改进自主潜水器导航:动态海洋环境路径搜索的混合群集智能

Husam Alowaidi, Hemalatha P, Poongothai K, Sundoss ALmahadeen, Prasath R, Amarendra K
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引用次数: 0

摘要

水下研究和监测工作在很大程度上依赖自主潜水器(AUV)进行科学调查、资源管理和监测,水下基础设施的维护水平也与其他应用息息相关。在瞬息万变、复杂多变的水下环境(UE)中,路径搜索(PF)需要克服众多挑战,而高效导航和预防方法只是其中的几个。动态环境和实时改进是传统模型面临的难题。为了给不确定的水下环境导航提供卓越的解决方案,这项工作提出了一种混合优化技术,将用于局部路径选择的蚁群优化(ACO)与用于全局路径调度的粒子群优化(PSO)相结合。运行时间效率、精确度和集中减少的距离这三个指标表明 PSO-ACO 混合方法优于传统算法,证明了其对改善 AUV 导航的重要意义。目前的研究支持了 AUV 在水下研究等领域功能的改进,进一步促进了自主水下导航系统(AUNS)的发明。PSO+ACO 混合方法在寻路方面优于 PSO、ACO 和 GA 算法,其执行时间为 6.43 秒,准确率为 93.5%--ACO 模型在 12.53 秒内完成,优于所提出的系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving Autonomous Underwater Vehicle Navigation: Hybrid Swarm Intelligence for Dynamic Marine Environment Path-finding
Underwater research and monitoring operations rely significantly on Autonomous Underwater Vehicles (AUVs) for scientific investigations, resource management, and monitoring, and underwater infrastructure is provided maintenance levels amid other applications. Efficient navigation and preventative methods are only a couple of the numerous challenges that Path-Finding (PF) in rapidly changing and sophisticated Underwater Environments (UE) requires overcoming. Dynamic environments and real-time improvements are problems for traditional models. In order to provide superior solutions for navigating uncertain UE, this work suggests a hybrid optimization technique that combines Ant Colony Optimization (ACO) for local path selection with Particle Swarm Optimization (PSO) for global path scheduling. Runtime efficiency, accuracy, and distance focused on decrease are three metrics that demonstrate how the PSO-ACO hybrid method outperforms conventional algorithms, proving its significance for improving AUV navigation. The improvement of AUV functions in fields such as underwater research, along with others, is supported by the current research, which further assists with the invention of Autonomous Underwater Navigation Systems (AUNS). The PSO+ACO hybrid method is superior to the PSO, ACO, and GA algorithms in pathfinding with a 6.43-second execution time and 93.5% accuracy—the ACO model completed in 12.53 seconds, superior to the proposed system.
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CiteScore
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