群机器人多目标搜索中带闭环调节的动态任务分工

doi:10.3724/SP.J.1218.2014.00057

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引用本文:
张云正, 薛颂东, 曾建潮. 群机器人多目标搜索中带闭环调节的动态任务分工[J]. 机器人, 2014, 36(1): 57-68.DOI: 10.3724/SP.J.1218.2014.00057. ZHANG Yunzheng, XUE Songdong, ZENG Jianchao. Dynamic Task Allocation with Closed-Loop Adjusting in Swarm Robotic Search for Multiple Targets. ROBOT, 2014, 36(1): 57-68. DOI: 10.3724/SP.J.1218.2014.00057.

摘要

群机器人在并行化地同时搜索多个目标时，须通过任务分工形成若干个子群联盟，每个子群分别针对一个确定性目标协同搜索．非结构化环境中搜索对象和搜索主体的变化，要求任务分工动态进行．故在基于响应阈值分工模型基础上，提出一种带闭环调节的动态分工方法．根据机器人或者通过检测目标信号、或者通过邻域通信获得目标认知的特点，将元任务分类并构造个性化任务集，基于概率原则评估针对目标激励的响应并自主选取意向目标．具有共同意向的机器人自组织地缔结子群联盟．然后用平均距离法度量子群联盟内的机器人资源配置水平，作为负反馈引入任务分工模型，调节机器人在不同子群联盟间动态迁移．仿真结果表明，使用本文方法对群机器人动态分工后进行协同搜索，搜索效率较现有方法有显著提高．

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关键词 ：群机器人学, 多目标, 任务分工, 迁移, 负反馈

Abstract：

To search for multiple targets in parallel and simultaneously with swarm robots, those robots should be divided into some sub-swarm-coalitions by job allocation, so that each sub-swarm can cooperatively work focusing on its desired target. Due to varying of search subjects and search objects in unstructured environment, dynamic task allocation is required. Thus a dynamic allocation method with closed-loop adjusting based on the existing response threshold allocation model is proposed. First, a personalized task set is constructed including some meta tasks for each robot, according to cognition receiving by means of either detected signals (I-type target, direct) or informed information of target through neighborhood communications (Ⅱ-type target, indirect). Then responses to target stimuli are evaluated based on probability principle, and a desired target is selected independently from task set. Those robots having the same desired target confederate independently with each other to work cooperatively. Then, the level of robot resource configuration is measured with an average distance approach and the result is linked into allocation model as a negative feedback. This mechanism can makes dynamic migration occurred among different sub-swarm-coalitions on demand. Results from simulations reveal that overall search efficiency is improved by using our proposed method, comparing with the existing methods.

Key words： swarm robotics multiple targets task allocation migration negative feedback

收稿日期: 2013-05-07 录用日期: 2013-10-24

ZTFLH:

TP242.6

基金资助:

国家自然科学基金资助项目（61165016）；山西省自然科学基金资助项目（2013011019-4）；山西高校科技研究开发项目（20111017）；太原科技大学博士科研启动基金资助项目（20112008）；太原科技大学同洲电子科技创新基金资助项目（TZ201304）

通讯作者: 薛颂东 E-mail: xuesongdong@163.com

作者简介: 张云正（1986- ），男，硕士生．研究领域：群机器人协调控制．
薛颂东（1968- ），男，博士，副教授．研究领域：群机器人协调控制．
曾建潮（1963- ），男，博士，教授．研究领域：复杂系统建模与仿真，智能计算．

链接本文:

https://robot.sia.cn/CN/10.3724/SP.J.1218.2014.00057 或 https://robot.sia.cn/CN/Y2014/V36/I1/57

[1] 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.

[2] Barca J, Sekercioglu Y. Swarm robotics reviewed[J]. Robotica, 2013, 31(3): 345-359.

[3] Pugh J, Martinoli A. Inspiring and modeling multi-robot search with particle swarm optimization[C]//Proceedings of IEEE Swarm Intelligence Symposium. Piscataway, USA: IEEE, 2007: 1-5.

[4] Doctor S, Venayagamoorthy G K, Gudise V G. Optimal PSO for collective robotic search applications[C]//Proceedings of Congress on Evolutionary Computation, vol.2. Piscataway, USA: IEEE, 2004.

[5] Jatmiko W, Sekiyama K, Fukuda T. A PSO-based mobile robot for odor source localization in dynamic advection-diffusion with obstacles environment: Theory, simulation and measurement[J]. IEEE Computational Intelligence Magazine, 2007, 2(2): 37-51.

[6] Marjovi A, Marques L. Multi-robot olfactory search in structured environments[J]. Robotics and Autonomous Systems, 2011, 59(11): 867-881.

[7] 刘宗春, 田彦涛, 李成凤.动态阻尼环境下多领导者群体机器人系统协同跟踪控制[J].机器人, 2011, 33(4):385-393. Liu Z C, Tian Y T, Li C F. Coadaptive following control of swarm robot system with multiple leaders in dynamic damping environment[J]. Robot, 2011, 33(4): 385-393.

[8] Meng Y, Gan J. Self-adaptive distributed multi-task allocation in a multi-robot system[C]//Conference of IEEE World Congress on Computational Intelligence. Piscataway, USA: IEEE, 2008: 398-404.

[9] 姜健, 臧希喆, 闫继宏, 等.基于一种蚁群算法的多机器人动态感知任务分配[J].机器人, 2008, 30(3):254-258, 263. Jiang J, Zang X Z, Yan J H, et al. Multi-robot dynamically perceived task allocation based on an ant colony algorithm[J]. Robot, 2008, 30(3): 254-258, 263.

[10] Liu L, Shell D. Large-scale multi-robot task allocation via dynamic partitioning and distribution[J]. Autonomous Robots, 2012, 33(3): 291-307.

[11] 柳林, 季秀才, 郑志强.基于市场法及能力分类的多机器人任务分配方法[J].机器人, 2006, 28(3):337-343. Liu L, Ji X C, Zheng Z Q. Multi-robot task allocation based on market and capability classification[J]. Robot, 2006, 28(3): 337-343.

[12] Dahl T　S, Mataric M, Sukhatme G　S. Multi-robot task allocation through vacancy chain scheduling[J]. Robotics and Autonomous Systems, 2009, 57(6): 674-687.

[13] Zhang D, Xie G, Yu J, et al. Adaptive task assignment for multiple mobile robots via swarm intelligence approach[J]. Robotics and Autonomous Systems, 2007, 55(7): 572-588.

[14] Konur S, Dixon C, Fisher M. Analysing robot swarm behaviour via probabilistic model checking[J]. Robotics and Autonomous Systems, 2012, 60(2): 199-213.

[15] Derr K, Manic M. Multi-robot, multi-target particle swarm optimization search in noisy wireless environments[C]//Proceed-ings of IEEE Conference on Human System Interactions. Piscataway, USA: IEEE, 2009: 81-86.

[16] 张国有, 曾建潮.基于黄蜂群算法的群机器人全区域覆盖算法[J].模式识别与人工智能, 2011, 24(3):431-437. Zhang G Y, Zeng J C. Area coverage algorithm in swarm robotics based on wasp swarm algorithm[J]. Pattern Recognition and Artificial Intelligence, 2011, 24(3): 431-437.

[17] 张嵛, 刘淑华.多机器人任务分配的研究与进展[J].智能系统学报, 2008, 3(2): 115-120. Zhang Y, Liu S H. Survey of multi-robot task allocation[J]. CAAI Transactions on Intelligent Systems, 2008, 3(2): 115-120.

[18] 肖潇, 方勇纯, 贺锋, 等.未知环境下移动机器人自主搜索技术研究[J].机器人, 2007, 29(3):224-229. Xiao X, Fang Y C, He F. Autonomous search technology for mobile robots under unknown environments[J]. Robot, 2007, 29(3): 224-229.

[19] 徐志丹, 莫宏伟.生物地理信息优化算法中迁移算子的改进[J].模式识别与人工智能, 2012, 25(3):544-549. Xu Z D, Mo H W. Improvement for migration operator in biogeography-based optimization algorithm[J]. Pattern Recognition and Artificial Intelligence, 2012, 25(3): 544-549.

[20] Xue S, Zhang J, Zeng J. Parallel asynchronous control strategy for target search with swarm robots[J]. International Journal of Bio-Inspired Computation, 2009, 1(3): 151-163.