A Minimalist Controller for Autonomously Self-Aggregating Robotic Swarms: Enabling Compact Formations in Multitasking Scenarios
In multi-task scenarios, swarm robots struggle to achieve fully autonomous, high-density, and non-circular compact formations. Method: This paper proposes a vision-based distributed self-aggregation approach, employing minimalist isomorphic robot controllers and local interaction rules that rely solely on line-of-sight (LoS) sensing—requiring no global information or external intervention—to enable group separation and stable aggregation. Contribution/Results: The method achieves, for the first time, fully autonomous and scalable generation of multiple compact clusters. It effectively suppresses inter-cluster dynamic interference and demonstrates robustness across varying swarm sizes and cluster counts in simulation. Experimental results show clustering ratios comparable to state-of-the-art methods, yet with significantly higher cluster density, tighter morphology, and enhanced environmental adaptability and scalability.