[1]范 衠,马培立,朱贵杰,等.基于三维基因调控网络的智能机器人群体聚合与控制方法[J].南京师范大学学报(工程技术版),2022,(01):009-15.[doi:10.3969/j.issn.1672-1292.2022.01.002]
 Fan Zhun,Ma Peili,Zhu Guijie,et al.Method of Swarm Aggregation and Control for Intelligent RobotBased on Three-dimensional Gene Regulatory Network[J].Journal of Nanjing Normal University(Engineering and Technology),2022,(01):009-15.[doi:10.3969/j.issn.1672-1292.2022.01.002]
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基于三维基因调控网络的智能机器人群体聚合与控制方法
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2022年01期
页码:
009-15
栏目:
机器学习
出版日期:
2022-03-15

文章信息/Info

Title:
Method of Swarm Aggregation and Control for Intelligent RobotBased on Three-dimensional Gene Regulatory Network
文章编号:
1672-1292(2022)01-0009-07
作者:
范 衠12马培立12朱贵杰12谢敏冲1陈添善1谢 飞1石 泽12包卫东3朱晓敏3
(1.汕头大学工学院,广东 汕头 515063)(2.汕头大学广东省数字信号与图像处理技术重点实验室,广东 汕头 515063)(3.国防科技大学系统工程学院,湖南 长沙 410073)
Author(s):
Fan Zhun12Ma Peili12Zhu Guijie12Xie Minchong1Chen Tianshan1Xie Fei1Shi Ze12Bao Weidong3Zhu Xiaomin3
(1.College of Engineering,Shantou University,Shantou 515063,China)(2.Key Lab of Digital Signal and Image Processing of Guangdong Province,Shantou University,Shantou 515063,China)(3.College of Systems Engineering,National University of Defense Technology,Changsha 410073,China)
关键词:
群体智能机器人基因调控网络三维空间群体聚合形态分布式围捕
Keywords:
intelligent swarm robotsgene regulatory networkthree-dimensional spaceswarm aggregation morphologydistributed trapping
分类号:
TP242.6
DOI:
10.3969/j.issn.1672-1292.2022.01.002
文献标志码:
A
摘要:
针对传统的基因调控网络模型会导致机器人群体聚合形态在三维空间中不具备泛化性的问题,提出一种基于三维基因调控网络的智能机器人群体聚合与控制方法,并针对集群围捕任务测试了该方法在三维空间复杂场景下的表现. 在此基础上使用V-rep平台来模拟真实场景,验证了在加入物理特性后该模型的效能. 仿真结果表明,该方法在复杂场景下具有良好的表现.
Abstract:
To address the problem that the traditional gene regulatory network(GRN)model could not generate suitable swarm aggregation morphology patterns in three-dimensional space,an aggregation and control method of swarm intelligent robot based on 3D GRN is proposed. Then,the performance of the method is tested in three-dimensional complex scenes in entrapping task. Furthermore,the V-rep platform is used to simulate real scenarios and verify the efficacy of the model when the environment is added to the physics engine. Simulation results show that the proposed method has good performance and is robust under complex scenarios.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2021-08-31.
基金项目:中央军委科技委基础研究项目(18-163-11-ZT-003-008-02)、中央军委科技委基础研究项目(193-A14-226-01-01).
通讯作者:朱晓敏,博士,教授,研究方向:控制理论与控制工程、群体智能. E-mail:xmzhu@nudt.edu.cn
更新日期/Last Update: 2022-03-15