基于简化虚拟受力模型的未知复杂环境下群机器人围捕

doi:10.3969/j.issn.0372-2112.2015.04.007

电子学报 ›› 2015, Vol. 43 ›› Issue (4): 665-674.DOI: 10.3969/j.issn.0372-2112.2015.04.007

基于简化虚拟受力模型的未知复杂环境下群机器人围捕

张红强^1,2, 章兢¹, 周少武², 曾照福², 吴亮红²

1. 湖南大学电气与信息工程学院, 湖南长沙 410082;
2. 湖南科技大学信息与电气工程学院, 湖南湘潭 411201

收稿日期:2014-03-28 修回日期:2014-08-01 出版日期:2015-04-25 发布日期:2015-04-25
作者简介:张红强 男,1979年2月出生,河南延津人,讲师.2001年和2004年分别获得湖南科技大学学士学位和湖南大学硕士学位,其后在湖南科技大学从事教学工作,2011年进入湖南大学攻读博士学位.从事群机器人系统、群体智能、优化与智能控制的有关研究.E-mail:hongniuok@qq.com;章兢男,1957年1月出生,湖南韶山人,教授、博士生导师.1982年、1984年和1997年分别获得湖南大学学士、硕士和博士学位.现为湖南大学教授,获国家科技进步奖二等奖1项,出版专著4部,从事智能控制理论与应用、复杂系统工业控制、节能减排管控一体化、智能系统的有关研究.E-mail:zhangj@hnu.cn
基金资助:
国家自然科学基金(No.61174140,No.51374107,No.61203016,No.61174050);国家自然科学青年基金(No.61203309);湖南省自然科学基金(No.13JJ8014);湖南省教育厅优秀青年项目(No.12B043);博士点基金(No.20110161110035);国家科技支撑计划(No.2012BAH09B02)

Hunting in Unknown Complex Environments by Swarm Robots Based on Simplified Virtual-Force Model

ZHANG Hong-qiang^1,2, ZHANG Jing¹, ZHOU Shao-wu², ZENG Zhao-fu², WU Liang-hong²

1. College of Electrical and Information Engineering, Hunan University, Changsha, Hunan 410082, China;
2. College of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China

Received:2014-03-28 Revised:2014-08-01 Online:2015-04-25 Published:2015-04-25

摘要/Abstract

摘要：

针对未知非凸和凸以及动态障碍物环境下群机器人围捕,提出了一种基于简化虚拟受力模型的循障和围捕方法.首先给出了目标和动态障碍物的运动模型.然后通过对复杂环境下围捕行为的分解,抽象出简化虚拟受力模型.基于此模型,设计了个体循障和围捕方法,接着证明了系统的稳定性并给出了参数设置范围.仿真结果表明,本文围捕方法可以使群机器人在未知复杂环境下保持较好的围捕队形,并具有良好的避障性能和灵活性,同时分析了与基于松散偏好规则的围捕方法相比的优势.

关键词: 移动机器人, 群机器人, 非凸障碍物, 简化虚拟受力模型, 避碰, 队形保持

Abstract:

In this paper,a method based on a simplified virtual-force model is proposed for swarm robots hunting in unknown non-convex,convex and dynamic obstacles environments.First,the motion models for the hunting target and dynamic obstacles are presented.Then through the decomposition of hunting behaviors under complicated environments,a simplified virtual-force model is formed.Based on the model,the control method is designed for swarm robots following motions of barriers and the hunting target;the stability of the hunting system is analyzed and the control parameter ranges are given.Simulation results demonstrate that the proposed method can maintain a good hunting formation and has good performance of obstacles avoidance and flexibility.Finally,advantages of this method are presented,compared with the hunting method based on loose preference rule.

Key words: mobile robots, swarm robots, non-convex obstacles, simplified virtual-force model, collision avoidance, formation keeping

中图分类号:

TP24

张红强, 章兢, 周少武, 曾照福, 吴亮红. 基于简化虚拟受力模型的未知复杂环境下群机器人围捕[J]. 电子学报, 2015, 43(4): 665-674.

ZHANG Hong-qiang, ZHANG Jing, ZHOU Shao-wu, ZENG Zhao-fu, WU Liang-hong. Hunting in Unknown Complex Environments by Swarm Robots Based on Simplified Virtual-Force Model[J]. Acta Electronica Sinica, 2015, 43(4): 665-674.

参考文献

[1] Sahin E.Swarm Robotics:From Sources of Inspiration to Domains of Application[M].Berlin,Heidelberg:Springer,2005.10-20.
[2] Phan T A,Russell R A.A swarm robot methodology for collaborative manipulation of non-identical objects[J].The International Journal of Robotics Research,2012,31(1):101-122.
[3] 张勇,巩敦卫,胡滢,等.室内噪声环境下气味源的多机器人微粒群搜索方法[J].电子学报,2014,42(1):70-76. Zhang Yong,Gong Dun-wei,Hu Ying,et al.A PSO-based multi-robot search method for odor source in indoor environment with noise[J].Acta Electronica Sinica,2014,42(1):70-76.(in Chinese)
[4] Brambilla M,Ferrante E,Birattari M,et al.Swarm robotics:A review from the swarm engineering perspective[J].Swarm Intelligence,2013,7(1):1-41.
[5] Ni J J,Yang S X.Bioinspired neural network for real-time cooperative hunting by multi-robots in unknown environments[J].IEEE Transactions on Neural Networks,2011,22(12):2062-2077.
[6] 胡俊,朱庆保.基于动态预测目标轨迹和围捕点的多机器人围捕算法[J].电子学报,2011,39(11):2480-2485. Hu Jun,Zhu Qing-bao.A multi-robot hunting algorithm based on dynamic prediction for trajectory of the moving target and hunting points[J].Acta Electronica Sinica,2011,39(11):2480-2485.(in Chinese)
[7] 李,潘启树,周浦城,等.未知环境下多机器人协作追捕算法[J].电子学报,2011,39(3):567-574. Li Jun,Pan Qi-shu,Zhou Pu-cheng,et al.Multi-robot cooperative pursuit algorithm for in an unknown environment[J].Acta Electronica Sinica,2011,39(3):567-574.(in Chinese)
[8] Xiong J F,Tan G Z.Virtual forces based approach for target capture with swarm robots[A].Proceedings of Control and Decision Conference[C].China:CDC Press,2009.642-646.
[9] Pimenta L C A,Pereira G A S,Michael N,et al.Swarm coordination based on smoothed particle hydrodynamics technique[J].IEEE Transactions on Robotics,2013,29(2):383-399.
[10] Ghanaatpishe M,Mousavi S M A,Abedini M,et al.Following,surrounding and hunting an escaping target by stochastic control of swarm in multi-agent systems[A].Proceedings of the 2nd International Conference on Control,Instrumentation and Automation[C].Bandung:IEEE,2011.576-581.
[11] Cai Z S,Zhao J,Cao J.Formation control and obstacle avoidance for multiple robots subject to wheel-slip[J].International Journal of Advanced Robotic Systems,2012,9(188):1-15.
[12] Huang T Y,Chen X B,Xu W B,et al.A self-organizing cooperative hunting by swarm robotic systems based on loose-preference rule[J].Acta Automatica Sinica,2013,39(1):57-68.
[13] Wu M,Huang F F,Wang L,et al.A distributed multi-robot cooperative hunting algorithm based on limit-cycle[A].Proceedings of International Asia Conference on Automation and Robotics in Informatics in Control[C].Bangkok:IEEE,2009.156-160.
[14] Thrun S,Burgard W,Fox D.Probabilistic Robotics[M].Cambridge,Massachusetts,United States:MIT Press,2005.91-119
[15] Xu W B,Chen X B,Zhao J,et al.A decentralized method using artificial moments for multi-robot path-planning[J].International Journal of Advanced Robotic Systems,2013,10(24):1-12.
[16] Muro C,Escobedo R,Spector L,et al.Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations[J].Behavioural Processes,2011,88(3):192-197.
[17] 蔡自兴,肖正,于金霞.基于激光雷达的动态障碍物实时检测[J].控制工程,2008,15(2):200-203. Cai Zi-xing,Xiao Zheng,Yu Jin-xia.Real-time detection of dynamic obstacles based on laser radar[J].Control Engineering of China,2008,15(2):200-203.(in Chinese)

基于简化虚拟受力模型的未知复杂环境下群机器人围捕

Hunting in Unknown Complex Environments by Swarm Robots Based on Simplified Virtual-Force Model

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