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Navigation Decision-Making Method of Complex Multitype Ships’ Routing Waters

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This study aims to develop a Bayesian regression model to evaluate the economic loss resulting from two-ship collisions using ten years’ ship collision data for occurrances in Fujian waters. The model results show that the involvement of passenger/cruise ships could cause the largest increment on economic loss in ship collisions. Interestingly, it is found the involvement of fishing ships could greatly increase the ship collision consequence in terms of economic loss. Results also reveal that the higher economic loss is associated with the collisions in the straits/sea areas, under the strong wind/wave conditions, during nighttime period, and in poor visibility conditions. The impact analysis results highlight that judgment errors play a decisive role in increasing the economic loss as compared with the other two types of human errors: lookout failure and operation errors. The results of this study are useful for policy-makers in proposing efficient strategies to mitigate the economic loss from two-ship collisions. The developed model is also beneficial for insurance companies in determining the appropriate ship insurance rates. © National Academy of Sciences: Transportation Research Board 2019.
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This paper presents a real-time and deterministic path planning method for autonomous ships or Unmanned Surface Vehicles (USV) in complex and dynamic navigation environments. A modified Artificial Potential Field (APF), which contains a new modified repulsion potential field function and the corresponding virtual forces, is developed to address the issue of Collision Avoidance (CA) with dynamic targets and static obstacles, including emergency situations. Appropriate functional and safety requirements are added in the corresponding virtual forces to ensure International Regulations for Preventing Collisions at Sea (COLREGS)-constrained behaviour for the own ship's CA actions. Simulations show that the method is fast, effective and deterministic for path planning in complex situations with multiple moving target ships and stationary obstacles and can account for the unpredictable strategies of other ships. The authors believe that automatic navigation systems operated without human interaction could benefit from the development of path planning algorithms.
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Ship collision avoidance is highly dependent upon seamanship and rules. When ship collision risk exists, proper collision avoidance actions must be taken according to the correct encounter situation and determined stage. All autonomous collision avoidance (ACA) operations in the future must comply with given rules and seamanship practices, which make the quantitative analysis of them prerequisites for ACA. This study presents a novel quantitative analysis system for the International Regulations for Preventing Collisions at Sea (COLREG) Rules and Seamanship. The proposed system consists of three parts: an encounter situation discrimination model based on the mutually relative bearing of the “target ship” (TS) and “own ship” (OS); a stage discrimination model representing the extent of collision risk per different domain models for every potential situation and a model to determine collision avoidance action per COLREG, seamanship, and ship maneuverability information was established accordingly. The collision avoidance plans appropriate for different situations and stages are generated based on the rules, seamanship, and rudder steering direction judgments. A simulation scenario was utilized to validate the effectiveness and feasibility of the system.
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Automatic collision avoidance from obstacles is important, but the conventional methods for ship collision avoidance emphasize how to design the feasible routes for collision avoidance basing on a ship’s real time position, not to consider the dynamics or control for ship.. In this paper, an applicable multi-ship collision avoidance control method on the basis of the directions of relative velocity is presented, which applies to the routing plan of collision avoidance and ship motion control simultaneously. This method consists of three main steps. The first step is extracting/calculating basic motion and position parameters of the own ship and target ships. The second step is calculating suitable real time directions of velocity of the own ship basing on the method of velocity obstacle. The third step is controlling the variable transformation and algorithm design. This method is simple and flexible. It can be applied to the real time multi-ship collision avoidance, and is very feasible. Simulation results show that the algorithm has good effects.
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Recently, a basic pattern of coast seaway system networking has been established in China. As a result, a large number of precautionary areas will be gradually established. Because of the high frequency of ships' encounters and accidents, these precautionary areas have been a weak but important facet of ships' routeing. Navigational Traffic Conflict Technique (NTCT) was introduced to quantitatively evaluate the safety of precautionary areas. Focusing on traffic conflict, the calculation method of navigational traffic conflict points is discussed, and a comparison analysis of the two editions (2002 and 2008) of Yangtze estuary ships’ routeing is performed. Then the automatic acquisition system of navigational traffic conflict data that can be used in the quantitative analysis of the influence degree of a single traffic flow on the whole precautionary area safety is developed, and the left-turning traffic flows of Yangtze estuary ships’ routeing precautionary areas B are taken as an example. This article establishes a foundation for the precautionary area safety analysis system, and provides the decision basis for policy making in optimisation of geometric design and traffic organisation of precautionary areas.
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In this paper, the fuzzy inference system combined with an expert system is applied to collision avoidance system. Especially, calculation method of the collision risk by using neural network is proposed. At first, the membership functions of DCPA and TCPA are determined on the basis of simulation results using the KT equations. And then, the inference table is redesigned by using the ANFIS (Adaptive Network-based Fuzzy Inference System) algorithm. Secondly, additional factors, the ship domain, topological characteristics and restricted visibility, which can affect navigator's reasoning of the collision risk besides DCPA and TCPA are considered. Finally, MLP (Multilayer Perceptron) neural network to the collision avoidance system is applied to make up for fuzzy logic.
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The annual total losses of trading ships as a result of collision and wreck were considered in a previous paper for the purposes of comparing the safety records of different flag groups. Losses up to the year 1979 were included in the tables. In order to assess whether there has been an improvement, the losses for the 5-year period 1980–4 will now be considered and compared with losses in previous years.
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