The research progress of swarm robotics is reviewed in details. The swarm robotics inspired from nature is a combination of swarm intelligence and robotics, which shows a great potential in several aspects. First of a...The research progress of swarm robotics is reviewed in details. The swarm robotics inspired from nature is a combination of swarm intelligence and robotics, which shows a great potential in several aspects. First of all, the cooperation of nature swarm and swarm intelligence are briefly introduced, and the special features of the swarm robotics are summarized compared to a single robot and other multi-individual systems. Then the modeling methods for swarm robotics are described by a list of several widely used swarm robotics entity projects and simulation platforms. Finally, as a main part of this paper, the current research on the swarm robotic algorithms are presented in detail, including cooperative control mechanisms in swarm robotics for flocking, navigating and searching applications.展开更多
In classical matter systems, typical phase-transition phenomena usually stem from changes in state variables, such as temperature and pressure, induced by external regulations such as heat transfer and volume adjustme...In classical matter systems, typical phase-transition phenomena usually stem from changes in state variables, such as temperature and pressure, induced by external regulations such as heat transfer and volume adjustment. However, in active matter systems, the self-propulsion nature of active particles endows the systems with the ability to induce unique collectivestate transitions by spontaneously regulating individual properties to alter the overall states. Based on an innovative robot-swarm experimental system, we demonstrate a field-driven active matter model capable of modulating individual motion behaviors through interaction with a recoverable environmental resource field by the resource perception and consumption.In the simulated model, by gradually reducing the individual resource-conversion coefficient over time, this robotic active matter can spontaneously decrease the overall level of motion, thereby actively achieving a regulation behavior like the cooling-down control. Through simulation calculations, we discover that the spatial structures of this robotic active matter convert from disorder to order during this process, with the resulting ordered structures exhibiting a high self-adaptability on the geometry of the environmental boundaries.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
For swarm robots moving in a harsh or uncharted outdoor environment without GPS guidance and global communication,algorithms that rely on global-based information are infeasible.Typically,traditional gene regulatory n...For swarm robots moving in a harsh or uncharted outdoor environment without GPS guidance and global communication,algorithms that rely on global-based information are infeasible.Typically,traditional gene regulatory networks(GRNs)that achieve superior performance in forming trapping pattern towards targets require accurate global positional information to guide swarm robots.This article presents a gene regulatory network with Self-organized grouping and entrapping method for swarms(SUNDER-GRN)to achieve adequate trapping performance with a large-scale swarm in a confined multitarget environment with access to only local information.A hierarchical self-organized grouping method(HSG)is proposed to structure subswarms in a distributed way.In addition,a modified distributed controller,with a relative coordinate system that is established to relieve the need for global information,is leveraged to facilitate subswarms entrapment toward different targets,thus improving the global multi-target entrapping performance.The results demonstrate the superiority of SUNDERGRN in the performance of structuring subswarms and entrapping 10 targets with 200 robots in an environment confined by obstacles and with only local information accessible.展开更多
The complexity of unknown scenarios and the dynamics involved in target entrapment make designing control strategies for swarm robots a formidable task,which in turn impacts their efficiency in complex and dynamic set...The complexity of unknown scenarios and the dynamics involved in target entrapment make designing control strategies for swarm robots a formidable task,which in turn impacts their efficiency in complex and dynamic settings.To address these challenges,this paper introduces an adaptive swarm robot entrapment control model grounded in the transformation of gene regulatory networks(AT-GRN).This innovative model enables swarm robots to dynamically adjust entrap-ment strategies by assessing current environmental conditions via real-time sensory data.Further-more,an improved motion control model for swarm robots is designed to dynamically shape the for-mation generated by the AT-GRN.Through two sets of rigorous experimental environments,the proposed model significantly enhances the trapping performance of swarm robots in complex envi-ronments,demonstrating remarkable adaptability and stability.展开更多
基金Sponsored by National Natural Science Foundation of China under Grant( 61170057,60875080)
文摘The research progress of swarm robotics is reviewed in details. The swarm robotics inspired from nature is a combination of swarm intelligence and robotics, which shows a great potential in several aspects. First of all, the cooperation of nature swarm and swarm intelligence are briefly introduced, and the special features of the swarm robotics are summarized compared to a single robot and other multi-individual systems. Then the modeling methods for swarm robotics are described by a list of several widely used swarm robotics entity projects and simulation platforms. Finally, as a main part of this paper, the current research on the swarm robotic algorithms are presented in detail, including cooperative control mechanisms in swarm robotics for flocking, navigating and searching applications.
基金Project supported by the National Natural Science Foundation of China(Grant No.12174041)China Postdoctoral Science Foundation(Grant No.2022M723118)the Seed Grants from the Wenzhou Institute,University of Chinese Academy of Sciences(Grant No.WIUCASQD2021002)。
文摘In classical matter systems, typical phase-transition phenomena usually stem from changes in state variables, such as temperature and pressure, induced by external regulations such as heat transfer and volume adjustment. However, in active matter systems, the self-propulsion nature of active particles endows the systems with the ability to induce unique collectivestate transitions by spontaneously regulating individual properties to alter the overall states. Based on an innovative robot-swarm experimental system, we demonstrate a field-driven active matter model capable of modulating individual motion behaviors through interaction with a recoverable environmental resource field by the resource perception and consumption.In the simulated model, by gradually reducing the individual resource-conversion coefficient over time, this robotic active matter can spontaneously decrease the overall level of motion, thereby actively achieving a regulation behavior like the cooling-down control. Through simulation calculations, we discover that the spatial structures of this robotic active matter convert from disorder to order during this process, with the resulting ordered structures exhibiting a high self-adaptability on the geometry of the environmental boundaries.
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.
基金supported in part by National Key R&D Program of China(Grant Nos.2021ZD0111501,2021ZD0111502)the Key Laboratory of Digital Signal and Image Processing of Guangdong Province+8 种基金the Key Laboratory of Intelligent Manufacturing Technology(Shantou University)Ministry of Education,the Science and Technology Planning Project of Guangdong Province of China(Grant No.180917144960530)the Project of Educational Commission of Guangdong Province of China(Grant No.2017KZDXM032)the State Key Lab of Digital Manufacturing Equipment&Technology(grant number DMETKF2019020)National Natural Science Foundation of China(Grant Nos.62176147,62002369)STU Scientific Research Foundation for Talents(Grant No.NTF21001)Science and Technology Planning Project of Guangdong Province of China(Grant Nos.2019A050520001,2021A0505030072,2022A1515110660)Science and Technology Special Funds Project of Guangdong Province of China(Grant Nos.STKJ2021176,STKJ2021019)Guangdong Special Support Program for Outstanding Talents(Grant No.2021JC06X549)。
文摘For swarm robots moving in a harsh or uncharted outdoor environment without GPS guidance and global communication,algorithms that rely on global-based information are infeasible.Typically,traditional gene regulatory networks(GRNs)that achieve superior performance in forming trapping pattern towards targets require accurate global positional information to guide swarm robots.This article presents a gene regulatory network with Self-organized grouping and entrapping method for swarms(SUNDER-GRN)to achieve adequate trapping performance with a large-scale swarm in a confined multitarget environment with access to only local information.A hierarchical self-organized grouping method(HSG)is proposed to structure subswarms in a distributed way.In addition,a modified distributed controller,with a relative coordinate system that is established to relieve the need for global information,is leveraged to facilitate subswarms entrapment toward different targets,thus improving the global multi-target entrapping performance.The results demonstrate the superiority of SUNDERGRN in the performance of structuring subswarms and entrapping 10 targets with 200 robots in an environment confined by obstacles and with only local information accessible.
基金supported in part by the National Science and Technol-ogy Major Project(No.2021ZD0111502)the National Nat-ural Science Foundation of China(Nos.62176147,62476163)+2 种基金the Science and Technology Planning Project of Guangdong Province of China(Nos.2022A1515110660,2021JC06X549)the STU Scientific Research Foundation for Talents(No.NTF21001)Guangdong Basic and Applied Basic Research Foundation(No.2023B1515120020)。
文摘The complexity of unknown scenarios and the dynamics involved in target entrapment make designing control strategies for swarm robots a formidable task,which in turn impacts their efficiency in complex and dynamic settings.To address these challenges,this paper introduces an adaptive swarm robot entrapment control model grounded in the transformation of gene regulatory networks(AT-GRN).This innovative model enables swarm robots to dynamically adjust entrap-ment strategies by assessing current environmental conditions via real-time sensory data.Further-more,an improved motion control model for swarm robots is designed to dynamically shape the for-mation generated by the AT-GRN.Through two sets of rigorous experimental environments,the proposed model significantly enhances the trapping performance of swarm robots in complex envi-ronments,demonstrating remarkable adaptability and stability.
