用于自主流体动力学的无节幼体优化算法 (Nauplius Optimisation for Autonomous Hydrodynamics)

Autonomous Underwater vehicles must operate in strong currents, limited acoustic bandwidth, and persistent sensing requirements where conventional swarm optimisation methods are unreliable. This paper formulates an irreversible hydrodynamic deployment problem for Autonomous Underwater Vehicle (AUV) swarms and presents Nauplius Optimisation for Autonomous Hydrodynamics (NOAH), a novel nature-inspired swarm optimisation algorithm that combines current-aware drift, irreversible settlement in persistent sensing nodes, and colony-based communication. Drawing inspiration from the behaviour of barnacle nauplii, NOAH addresses the critical limitations of existing swarm algorithms by providing hydrodynamic awareness, irreversible anchoring mechanisms, and colony-based communication capabilities essential for underwater exploration missions. The algorithm establishes a comprehensive foundation for scalable and energy-efficient underwater swarm robotics with validated performance analysis. Validation studies demonstrate an 86% success rate for permanent anchoring scenarios, providing a unified formulation for hydrodynamic constraints and irreversible settlement behaviours with an empirical study under flow.

翻译：自主水下航行器必须在强洋流、有限声学带宽和持续感知需求的复杂环境中运行，而传统的群体优化方法在此类场景下往往不可靠。本文针对自主水下航行器（AUV）集群提出了一个不可逆的流体动力学部署问题，并介绍了用于自主流体动力学的无节幼体优化算法（NOAH）——一种新颖的受自然启发的群体优化算法，该算法结合了洋流感知漂移、在持续感知节点上的不可逆沉降以及基于群体的通信机制。受藤壶无节幼体行为的启发，NOAH通过提供流体动力学感知、不可逆锚定机制以及水下探测任务所必需的群体通信能力，解决了现有群体算法的关键局限性。该算法为可扩展且高能效的水下群体机器人技术建立了全面的理论基础，并提供了经过验证的性能分析。验证研究表明，在永久锚定场景中，NOAH实现了86%的成功率，通过流场下的实证研究，为流体动力学约束和不可逆沉降行为提供了统一的数学表述。