The swarm robots should be divided into some sub-swarms through task allocation when searching for multiple targets so as that each sub-swarm can work together on a desired target. For this end, a strategy of control is proposed which apply to cooperation relationship and competition relationship between some sub-swarms. To coordinate cooperatively, a spokesperson for each sub-swarm is required to elect in a self-organization fashion. On behalf of its sub-swarm, it communicate with spokespersons of other sub-swarms, exchanging the respective local best information with these spokespersons each other, guiding its sub-swarm to search a target. For coordinating competitively, a mechanism of contract is proposed. These advantage sub-swarms contract with targets. On the contrast, those disadvantage sub-swarms abandon searching these targets. In a word, we design a control algorithm for the two kinds of coordination at coarse granularity level. Simulation results show that, coordinating cooperatively expand robots' sense range. Coordinating competitively resolve space conflicts. Coordinating which occur between sub-swarms at coarse granularity level and in sub-swarm frame at fine granularity level, promote search efficiency to more high obviously than the existed methods.
 原魁,李园,房立新.多移动机器人系统研究发展近况[J].自动化学报,2007,33(8):785-794. Yuan K, Li Y, Fang L X. Multiple mobile robot systems: A survey of recent work[J]. Acta Automatica Sinica, 2007, 33(8): 785-794. Konur S, Dixon C, Fisher M. Analysing robot swarm behaviour via probabilistic model checking[J]. Robotics and Autonomous Systems, 2012, 60(2): 199-213.  Pugh J, Martinoli A. Inspiring and modeling multi-robot search with particle swarm optimization[C]//IEEE Swarm Intelligence Symposium. Piscataway, USA: IEEE, 2007: 332-339. Tang Q R, Eberhard P. A PSO-based algorithm designed for a swarm of mobile robots[J]. Structural and Multidisciplinary Optimization, 2011, 44(4): 483-498.  Hereford J M, Siebold M A. Bio-inspired search strategies for robot swarms[M]//Swarm Robotics from Biology to Robotics. Vienna, Austria: I-Tech, 2010. Xue S D, Zhang J H, Zeng J C. Parallel asynchronous control strategy for target search with swarm robots[J]. International Journal of Bio-Inspired Computation, 2009, 1(3): 151-163.  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. Zhang Y Z, Xue S D, Zeng J C. Dynamic task allocation with closed-loop adjusting in swarm robotic search for multiple targets[ J]. Robot, 2014, 36(1): 57-68. Tan Y, Zheng Z Y. Research advance in swarm robotics[J]. Defence Technology, 2013, 9(1): 18-39.  Derr K, Manic M. Multi-robot, multi-target particle swarm optimization search in noisy wireless environments[C]//2nd Conference on Human System Interactions. Piscataway, USA: IEEE, 2009: 81-86. Couceiro M S, Rocha R P, Ferreira N M F. A novel multi-robot exploration approach based on particle swarm optimization algorithms[ C]//IEEE International Symposium on Safety, Security, and Rescue Robotics. Piscataway, USA: IEEE, 2011: 327- 332. Zheng Z Y, Tan Y. Group explosion strategy for searching multiple targets using swarm robotic[C]//IEEE Congress on Evolutionary Computation. Piscataway, USA: IEEE, 2013: 821-828. Marjovi A, Marques L. Multi-robot olfactory search in structured environments[J]. Robotics and Autonomous Systems, 2011, 59(11): 867-881. 翟中和,王喜忠,丁明孝.细胞生物学[M].北京:高等 教育出版社,2007:469-479. Zhai Z H, Wang X Z, Ding M X. Cell biology[M]. Beijing: Higher Education Press, 2007: 469-479. 张云正.面向多目标搜索的群机器人协同控制研究[D].太原:太原科技大学,2014. Zhang Y Z. Swarm robots search for multiple targets in cooperation control[D]. Taiyuan: Taiyuan University of Science and Technology, 2014. 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. 薛颂东.面向目标搜索的群机器人协调控制及其仿真研究[D].兰州:兰州理工大学,2009. Xue S D. Swarm robotic search for target: Cooperative control techniques, strategies, and simulations[D]. Lanzhou: Lanzhou University of Technology, 2009.