A Review of Coordination, Congestion Management, and Learning-Based Approaches in Swarm Robotics Control

Swarm robotics has emerged as a transformative paradigm for accomplishing complex tasks through the coordinated operation of large numbers of simple robots. As swarm sizes scale from tens to hundreds of agents, effective control strategies become increasingly critical to prevent congestion, maintain system throughput, and ensure safe coordination. This review surveys the state of the art in swarm robotics control, with particular emphasis on coordination mechanisms and congestion management. Four major control paradigms are examined: centralized trajectory planning, reactive collision avoidance, spatial partitioning, and learning-based adaptive methods. Beyond these established approaches, the paper explores emerging hybrid frameworks, including Centralized Training with Decentralized Execution (CTDE) architectures, Graph Neural Network (GNN)-based coordination, and hybrid rule-learning integration. For each paradigm, a formal mathematical analysis is provided, covering computational complexity, convergence properties, and stability guarantees. To support rigorous and reproducible evaluation, this paper proposes a standardized assessment framework comprising mandatory performance metrics, scalability benchmarks, and systematic ablation protocols. Finally, open challenges are identified and future research directions outlined, with the aim of advancing the development of robust and deployable swarm robotic systems.