Comprehensive review of swarm intelligence for space robotics

Abstract

Swarm intelligence has emerged as a transformative paradigm for autonomous space robotics, enabling scalable, robust, and adaptive behaviors through decentralized coordination of multiple agents. Inspired by collective phenomena in nature, swarm intelligence provides solutions to the challenges of extreme space environments, where resilience, autonomy, and fault tolerance are crucial. This review explores recent advances in the modeling, control, and validation of swarm-based space robotic systems. Mathematical frameworks ranging from single- and double-integrator dynamics to orbital swarm dynamics are examined, alongside formation control strategies such as consensus-based, leader–follower, virtual structure, and behavior-based approaches. The review covers swarm controllability, scalability, and performance metrics, highlighting trade-offs between efficiency, robustness, and computational complexity. Emerging optimization paradigms, including bio-inspired algorithms, hybrid global-local strategies, and multi-objective optimization, are surveyed for their applicability to mission-critical tasks such as debris removal, and distributed satellite constellations. The review also investigates numerical simulation platforms and experimental testbeds associated with swarm intelligence, highlighting their role in bridging the gap between theory and deployment. Case studies of current and proposed space missions illustrate the transition of swarm intelligence from conceptual design to operational reality, while trends in reinforcement learning, blockchain integration, and large language model-guided swarms signal future research directions. By consolidating theoretical foundations, experimental progress, and mission applications, this paper outlines the opportunities and challenges of harnessing swarm intelligence for future space exploration and infrastructure.