幼体优化用于自主水动力学
Nauplius Optimisation for Autonomous Hydrodynamics
AI总结：
本文提出NOAH算法，结合水流感知漂移、不可逆锚定和群体通信，解决水下集群机器人在强流和持续感知中的优化问题。
摘要：
自主水下车辆必须在强流、有限声学带宽和持续感知需求中运行，传统群体优化方法不可靠。本文提出自主水下车辆集群的不可逆水动力部署问题，提出Nauplius优化用于自主水动力学（NOAH）算法，结合水流感知漂移、不可逆锚定和群体通信。受藤壶幼体行为启发，NOAH通过提供水动力意识、不可逆锚定机制和群体通信能力，解决现有群体算法的关键限制。该算法为可扩展且节能的水下集群机器人建立了全面基础，并通过验证性能分析。验证研究显示，在永久锚定场景中成功率为86%，提供了统一的水动力约束和不可逆锚定行为的公式化方法，并通过流体实验进行实证研究。
英文摘要：
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.
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