A Comprehensive Review of Bio-Inspired Approaches to Coordination, Communication, and System Architecture in Underwater Swarm Robotics

TL;DR

This paper addresses the fragmentation in underwater swarm robotics by unifying bio-inspired coordination, underwater communication constraints, and system design within a four-dimensional classification framework. It highlights four marine-specific algorithms—AFSA, WOA, CRO, and MPA—as representative methods and analyzes their trade-offs in communication dependency, environmental adaptability, energy efficiency, and swarm scalability, revealing distinct strengths and gaps. The review emphasizes cross-layer integration, empirical validation in realistic ocean conditions, and standardized benchmarking as key enablers for practical deployment, noting per-iteration complexity on the order of $O(N \cdot D)$ for these approaches. Field deployments such as COMET and NemoSens demonstrate decentralised TDMA-based coordination, but the literature still relies heavily on simulations with limited real-world trials, underscoring the need for holistic system-level studies, hybrid algorithm design, and open datasets to achieve robust, scalable, and energy-efficient marine swarm operations.

Abstract

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.

Figures (8)

• Figure S1: Publication trend for underwater swarm robotics research (2001 to 2025). The analysis includes 446 research articles from major databases, demonstrating exponential growth in the field. Search keywords included: marine swarm robotics, AUV swarm robotics, underwater swarm robotics, and related terms.
• Figure S2: Three-phase research methodology for synthesising the literature on bio-inspired underwater swarm robotics. The methodology integrates evidence gathering arksey2005scoping, systematic data extraction and coding braun2006thematic, and integrated synthesis jabareen2009building to consolidate findings across algorithmic, communication, and system design domains.
• Figure S3: Aquatic organisms that underpin many bio-inspired optimisation algorithms, illustrating representative marine species used as the biological basis for these methods.
• Figure S4: Visual perception and behaviour selection in the Artificial Fish Swarm Algorithm (AFSA). The central artificial fish perceives neighbouring agents and environmental stimuli within its visual range and dynamically selects between prey attraction, swarming, and following behaviours based on local sensory input. Coordination emerges from environmental perception rather than direct communication.
• Figure S5: Bubble-net feeding behaviour of humpback whales illustrating the search mechanism of the Whale Optimisation Algorithm (WOA).