摘要
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 National Key R&D Program of China(Grant Nos.2021ZD0111501,2021ZD0111502)
the Key Laboratory of Digital Signal and Image Processing of Guangdong Province
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)。
