Controlling the dynamic behavior of decentralized cluster through centralized approaches

作     者：袁大明 王培龙 王鹏 马星宇 汪楚云 王璟 陈怀城 王高 叶方富 Daming Yuan;Peilong Wang;Peng Wang;Xingyu Ma;Chuyun Wang;Jing Wang;Huaicheng Chen;Gao Wang;Fangfu Ye

作者机构：School of Ophthalmology and OptometryEye HospitalSchool of Biomedical EngineeringWenzhou Medical UniversityWenzhou 325035China Postgraduate Training Base AllianceWenzhou Medical UniversityWenzhou 325035China Wenzhou InstituteUniversity of Chinese Academy of SciencesWenzhou 325011China School of Physical SciencesUniversity of Chinese Academy of SciencesBeijing 100049China 

出 版 物：《Chinese Physics B》 (中国物理B（英文版）)

年 卷 期：2024年第33卷第6期

页      面：46-54页

核心收录：

学科分类：080202[工学-机械电子工程] 08[工学] 0804[工学-仪器科学与技术] 0802[工学-机械工程] 

基　　金：Project supported by the National Natural Science Foundation of China (Grant No. 12174041) China Postdoctoral Science Foundation (CPSF)(Grant No. 2022M723118) the seed grants from the Wenzhou Institute,University of Chinese Academy of Sciences (Grant No. WIUCASQD2021002) 

主　　题：self-organizing system centralized control dynamics regulation 

摘      要：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.