopt avoidance action [15,47,62].…”

Section: Collision Risk Assessmentmentioning

confidence: 99%

Path planning and collision avoidance for autonomous surface vehicles II: a comparative study of algorithms

et al. 2021

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Artificial intelligence is an enabling technology for autonomous surface vehicles, with methods such as evolutionary algorithms, artificial potential fields, fast marching methods, and many others becoming increasingly popular for solving problems such as path planning and collision avoidance. However, there currently is no unified way to evaluate the performance of different algorithms, for example with regard to safety or risk. This paper is a step in that direction and offers a comparative study of current state-of-the art path planning and collision avoidance algorithms for autonomous surface vehicles. Across 45 selected papers, we compare important performance properties of the proposed algorithms related to the vessel and the environment it is operating in. We also analyse how safety is incorporated, and what components constitute the objective function in these algorithms. Finally, we focus on comparing advantages and limitations of the 45 analysed papers. A key finding is the need for a unified platform for evaluating and comparing the performance of algorithms under a large set of possible real-world scenarios.

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Section: Collision Risk Assessmentmentioning

confidence: 99%

Path planning and collision avoidance for autonomous surface vehicles II: a comparative study of algorithms

et al. 2021

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“…Human factors have always remained the main causation factors of maritime traffic accidents. According to the international ship safety operation and pollution prevention management rules (ISM rules) of International Maritime Organization (IMO), about 80% of maritime traffic accidents are attributed to human factors [34,35]. Human system factors refer to those influences initiated by human behavior or decisions affecting the whole system.…”

Section: Maritime Transportation System Schemementioning

confidence: 99%

The Multi-State Maritime Transportation System Risk Assessment and Safety Analysis

2020

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Maritime transportation has a pivotal role in the foreign trade and hence, the world’s economic growth. It augments the realization of “Maritime Silk Road” strategy. However, the catastrophic nature of the maritime accidents has posed a serious threat to life, property, and environment. Maritime transportation safety is a complex system and is prone to human, equipment, and environment-based risks. In the existing literature, the risk assessment studies aimed at the analysis of maritime traffic safety usually consider the state of system as two ultimate states—one is the normal state and the other is the complete failure state. In contrast to the conventional approaches, this study incorporates a multistate criterion for system state giving consideration to the near or partial failures also. A Markov Chain-based methodology was adopted to determine the variations in state system and define the instant at which a low probability incident transforms into a high-risk intolerable event. The analysis imparts critical time nodes that could be utilized to reduce the risk and evade accidents. This study holds practical vitality for the concerned departments to circumvent the potential dangers and devise systematic preemptive procedures before the accident takes place. The results of this study could be employed to augment safety and sustainability of maritime traffic and decrease the associated pollution.

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“…In this case, challenges include the choice of the model and its structure and the identification of model parameters for the various navigational conditions. To determine the anti-collision manoeuvres, nowadays, AI methods are more frequently used, such as genetic and evolutionary algorithms [ 20 , 21 , 22 , 23 , 24 ], artificial neural networks [ 25 , 26 , 27 ], machine learning [ 28 ], or systems of fuzzy inference [ 29 , 30 , 31 , 32 ]. What restricts the use of these methods on an autonomous ship is essentially the lack of evidence on the stability of the solutions.…”

Section: Introductionmentioning

confidence: 99%

The Algorithm of Determining an Anti-Collision Manoeuvre Trajectory Based on the Interpolation of Ship’s State Vector

Borkowski

Pietrzykowski

Magaj

2021

Sensors

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The determination of a ship’s safe trajectory in collision situations at sea is one of the basic functions in autonomous navigation of ships. While planning a collision avoiding manoeuvre in open waters, the navigator has to take into account the ships manoeuvrability and hydrometeorological conditions. To this end, the ship’s state vector is predicted—position coordinates, speed, heading, and other movement parameters—at fixed time intervals for different steering scenarios. One possible way to solve this problem is a method using the interpolation of the ship’s state vector based on the data from measurements conducted during the sea trials of the ship. This article presents the interpolating function within any convex quadrilateral with the nodes being its vertices. The proposed function interpolates the parameters of the ship’s state vector for the specified point of a plane, where the values in the interpolation nodes are data obtained from measurements performed during a series of turning circle tests, conducted for different starting conditions and various rudder settings. The proposed method of interpolation was used in the process of determining the anti-collision manoeuvre trajectory. The mechanism is based on the principles of a modified Dijkstra algorithm, in which the graph takes the form of a regular network of points. The transition between the graph vertices depends on the safe passing level of other objects and the degree of departure from the planned route. The determined shortest path between the starting vertex and the target vertex is the optimal solution for the discrete space of solutions. The algorithm for determining the trajectory of the anti-collision manoeuvre was implemented in autonomous sea-going vessel technology. This article presents the results of laboratory tests and tests conducted under quasi-real conditions using physical ship models. The experiments confirmed the effective operation of the developed algorithm of the determination of the anti-collision manoeuvre trajectory in the technological framework of autonomous ship navigation.

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Multiobjective Optimization Based Vessel Collision Avoidance Strategy Optimization

Cited by 13 publications

References 31 publications

Path planning and collision avoidance for autonomous surface vehicles II: a comparative study of algorithms

Path planning and collision avoidance for autonomous surface vehicles II: a comparative study of algorithms

The Multi-State Maritime Transportation System Risk Assessment and Safety Analysis

The Algorithm of Determining an Anti-Collision Manoeuvre Trajectory Based on the Interpolation of Ship’s State Vector

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