The application research for ship collision avoidance with hybrid optimization algorithm

Liu, Hongdan; Deng, Rong; Zhang, Lanyong

doi:10.1109/icinfa.2016.7831921

Cited by 11 publications

(6 citation statements)

References 3 publications

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“…Tam and Bucknall (2013) developed a deterministic collision avoidance path planning algorithm to provide collision-free paths for all involved ships, assuming that all encountering ships are in a cooperative mode. Liu et al (2016) proposed a hybrid optimisation cultural algorithm (CA) based on the particle swarm optimisation and bacterial foraging algorithm. Szlapczynski and Szlapczynska (2012) presented a multi-ship trajectory planning method by using evolutionary algorithms.…”

Section: Related Workmentioning

confidence: 99%

A collaborative collision avoidance strategy for autonomous ships under mixed scenarios

et al. 2023

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Ship collision avoidance has always been one of the classic topics in the field of marine research. In traditional encounter situations, officers on watch (OOWs) usually use a very high frequency (VHF) radio to coordinate each other. In recent years, with the continuous development of autonomous ships, there will be a mixed situation where ships of different levels of autonomy coexist at the same time. Under such a scenario, different decision makers have different perceptions of the current scene and different decision-making logic, so conventional collision avoidance methods may not be applicable. Therefore, this paper proposes a collaborative collision avoidance strategy for multi-ship collision avoidance under mixed scenarios. It builds a multi-ship cooperative network to determine cooperative objects and timing, at the same time. Based on a cooperative game model, a global collision avoidance responsibility distribution that satisfies group rationality and individual rationality is realised, and finally achieves a collaborative strategy according to the generalised reciprocal velocity obstacle (GRVO) algorithm. Case studies show that the strategy proposed in this paper can make all ships pass each other clearly and safely.

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Section: Related Workmentioning

confidence: 99%

A collaborative collision avoidance strategy for autonomous ships under mixed scenarios

et al. 2023

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“…Combined the improved bacterial foraging algorithm with the particle swarm optimization algorithm with strong global search ability and fast local convergence ability and applied it to ship collision avoidance path optimization. The hybrid optimization algorithm could get collision avoidance path quickly [18]. By modeling the dynamic domain of ships, a reasonable and effective ship automatic collision avoidance decision system was established by Xu et al.…”

Section: B Local Dynamic Collision Avoidancementioning

confidence: 99%

PE-A* Algorithm for Ship Route Planning Based on Field Theory

Liu

Wang

2022

IEEE Access

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Route planning is the key to safe, efficient and intelligent navigation of maritime traffic. Autonomous route planning is a complex optimization problem, which requires both global route planning and local collision avoidance. In this paper, we propose an optimization algorithm which can consider both global route planning and local collision avoidance. Firstly, nonlinear constraint optimization models of obstacle limitation, safe water depth limitation and ship steering limitation are established. Then, the PE-A * algorithm and route planning framework are proposed by using potential energy field to accurately express the environment. Finally, the safety and feasibility of the route planned by PE-A * algorithm are discussed through simulation experiments. Simulation results show that PE-A * algorithm can realize both global route planning and local collision avoidance, and the safety and feasibility of the planned route are greatly improved. From the perspective of potential energy, this work proposes an automatic route planning algorithm and establishes a flexible mathematical model, which can add fuel consumption, time and other related engineering requirements into the model to plan the optimal route meeting the requirements.INDEX TERMS Ship, potential energy, A * algorithm, route planning.

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“…In other words, the target ship is traveling on the left front side or the right front side of our ship. In an overtake situation, the target ship is sailing toward our ship with an azimuth angle in the range 112.5°to 247.5°, i.e., the target ship is following our ship from the rear with an angle exceeding 22.5° [13] [14].…”

Section: A Program Performance Analysismentioning

confidence: 99%