1 School of Ophthalmology and Optometry, Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325035, China; 2 Postgraduate Training Base Alliance, Wenzhou Medical University, Wenzhou 325035, China; 3 Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325011, China; 4 School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
Abstract How to control the dynamic behavior of large-scale artificial active matter is a critical concern in experimental research on soft matter, particularly regarding the emergence of collective behaviors and the formation of group patterns. Centralized systems excel in precise control over individual behavior within a group, ensuring high accuracy and controllability in task execution. Nevertheless, their sensitivity to group size may limit their adaptability to diverse tasks. In contrast, decentralized systems empower individuals with autonomous decision-making, enhancing adaptability and system robustness. Yet, this flexibility comes at the cost of reduced accuracy and efficiency in task execution. In this work, we present a unique method for regulating the centralized dynamic behavior of self-organizing clusters based on environmental interactions. Within this environment-coupled robot system, each robot possesses similar dynamic characteristics, and their internal programs are entirely identical. However, their behaviors can be guided by the centralized control of the environment, facilitating the accomplishment of diverse cluster tasks. This approach aims to balance the accuracy and flexibility of centralized control with the robustness and task adaptability of decentralized control. The proactive regulation of dynamic behavioral characteristics in active matter groups, demonstrated in this work through environmental interactions, holds the potential to introduce a novel technological approach and provide experimental references for studying the dynamic behavior control of large-scale artificial active matter systems.
Received: 06 February 2024
Revised: 13 March 2024
Accepted manuscript online:
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 12174041), China Postdoctoral Science Foundation (CPSF) (Grant No. 2022M723118), and the seed grants from the Wenzhou Institute, University of Chinese Academy of Sciences (Grant No. WIUCASQD2021002).
Corresponding Authors:
Gao Wang, Fangfu Ye
E-mail: wanggao@ucas.ac.cn;ye@iphy.ac.cn
Cite this article:
Daming Yuan(袁大明), Peilong Wang(王培龙), Peng Wang(王鹏), Xingyu Ma(马星宇), Chuyun Wang(汪楚云), Jing Wang(王璟), Huaicheng Chen(陈怀城), Gao Wang(王高), and Fangfu Ye(叶方富) Controlling the dynamic behavior of decentralized cluster through centralized approaches 2024 Chin. Phys. B 33 060702
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