Swarm Robotics, Swarm Intelligence and Machine Learning: A Classification Framework and Review of Hybrid Swarm Architectures

Swarm robotics (SR) and swarm intelligence (SI) share decentralization and collective behavior but address different problems. SR concerns physical multi-robot systems whose collective behavior must hold under sensing, communication, energy, and safety constraints, while SI includes computational models and optimization methods, many of which involve no robots. Conflating them obscures which SI results transfer to robot swarms and what machine learning (ML) contributes. This review examines SR coordination, SI optimization, and ML-augmented hybrid methods from a deployment perspective. It proposes a tiered framework for deciding whether a system is a robotic swarm, a taxonomy of five hybrid SR–SI–ML architecture types, and feasibility conditions for combining methods on a given platform. Across 17 coded studies, SI optimizers such as Ant Colony Optimization and Particle Swarm Optimization are evaluated mainly on benchmarks or in simulation and reach hardware only with supporting infrastructure; hardware experiments, with one exception, use twenty robots or fewer, and learned controllers transfer to hardware in some studies but show a large simulation-to-reality gap in others. Layered hybrid architectures, in which SR provides coordination, SI provides optimization and ML provides adaptation, remain a design hypothesis. The review proposes a reporting checklist for future studies.