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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2510.15350 |
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| _version_ | 1866909002423599104 |
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| author | Ramesh, Shyalan Mann, Scott Stumpf, Alex |
| author_facet | Ramesh, Shyalan Mann, Scott Stumpf, Alex |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15350 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Nauplius Optimisation for Autonomous Hydrodynamics Ramesh, Shyalan Mann, Scott Stumpf, Alex Robotics Neural and Evolutionary Computing 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. |
| title | Nauplius Optimisation for Autonomous Hydrodynamics |
| topic | Robotics Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2510.15350 |