受藤壶幼虫启发，提出新型水下集群优化算法，提升深海探测可靠性。
Nauplius Optimisation for Autonomous Hydrodynamics
- 模仿藤壶幼虫行为，融合水流感知与不可逆锚定机制
- 在流动环境下实现86%的永久锚定成功率
- 适合强流环境下的水下机器人集群任务
自主水下航行器（AUV）需在强流、声学带宽受限及持续感知需求下运行，传统群体优化方法在此类场景中不可靠。本文针对水下群体的不可逆水动力部署问题，提出一种受自然启发的新型群体优化算法——Nauplius Optimisation for Autonomous Hydrodynamics（NOAH）。该算法借鉴藤壶幼虫行为，结合水流感知漂移、持久感知节点的不可逆驻留机制以及群体通信能力。相比现有方法，NOAH具备水动力感知、不可逆锚定和群体通信能力，有效解决水下探索任务中的关键挑战。验证研究表明，在流动条件下实现了86%的永久锚定成功率，建立了统一的水动力约束与不可逆驻留行为建模框架，并通过实证分析验证了其性能。该算法为可扩展、低能耗的水下群体机器人系统提供了坚实基础。
原文摘要 · Abstract (English)
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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