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...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.展开更多
Boundary effect and time-reversal symmetry are hot topics in active matter. We present a biology-inspired robotenvironment-interaction active matter system with the field-drive motion and the rules of resource search,...Boundary effect and time-reversal symmetry are hot topics in active matter. We present a biology-inspired robotenvironment-interaction active matter system with the field-drive motion and the rules of resource search, resource consumption, and resource recovery. In an environmental compression–expansion cycle, the swarm emerges a series of boundary-dependent phase transitions, and the whole evolution process is time-reversal symmetry-breaking;we call this phenomenon “orderly hysteresis”. We present the influence of the environmental recovery rate on the dynamic collective behavior of the swarm.展开更多
How biologically active matters survive adaptively in complex and changeable environments is a common concern of scientists.Genetics,evolution and natural selection are vital factors in the process of biological evolu...How biologically active matters survive adaptively in complex and changeable environments is a common concern of scientists.Genetics,evolution and natural selection are vital factors in the process of biological evolution and are also the key to survival in harsh environments.However,it is challenging to intuitively and accurately reproduce such longterm adaptive survival processes in the laboratory.Although simulation experiments are intuitive and efficient,they lack fidelity.Therefore,we propose to use swarm robots to study the adaptive process of active matter swarms in complex and changeable environments.Based on a self-built virtual environmental platform and a robot swarm that can interact with the environment,we introduce the concept of genes into the robot system,giving each robot unique digital genes,and design robot breeding methods and rules for gene mutations.Our previous work[Proc.Natl.Acad.Sci.USA 119 e2120019119(2022)]has demonstrated the effectiveness of this system.In this work,by analyzing the relationship between the genetic traits of the population and the characteristics of environmental resources,and comparing different experimental conditions,we verified in both robot experiments and corresponding simulation experiments that agents with genetic inheritance can survive for a long time under the action of natural selection in periodically changing environments.We also confirmed that in the robot system,both breeding and mutation are essential factors.These findings can help answer the practical scientific question of how individuals and swarms can successfully adapt to complex,dynamic,and unpredictable actual environments.展开更多
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)。
文摘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.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 11974066 and 12174041)the Seed Grants from the Wenzhou Institute, University of Chinese Academy of Sciences (Grant No. WIUCASQD2021002)。
文摘Boundary effect and time-reversal symmetry are hot topics in active matter. We present a biology-inspired robotenvironment-interaction active matter system with the field-drive motion and the rules of resource search, resource consumption, and resource recovery. In an environmental compression–expansion cycle, the swarm emerges a series of boundary-dependent phase transitions, and the whole evolution process is time-reversal symmetry-breaking;we call this phenomenon “orderly hysteresis”. We present the influence of the environmental recovery rate on the dynamic collective behavior of the swarm.
基金Project supported by the National Natural Science Foundation of China(Grant No.12174041)China Postdoctoral Science Foundation(Grant No.2022M723118)+1 种基金the seed grants from the Wenzhou InstituteUniversity of Chinese Academy of Sciences(Grant No.WIUCASQD2021002)。
文摘How biologically active matters survive adaptively in complex and changeable environments is a common concern of scientists.Genetics,evolution and natural selection are vital factors in the process of biological evolution and are also the key to survival in harsh environments.However,it is challenging to intuitively and accurately reproduce such longterm adaptive survival processes in the laboratory.Although simulation experiments are intuitive and efficient,they lack fidelity.Therefore,we propose to use swarm robots to study the adaptive process of active matter swarms in complex and changeable environments.Based on a self-built virtual environmental platform and a robot swarm that can interact with the environment,we introduce the concept of genes into the robot system,giving each robot unique digital genes,and design robot breeding methods and rules for gene mutations.Our previous work[Proc.Natl.Acad.Sci.USA 119 e2120019119(2022)]has demonstrated the effectiveness of this system.In this work,by analyzing the relationship between the genetic traits of the population and the characteristics of environmental resources,and comparing different experimental conditions,we verified in both robot experiments and corresponding simulation experiments that agents with genetic inheritance can survive for a long time under the action of natural selection in periodically changing environments.We also confirmed that in the robot system,both breeding and mutation are essential factors.These findings can help answer the practical scientific question of how individuals and swarms can successfully adapt to complex,dynamic,and unpredictable actual environments.
