Nauplius Optimisation for Autonomous Hydrodynamics

TL;DR

NOAH tackles the problem of deploying AUV swarms in dynamic ocean environments by turning mobile agents into permanently anchored sensing and relay colonies. It fuses hydrodynamic drift adaptation, irreversible anchoring, and colony-based communication into a mathematically grounded three-phase algorithm, guided by barnacle nauplii biology. Validation shows 86% anchoring success, robust convergence across benchmarks, and superior rankings relative to traditional baselines, supporting practical deployment for persistent monitoring, conservation, and infrastructure tasks. The approach enables energy-efficient, flow-aware underwater exploration with scalable, multi-hop colony networks that preserve discovered optima as fixed sensing assets. This framework has potential to transform long-term marine observation and operational resilience in underwater robotics. Key mathematical components include the flow field $U(x)$, colonies set $C_t$ with locations $c_k$ and strengths $S_k$, and the colony influence field $\\Phi(x)$ driving agent updates via $G_{\\Phi}(x)$, alongside an irreversible settlement probability $p_{i,t}^{settle}$ defined through a logistic combination of local fitness, colony distance, flow stability, crowding, and energy. NOAH’s convergence leverages progressive freezing of agents, reducing mobility over time and yielding a distributed sensor network with robust communication via colony-based relays.

Abstract

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.

Figures (8)

• Figure 1: Research gaps in current swarm optimisation capabilities for underwater exploration and marine robotics applications.
• Figure 2: Feature comparison of swarm optimisation algorithms highlighting unique capabilities and limitations.
• Figure 3: NOAH three-phase methodology framework: Hydrodynamic Drift Adaptation, Irreversible Anchoring, and Colony Communication.
• Figure 4: Photographs of four killer whales (O. orca) observed off northern Baffin Island in the eastern Canadian Arctic with Xenobalanus barnacles attached to the trailing edge of their upper dorsal fins. Adapted from Matthews matthews2020epizoic.
• Figure 5: NOAH algorithm workflow showing the integration of optimisation phases from initialisation to convergence.