Automated pattern generation for swarm robots using constrained multi-objective genetic programming
文献类型:期刊论文
作者 | Fan, Zhun6,7,8; Wang, Zhaojun8; Li, Wenji8; Zhu, Xiaomin3,4; Hu, Bingliang2; Zou, An-Min8; Bao, Weidong3; Gu, Minqiang5; Hao, Zhifeng5; Jin, Yaochu1 |
刊名 | SWARM AND EVOLUTIONARY COMPUTATION |
出版日期 | 2023-08 |
卷号 | 81 |
ISSN号 | 2210-6502;2210-6510 |
关键词 | Gene regulatory network (GRN) Entrapping pattern generation Self-organization Constrained multi-objective genetic programming (CMOGP) |
DOI | 10.1016/j.swevo.2023.101337 |
产权排序 | 7 |
英文摘要 | Swarm robotic systems (SRSs), which are widely used in many fields, such as search and rescue, usually comprise a number of robots with relatively simple mechanisms collaborating to accomplish complex tasks. A challenging task for SRSs is to design local interaction rules for self-organization of robots that can generate adaptive patterns to entrap moving targets. Biologically inspired approaches such as gene regulatory network (GRN) models provide a promising solution to this problem. However, the design of GRN models for generating entrapping patterns relies on the expertise of designers. As a result, the design of the GRN models is often a laborious and tedious trial-and-error process. In this study, we propose a modular design automation framework for GRN models that can generate entrapping patterns. The framework employs basic network motifs to construct GRN models automatically without requiring expertise. To this end, a constrained multi-objective genetic programming is utilized to simultaneously optimize the structures and parameters of the GRN models. A multi-criteria decision-making approach is adopted to choose the preferred GRN model for generating the entrapping pattern. Comprehensive simulation results demonstrate that the proposed framework can obtain novel GRN models with simpler structures than those designed by human experts yet better performance in complex and dynamic environments. Proof-of-concept experiments using e-puck robots confirmed the feasibility and effectiveness of the proposed GRN models. |
语种 | 英语 |
出版者 | ELSEVIER |
WOS记录号 | WOS:001018319300001 |
源URL | [http://ir.opt.ac.cn/handle/181661/96558] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Jin, Yaochu |
作者单位 | 1.Bielefeld Univ, Fac Technol, D-33619 Bielefeld, Germany 2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710000, Peoples R China 3.Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Peoples R China 4.Acad Mil Sci, Strateg Assessments & Consultat Inst, Beijing 100091, Peoples R China 5.Shantou Univ, Coll Sci, Shantou 515063, Peoples R China 6.Shantou Univ, Key Lab Intelligent Mfg Technol, Minist Educ, Shantou 515063, Peoples R China 7.Int Cooperat Base Evolutionary Intelligence & Robo, Shantou 515063, Peoples R China 8.Shantou Univ, Dept Elect Engn, Shantou 515063, Peoples R China |
推荐引用方式 GB/T 7714 | Fan, Zhun,Wang, Zhaojun,Li, Wenji,et al. Automated pattern generation for swarm robots using constrained multi-objective genetic programming[J]. SWARM AND EVOLUTIONARY COMPUTATION,2023,81. |
APA | Fan, Zhun.,Wang, Zhaojun.,Li, Wenji.,Zhu, Xiaomin.,Hu, Bingliang.,...&Jin, Yaochu.(2023).Automated pattern generation for swarm robots using constrained multi-objective genetic programming.SWARM AND EVOLUTIONARY COMPUTATION,81. |
MLA | Fan, Zhun,et al."Automated pattern generation for swarm robots using constrained multi-objective genetic programming".SWARM AND EVOLUTIONARY COMPUTATION 81(2023). |
入库方式: OAI收割
来源:西安光学精密机械研究所
其他版本
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。