Global Path Planning of Unmanned Surface Vessel Based on Multi-objective Hybrid Particle Swarm Algorithm

Zhou, Hao; Zhao, Dongming; Guo, Xuan

doi:10.1007/978-981-10-7179-9_7

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…Whilst these algorithms are often more complicated, the result is often better than when the combined methods are applied separately, as in many cases they overcome each other's drawbacks. Some good examples are presented by Zhou et al [65], Wang et al [45], Xiong et al [66], Blaich et al [67].…”

Section: Path Planning Algorithmsmentioning

confidence: 99%

Path planning and collision avoidance for autonomous surface vehicles I: a review

et al. 2021

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Autonomous surface vehicles are gaining increasing attention worldwide due to the potential benefits of improving safety and efficiency. This has raised the interest in developing methods for path planning that can reduce the risk of collisions, groundings, and stranding accidents at sea, as well as costs and time expenditure. In this paper, we review guidance, and more specifically, path planning algorithms of autonomous surface vehicles and their classification. In particular, we highlight vessel autonomy, regulatory framework, guidance, navigation and control components, advances in the industry, and previous reviews in the field. In addition, we analyse the terminology used in the literature and attempt to clarify ambiguities in commonly used terms related to path planning. Finally, we summarise and discuss our findings and highlight the potential need for new regulations for autonomous surface vehicles.

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Section: Path Planning Algorithmsmentioning

confidence: 99%

Path planning and collision avoidance for autonomous surface vehicles I: a review

et al. 2021

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“…The static collision-avoidance-based ship path planning problems ensure the avoidance of a collision with any stable obstacles, such as an island, buoys, shallows, rocks and fishing nets. It can be solved by the traditional algorithms including a spatial structural model, approaches based on raster grids, the lineof-sight method and the potential field approach, as well as artificial intelligence-based algorithms and simulations such as particle swarm, evolutionary, neural networks and fuzzy logic, as stated by Yan et al (2009), Tsou and Hsueh (2010), Szłapczyński (2012), Chen et al (2016), Kolendo and Śmierzchalski (2016), Deng et al (2017), Hinostroza et al (2017), Witkowska et al (2017), Zhou et al (2017), Wang et al (2018b) and Tang et al (2019).…”

Section: What Are the Significant Differences Between The Static And ...mentioning

confidence: 99%

A review of ship collision risk assessment, hotspot detection and path planning for maritime traffic control in restricted waters

Meng

Fang

et al. 2022

J. Navigation

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The three research topics, ship collision risk assessment, ship traffic hotspot detection and prediction, and collision-avoidance based ship path planning, are vital for next-generation vessel traffic management and monitoring systems. The system development is closely related to big data analytics and artificial intelligence for restricted waters. This study, therefore, aims to analyse the state-of-the art of these three topics over the latest decade, identify research gaps, and shed light on future research avenues. To achieve these three objectives, we critically and systematically review related articles that were published during the period between 2011 and 2021. We believe that this comprehensive and critical literature review would have a significant and profound impact on the formal safety assessment and vessel traffic management, and monitoring studies because it is not only an extension but also an essential continuity work of the literature review on maritime waterway risk assessment and prediction, as well as ship path guidance for ship collision risk mitigation in accordance with current automation vessels development and modern intelligent port construction.

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“…By simulating the kinematics model of lawn mower, the number of turns in path planning was reduced, and the problems of task assignment and battery life were effectively solved. Aiming at the problems of vehicle processing speed, path planning efficiency, safety verification ability and dynamic obstacle avoidance performance during autonomous driving, Qin [16] et al, Zhang [17] et al, Zhao [18] et al, respectively, proposed three improvement strategies: improving artificial potential field algorithm, combining A* algorithm and random tree algorithm, and optimizing traditional particle swarm optimization. Through the hierarchical solution of the original algorithm, the method of expanding the node of the original algorithm and applying the map simplification strategy shortens the solution time, and enhances the obstacle avoidance ability and real-time performance of the unmanned vehicle.…”

Section: Introductionmentioning

confidence: 99%

Obstacle avoidance strategy and path planning of medical AGV based on the bionic characteristics of antelope migration

Hu,

Niu,

Gao

et al. 2024

Preprint

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Aiming at the problem that Automated guided vehicle (AGV) faces dynamic and static obstacles in the process of transporting patients in medical environment and needs to avoid in real time, inspired by the behavior of antelopes during migration, this paper proposes a bionic obstacle avoidance strategy based on adaptive behavior of antelopes. The traditional artificial potential field and dynamic window algorithm are improved by using the bionic characteristics of antelope migration.By adding new potential field force points and improving the window size, the success rate and prediction range of AGV navigation are improved.Simulation experiments were carried out through the numerical simulation platform, and the verification results showed that:The bionic obstacle avoidance strategy proposed in this paper can avoid dynamic and static obstacles at the same time. In the example, the success rate of path planning is increased by 34%, the running time is reduced by 33%, and the average path length is reduced by 1%. The proposed method can realize the integration of “dynamic and static” avoidance in the process of transporting patients, and effectively save the time of transporting patients by AGV.It provides a theoretical basis for realizing obstacle avoidance and rapid loading of AGV in medical environment.

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Global Path Planning of Unmanned Surface Vessel Based on Multi-objective Hybrid Particle Swarm Algorithm

Cited by 4 publications

References 9 publications

Path planning and collision avoidance for autonomous surface vehicles I: a review

Path planning and collision avoidance for autonomous surface vehicles I: a review

A review of ship collision risk assessment, hotspot detection and path planning for maritime traffic control in restricted waters

Obstacle avoidance strategy and path planning of medical AGV based on the bionic characteristics of antelope migration

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