南方科技大学知识苑(SUSTech KC): Cooperative coevolution of real predator robots and virtual robots in the pursuit domain

题名	Cooperative coevolution of real predator robots and virtual robots in the pursuit domain
作者	Sun，Lijun1,2 ; Lyu，Chao 1,3; Shi，Yuhui1
通讯作者	Shi，Yuhui
发表日期	2020-04-01
DOI	10.1016/j.asoc.2020.106098
发表期刊	APPLIED SOFT COMPUTING 影响因子和分区
ISSN	1568-4946
EISSN	1872-9681
卷号	89
摘要	The pursuit domain, or predator–prey problem is a standard testbed for the study of coordination techniques. In spite that its problem setup is apparently simple, it is challenging for the research of the emerged swarm intelligence. This paper presents a particle swarm optimization (PSO) based cooperative coevolutionary algorithm for the (predator) robots, called CCPSO-R, where real and virtual robots coexist in an evolutionary algorithm (EA). Virtual robots sample and explore the vicinity of the corresponding real robots and act as their action spaces, while the real robots consist of the real predators who actually pursue the prey robot without fixed behavior rules under the immediate guidance of the fitness function, which is designed in a modular manner with very limited domain knowledge. In addition, kinematic limits and collision avoidance considerations are integrated into the update rules of robots. Experiments are conducted on a scalable swarm of predator robots with 4 types of preys, the results of which show the reliability, generality, and scalability of the proposed CCPSO-R. Comparison with a representative dynamic path planning based algorithm Multi-Agent Real-Time Pursuit (MAPS) further shows the effectiveness of CCPSO-R. Finally, the codes of this paper are public available at: https://github.com/LijunSun90/pursuitCCPSOR.
关键词	Cooperative coevolution Particle swarm optimization Pursuit domain Swarm intelligence Virtual robot
相关链接	[Scopus记录]
收录类别	EI ; SCI
语种	英语
学校署名	第一 ; 通讯
资助项目	National Basic Research Program of China (973 Program)[2017YFC0804002] ; Guangdong Province Introduction of Innovative R&D Team[2017ZT07X386] ; National Natural Science Foundation of China[61761136008] ; Shenzhen Peacock Plan[KQTD2016112514355531] ; Shenzhen Science and Technology Innovation Commission[ZDSYS201703031748284]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:000520042200025
出版者	Elsevier Ltd
EI入藏号	20200408076888
EI主题词	Intelligent robots ; Motion planning ; Multi agent systems ; Particle swarm optimization (PSO)
EI分类号	Computer Software, Data Handling and Applications:723 ; Robot Applications:731.6
ESI学科分类	COMPUTER SCIENCE
Scopus记录号	2-s2.0-85078153560
来源库	Scopus
引用统计	被引频次[WOS]：5
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/64972
专题	工学院_计算机科学与工程系
作者单位	1.Shenzhen Key Laboratory of Computational Intelligence,Department of Computer Science and Engineering,Southern University of Science and Technology,China 2.Centre for Artificial Intelligence,CIBCI Lab,Faculty of Engineering and Information Technology,University of Technology Sydney,Australia 3.Harbin Institute of Technology,China
第一作者单位	计算机科学与工程系
通讯作者单位	计算机科学与工程系
第一作者的第一单位	计算机科学与工程系
推荐引用方式 GB/T 7714	Sun，Lijun,Lyu，Chao,Shi，Yuhui. Cooperative coevolution of real predator robots and virtual robots in the pursuit domain[J]. APPLIED SOFT COMPUTING,2020,89.
APA	Sun，Lijun,Lyu，Chao,&Shi，Yuhui.(2020).Cooperative coevolution of real predator robots and virtual robots in the pursuit domain.APPLIED SOFT COMPUTING,89.
MLA	Sun，Lijun,et al."Cooperative coevolution of real predator robots and virtual robots in the pursuit domain".APPLIED SOFT COMPUTING 89(2020).