Exploring the criticality hypothesis using programmable swarm robots with Vicsek-like interactions

• Published in: Journal of the Royal Society interface Vol. 20; no. 204; p. 20230176
• Main Authors: Lei, Xiaokang, Xiang, Yalun, Duan, Mengyuan, Peng, Xingguang
• Format: Journal Article
• Language: English
• Published: England The Royal Society 19.07.2023
• Subjects: Animals; Computer Simulation; Life Sciences–Physics interface; Robotics; ordered–disordered motion transition; criticality; collective response; self-organization; alignment; swarm robots
• ISSN: 1742-5662; 1742-5689; 1742-5662
• DOI: 10.1098/rsif.2023.0176

A widely mentioned but not experimentally confirmed view (known as the ‘criticality hypothesis’) argues that biological swarm systems gain optimal responsiveness to perturbations and information processing capabilities by operating near the critical state where an ordered-to-disordered state transition occurs. However, various factors can induce the ordered–disordered transition, and the explicit relationship between these factors and the criticality is still unclear. Here, we present an experimental validation for the criticality hypothesis by employing real programmable swarm-robotic systems (up to 50 robots) governed by Vicsek-like interactions, subject to time-varying stimulus-response and hazard avoidance. We find that (i) not all ordered–disordered motion transitions correspond to the functional advantages for groups; (ii) collective response of groups is maximized near the critical state induced by alignment weight or scale rather than noise and other non-alignment factors; and (iii) those non-alignment factors act to highlight the functional advantages of alignment-induced criticality. These results suggest that the adjustability of velocity or directional coupling between individuals plays an essential role in the acquisition of maximizing collective response by criticality. Our results contribute to understanding the adjustment strategies of animal interactions from a perspective of criticality and provide insights into the design and control of swarm robotics.