对水下群体机器人协调、通信和系统架构中生物启发方法的综合回顾
A Comprehensive Review of Bio-Inspired Approaches to Coordination, Communication, and System Architecture in Underwater Swarm Robotics
AI总结：
本文综述了水下群体机器人中生物启发方法的协调、通信和系统架构，分析了关键算法、通信限制及硬件设计，提出多维分类框架以指导未来研究方向。
摘要：
随着海洋操作复杂性的增加，对智能机器人系统支持海洋观测、探索和资源管理的需求日益增强。水下群体机器人提供了一个有前景的框架，通过集体协调扩展单个自主平台的能力。受自然系统如鱼群和昆虫群落的启发，生物启发式群体方法使分布式决策、适应性和在挑战性海洋条件下具备韧性。然而，该领域研究仍然碎片化，算法、通信和硬件设计视角的整合有限。本文综述了水下群体机器人的生物启发协调机制、通信策略和系统设计考虑。它检查了关键的海洋特定算法，包括人工鱼群算法、鲸鱼优化算法、珊瑚礁优化和海洋捕食者算法，突显其在编队控制、任务分配和环境交互中的应用。本文还分析了水下领域的通信限制，并探讨了新兴的声学、光学和混合解决方案，以支持协作操作。此外，它还研究了硬件和系统设计的进步，以提高系统效率和可扩展性。一个多维分类框架评估了现有方法在通信依赖性、环境适应性、能耗效率和群体可扩展性方面的表现。通过这种综合分析，本文统一了生物启发式协调算法、通信模式和系统设计方法。它还识别了趋同趋势、关键挑战和未来研究方向，以促进水下群体系统的实际部署。
英文摘要：
The increasing complexity of marine operations has intensified the need for intelligent robotic systems to support ocean observation, exploration, and resource management. Underwater swarm robotics offers a promising framework that extends the capabilities of individual autonomous platforms through collective coordination. Inspired by natural systems, such as fish schools and insect colonies, bio-inspired swarm approaches enable distributed decision-making, adaptability, and resilience under challenging marine conditions. Yet research in this field remains fragmented, with limited integration across algorithmic, communication, and hardware design perspectives. This review synthesises bio-inspired coordination mechanisms, communication strategies, and system design considerations for underwater swarm robotics. It examines key marine-specific algorithms, including the Artificial Fish Swarm Algorithm, Whale Optimisation Algorithm, Coral Reef Optimisation, and Marine Predators Algorithm, highlighting their applications in formation control, task allocation, and environmental interaction. The review also analyses communication constraints unique to the underwater domain and emerging acoustic, optical, and hybrid solutions that support cooperative operation. Additionally, it examines hardware and system design advances that enhance system efficiency and scalability. A multi-dimensional classification framework evaluates existing approaches across communication dependency, environmental adaptability, energy efficiency, and swarm scalability. Through this integrated analysis, the review unifies bio-inspired coordination algorithms, communication modalities, and system design approaches. It also identifies converging trends, key challenges, and future research directions for real-world deployment of underwater swarm systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
