Bio-inspired Shape Self-Assembly in Large-Scale Swarm Robots Under Information Asymmetry *

Abstract

This study investigates the problem of large-scale swarm robots shape self-assembly problem under conditions of information asymmetry. Existing methods assume complete sharing of global information; however, this assumption has significant limitations in terms of resource consumption and swarm emergence. On the other hand, strategies that rely entirely on local information struggling to achieve the self-assembly of complex shapes, especially disconnected shapes. To address these challenges, this study proposes a novel bio-inspired distributed self-assembly strategy specifically designed for information asymmetric swarm. The strategy draws inspiration from the task specialization mechanism between scout ants and worker ants in social insects, guiding individuals to efficiently complete shape-assembly under information asymmetry through local perception, neighborhood interactions, and dynamic rule adjustments. Experimental results show that the proposed strategy successfully achieves the self-assembly of various shapes, including simple shapes (e.g., circle, rectangle), complex shapes (e.g., human, flower, and letter "A"), and disconnected shapes (e.g., letter "IO"). This demonstrates the strategy’s adaptability to shape complexity. Furthermore, experiments with varying swarm sizes validate the strategy’s robustness and scalability across different scales. During the experiments, we unexpectedly observed emergent behaviors within the swarm, further confirming that the proposed strategy not only significantly enhances task flexibility but also strengthens swarm emergence. These results indicate that the proposed method provides an efficient, scalable, and innovative solution for swarm robots self-assembly under information asymmetry.