基于修正蚁群算法的多机器人气味源定位策略研究

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骆德汉, 邹宇华, 庄家俊. 基于修正蚁群算法的多机器人气味源定位策略研究[J]. 机器人, 2008, 30(6): 536-541.. LUO De-han, ZOU Yu-hua, ZHUANG Jia-jun. Multi-Robot Odor Source Localization Strategy Based on a Modified Ant Colony Algorithm. ROBOT, 2008, 30(6): 536-541..

摘要为了使多机器人系统能够模仿蚁群寻找食物源的行为方式来搜索室内环境中存在的气味源，通过对蚁群算法的修正，形成一种新的多机器人协作策略．修正的蚁群算法包括局部遍历搜索、全局随机/概率搜索和信息素更新三个阶段．为了实现多个气味源的定位，在迭代搜索中加入了气味源确认机制．仿真结果表明，局部遍历搜索能够保证机器人逐步靠近气味源，而在全局搜索中设置气味浓度检测阈值可以避免机器人“群聚”现象的形成．最后验证了从不同入口点分散进入搜索区域时，机器人对多个气味源的搜索定位效果．

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	骆德汉
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关键词 ：蚁群算法, 多机器人协作, 气味源定位

Abstract：To enable robots to search for the indoor odor sources by imitating the foraging behavior of ant colony,a multi- robot cooperation strategy is proposed based on a modified ant colony algorithm(ACA).The modified ACA includes three stages,which are local traversal search,global random/probability search and pheromone update.A verification procedure is introduced into the iteration process to localize multiple odor sources.Simulation results show that the local traversal search can enable the robots to move towards the odor source gradually,and to set an odor concentration threshold(OCT)in the global search stage can prevent the robots from cluttering together.Finally,the multiple odor source localization performance of the robots which start at different entrances is validated.

Key words： ant colony algorithm multi-robot cooperation odor source localization

收稿日期: 2008-04-23

TP24

基金资助:广东省自然科学基金资助项目（05001836）

作者简介: 骆德汉（1958- ），男，博士，教授．研究领域：机器嗅觉，机器味觉，仿生信号与信息处理．
邹宇华（1984- ），男，硕士生．研究领域：机器嗅觉，智能传感信号处理．

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https://robot.sia.cn/CN/ 或 https://robot.sia.cn/CN/Y2008/V30/I6/536

[1] Vickers N J.Mechanisms of animal navigation in odor plumes[J].The Biological Bulletin,2000,198(2):203～212.
[2] Grasso F W,Consi T R,Mountain D C,et al.Biomimetic robot lobster performs chemo-orientation in turbulence using a pair of spatially separated sensors:Progress and challenges[J].Robotics and Autonomous Systems,2000,30(1):115～131.
[3] Russell R A,Thiel D,Deveza R,et al.A robotic system to locate hazardous chemical leaks[A].Proceedings of the IEEE International Conference on Robotics and Automation[C].Piscataway,NJ,USA:IEEE,1995.556～561.
[4] Ishida H,Nakamoto T,Moriizumi T.Remote sensing of gas/odor source location and concentration distribution using mobile system[J].Sensors and Actuators,B:Chemical,1998,B49(1-2):52～57.
[5] Farah A M,Duckett T.Reactive localization of an odour source by a learning mobile robot[DB/OL] ,ftp://aass.oru.se/pub/tdt/mohamod_duckett.ps.gz,2002/2008.
[6] Farrell J A,Mudis J,Long X Z,et al.Filament-based atmospheric dispersion model to achieve short time-scale structure of odor plumes[J].Enviroumental Fluid Mechanics,2002,2(1-2):143～169.
[7] Hayes A T.Self Organized Robotic System Design and Autonomous Odor Localization[D].California,USA:California Institute of Technology,2002.
[8] Russell R A.Ant trails-An example for robots to follow?[A].Proceedings of the IEEE International Conference on Robotics and Automation[C].Piscataway,N J,USA:IEEE,1999.2698～2703.
[9] 李俊彩,孟庆浩,梁琼.基于进化梯度搜索的机器人主动嗅觉仿真研究[J].机器人,2007,29(3):234～238.Li Jun-cai,Meng Qing-hao,Liang Qiong.Simulation study on robot active olfaetion based on evolutionary gradient search[J].Robot,2007,29(3):234～238.
[10] 段海滨.蚁群算法原理及其应用[M].北京:科学出版社,2005.Duan Hai-bin.Ant Colony Algorithm:Theory and Applications[M].Beijing:Science Press,2005.
[11] Dorigo M,Maniezzo V,Colomi A.Ant system:Optimization by acolony of coormating agents[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B,1996,26(1):29～41.
[12] 金飞虎,洪炳熔,高庆吉.基于蚁群算法的自由飞行空间机器人路径规划[J].机器人,2002,24(6):526～529.Jin Fei-hu,Hong Bing-rong,Gao Qing-ji.Path planning for free-flying space robot ruing ant algorithm[J].Robot,2002.24(6):526～529.
[13] Maniezzo V,Colomi A.The ant system applied to the quadratic assignment problem[J].IEEE Transactions on Knowledge and Data Engineering,1999,11(5):769～778.
[14] 王常青,操云甫,戴国忠.用双向收敛蚁群算法解作业车间调度问题[J].计算机集成制造系统,2004,10(7):820～824.Wang Chang-qing,Cao Yun-fu,Dai Guo-zhong.Bi-directional convergence ACO for job-shop scheduling[J].Computer Integrated Manufacturing Systems,2004,10(7):820～824.
[15] 高坚.基于自适应蚁群算法的多受限网络QoS路由优化[J].计算机工程,2003,29(19):40～41,67.Gao Jian.Optimization of multiple constrains QoS routing based on an adaptive antcolony system algorithm[J].Computer Engineering,2003,29(19):40～41,67.
[16] 陈卓,孟庆春,魏振钢.基于群体智能理论的聚类模型及优化算法[J].计算机工程,2005,31(4):34～36.Chen Zhuo,Meng Qing-chun,Wei Zlaeng-gang.A clustering model and optimized algorithm based on swarm intelligent theory[J].Computer Enginecring,2005,31(4):34～36.
[17] Matlaur M,Karale S B,Priye S,et al.Ant colony approach to continuous function optimization[J].Industrial and Engineering Chemistry Research,2000,39(10] :3814～3822.
[18] 丁滢颖,何衍,蒋静坪.基于蚁群算法的多机器人协作策略[J].机器人,2005,25(5):414～418.Ding Ying-ying.He Yan,Jiang Jing-ping.Multi-robot cooperation raethod based on the ant algorithm[J].Robot,2005,25(5):414～418.
[19] Meng Q H,Li J C,Li F,et al.Mobile robots odor localization with an improved ant colony algorithm[A].Proceedings of the IEEE International Conference on Robotics and Biomimetics[C].Piseataway,NJ.USA:IEEE,2006.959～964.