机器人室内未知环境探测规划研究
|
摘要
探测规划是移动机器人自主构建未知环境地图领域的核心问题之一,对提高机器人的自主性、确保未知环境地图构建的高效性、鲁棒性和精确性具有重要的理论研究意义和应用价值。
地图构建中探测规划的目的是实时生成机器人的运动控制,使机器人在较短的时间内感知范围覆盖尽可能大的区域,如何根据不完整的地图信息进行在线实时地规划,确保整体路径的最优性和探测的完全性,是该问题面临的挑战。本文主要针对地图构建探测规划中的单步探测规划、多步探测规划和多机器协作探测规划问题进行了研究,完成了以下工作:
①设计开发了基于Microsoft Visual C++和Open Dynamics Engine(ODE)的机器人仿真系统。系统中模拟室内静态和动态环境的障碍物,模拟机器人的激光测距仪和里程计传感器数据,并对机器人和障碍物的运动进行动力学仿真,为移动机器人地图构建、探测规划、定位导航、多机器人协作等问题的研究提供了一个功能丰富,具有良好实时性和交互能力的实验平台。
②针对未知环境信息的模糊性和不确定性,提出了基于模糊综合评价决策的单步探测规划方法。该方法将栅格图中的前沿点按距离和可行性进行分类,选取优先级较高的类别中的点作为候选点,根据候选点的距离、信息增益和可定位性进行模糊综合评价,以较小的计算代价决定机器人下一步的探测位姿,从而完成对未知区域的探测,构建出准确度高的环境栅格图和特征线段图。该方法减少了待评估点的数量,评估时采用模拟人类思维的模糊评价规则,避免了评估因子的不准确性对评价结果造成较大影响,提高了探测效率。
③利用环境地图构建中栅格图的概率信息,提出基于分布估计算法的路径规划和多步探测规划方法。该方法结合以上概率信息生成机器人的初始轨迹并更新轨迹群分布的概率模型,通过多个可行解的并行迭代,快速获得包含多个观测位姿的评价较优的可行路径,减少了规划次数。
④在力学原理的基础上,结合地图构建中计算获得的地图信息和机器人位姿信息,将虚拟力场法改进用于多机器人未知环境探测规划。利用栅格地图中前沿点对机器人产生的吸引力和障碍物对机器人产生的排斥力,综合与其他机器人之间的间距产生的影响,确定机器人的探测方向和速度。提出基于障碍物形状与虚拟力的方向的判据判断机器人是否陷入局部最小,并通过生成虚拟障碍物与虚拟目标,帮助机器人摆脱局部极小,避免振荡。多机器人之间采用分布式控制结构,通过控制机器人的间距,避免机器人的碰撞和过多的重复探测。实验证明此系统具有较强鲁棒性和较高协作效率。
⑤使用装备了激光测距仪与光电编码器的自制全方向移动机器人SR-M002在室内结构化环境中进行了自主探测规划的实验,验证了上述基于模糊评价的探测规划方法与基于分布估计的多步探测规划方法的有效性。
Exploration planning is one of the most important issues for automatic mapping in unknown environment of mobile robot.It has significant theoretical and practical influence on autonomy,effectiveness,robustness and accuracy of mapping.
The main target of exploration planning is to generate real-time motion control for robots that enlarges the perceptive range of the robot to cover largest area in a shortest time.Because the map information is incompleted,it is a big challenge to find an optimal and complete path in real time.Single-step planning,multi-steps planning and cooperative exploration of multi-robots are studied in this dissertation. The main work is as follows:
①A robot simulation system based on Microsoft Visual C++ and Open Dynamics Engine(ODE) is designed to construct the virtual indoor static and dynamic obstacles,provide the virtual measurement of laser range finder and odometer,and simulate the kinetics motion of robots and obstacles.Meanwhile,the system could become a real-time platform for the researches on mapping, exploration planning,localization,navigation and multi-robots cooperation.
②A fuzzy evaluation based exploration planning method is presented to deal with the fuzziness and uncertainty of unknown environmental information.Frontier points are classified by their discriminative distance and feasibility.With classification results,the candidate points with higher priority are evaluated according to their distance,information gain,and localizability.Thus,the next observation pose or a series of next poses could be determined with low computational costs.Finally,the exploration of unknown areas could be finished and both the grid map and feature map are constructed accurately.This method reduces the quantity of candidate points,while fuzzy evaluation which imitates human thinking could avoid the effect caused by inaccurate factors.The exploration efficiency is improved.
③Multi-steps exploration method using estimation of distribution algorithm is proposed to make use of probabilistic information of grids map.After initializing of robot's tracks from probabilistic information and updating the probabilistic model of their distribution,the optimal path could be efficiently acquired by multiple parallel iterations.The frequency of planning is reduced.
④An exploration strategy for mutlti-robots is also established in this dissertation with an improved virtual force method.The speed and exploring direction could be decided by combining the virtual attracting force generated by frontier points,repulsive force generated by obstacles,and the influence caused by distance between the robots.Considering the problem of local minima resulting from the virtual force method,,virtual obstacles and virtual targets are set to help robots to escape such situations and reduce oscillation.Due to the distance control,in this distributed control system,multiple robots could cooperate with each other with few repeating exploration area and collision in movement.Experimental results demonstrate the capability of this approach to escape local minima and improve the efficiency of exploring.
⑤Experiments of the proposed exploration algorithms are also conducted on a real robot,the SR-M002 robot.SR-M002 is an omnidirectional mobile robot made by our laboratory.It equips 4 photoelectric encoders and a laser ranger finder.The experiments are completed in the indoor unknown environments.With the results, the effectiveness of above fuzzy evaluation based exploration planning and multi-steps exploration method with estimation of distribution algorithm are verified.
地图构建中探测规划的目的是实时生成机器人的运动控制,使机器人在较短的时间内感知范围覆盖尽可能大的区域,如何根据不完整的地图信息进行在线实时地规划,确保整体路径的最优性和探测的完全性,是该问题面临的挑战。本文主要针对地图构建探测规划中的单步探测规划、多步探测规划和多机器协作探测规划问题进行了研究,完成了以下工作:
①设计开发了基于Microsoft Visual C++和Open Dynamics Engine(ODE)的机器人仿真系统。系统中模拟室内静态和动态环境的障碍物,模拟机器人的激光测距仪和里程计传感器数据,并对机器人和障碍物的运动进行动力学仿真,为移动机器人地图构建、探测规划、定位导航、多机器人协作等问题的研究提供了一个功能丰富,具有良好实时性和交互能力的实验平台。
②针对未知环境信息的模糊性和不确定性,提出了基于模糊综合评价决策的单步探测规划方法。该方法将栅格图中的前沿点按距离和可行性进行分类,选取优先级较高的类别中的点作为候选点,根据候选点的距离、信息增益和可定位性进行模糊综合评价,以较小的计算代价决定机器人下一步的探测位姿,从而完成对未知区域的探测,构建出准确度高的环境栅格图和特征线段图。该方法减少了待评估点的数量,评估时采用模拟人类思维的模糊评价规则,避免了评估因子的不准确性对评价结果造成较大影响,提高了探测效率。
③利用环境地图构建中栅格图的概率信息,提出基于分布估计算法的路径规划和多步探测规划方法。该方法结合以上概率信息生成机器人的初始轨迹并更新轨迹群分布的概率模型,通过多个可行解的并行迭代,快速获得包含多个观测位姿的评价较优的可行路径,减少了规划次数。
④在力学原理的基础上,结合地图构建中计算获得的地图信息和机器人位姿信息,将虚拟力场法改进用于多机器人未知环境探测规划。利用栅格地图中前沿点对机器人产生的吸引力和障碍物对机器人产生的排斥力,综合与其他机器人之间的间距产生的影响,确定机器人的探测方向和速度。提出基于障碍物形状与虚拟力的方向的判据判断机器人是否陷入局部最小,并通过生成虚拟障碍物与虚拟目标,帮助机器人摆脱局部极小,避免振荡。多机器人之间采用分布式控制结构,通过控制机器人的间距,避免机器人的碰撞和过多的重复探测。实验证明此系统具有较强鲁棒性和较高协作效率。
⑤使用装备了激光测距仪与光电编码器的自制全方向移动机器人SR-M002在室内结构化环境中进行了自主探测规划的实验,验证了上述基于模糊评价的探测规划方法与基于分布估计的多步探测规划方法的有效性。
Exploration planning is one of the most important issues for automatic mapping in unknown environment of mobile robot.It has significant theoretical and practical influence on autonomy,effectiveness,robustness and accuracy of mapping.
The main target of exploration planning is to generate real-time motion control for robots that enlarges the perceptive range of the robot to cover largest area in a shortest time.Because the map information is incompleted,it is a big challenge to find an optimal and complete path in real time.Single-step planning,multi-steps planning and cooperative exploration of multi-robots are studied in this dissertation. The main work is as follows:
①A robot simulation system based on Microsoft Visual C++ and Open Dynamics Engine(ODE) is designed to construct the virtual indoor static and dynamic obstacles,provide the virtual measurement of laser range finder and odometer,and simulate the kinetics motion of robots and obstacles.Meanwhile,the system could become a real-time platform for the researches on mapping, exploration planning,localization,navigation and multi-robots cooperation.
②A fuzzy evaluation based exploration planning method is presented to deal with the fuzziness and uncertainty of unknown environmental information.Frontier points are classified by their discriminative distance and feasibility.With classification results,the candidate points with higher priority are evaluated according to their distance,information gain,and localizability.Thus,the next observation pose or a series of next poses could be determined with low computational costs.Finally,the exploration of unknown areas could be finished and both the grid map and feature map are constructed accurately.This method reduces the quantity of candidate points,while fuzzy evaluation which imitates human thinking could avoid the effect caused by inaccurate factors.The exploration efficiency is improved.
③Multi-steps exploration method using estimation of distribution algorithm is proposed to make use of probabilistic information of grids map.After initializing of robot's tracks from probabilistic information and updating the probabilistic model of their distribution,the optimal path could be efficiently acquired by multiple parallel iterations.The frequency of planning is reduced.
④An exploration strategy for mutlti-robots is also established in this dissertation with an improved virtual force method.The speed and exploring direction could be decided by combining the virtual attracting force generated by frontier points,repulsive force generated by obstacles,and the influence caused by distance between the robots.Considering the problem of local minima resulting from the virtual force method,,virtual obstacles and virtual targets are set to help robots to escape such situations and reduce oscillation.Due to the distance control,in this distributed control system,multiple robots could cooperate with each other with few repeating exploration area and collision in movement.Experimental results demonstrate the capability of this approach to escape local minima and improve the efficiency of exploring.
⑤Experiments of the proposed exploration algorithms are also conducted on a real robot,the SR-M002 robot.SR-M002 is an omnidirectional mobile robot made by our laboratory.It equips 4 photoelectric encoders and a laser ranger finder.The experiments are completed in the indoor unknown environments.With the results, the effectiveness of above fuzzy evaluation based exploration planning and multi-steps exploration method with estimation of distribution algorithm are verified.
引文
[1]THRUN S..Robotic mapping:A survey.In Lakemeyer G.and Nebel B.,editors,Exploring Artificial Intelligence in the New Millenium[M].San Francisco:Morgan Kaufmann,2002.
[2]陈卫东,张飞.移动机器人的同步自定位与地图创建研究进展[J].控制理论与应用,2005,22(3):455-460.
[3]ISLER V,KANNAN S,DANIILIDIS K.Local Exploration:Online Algorithms and a Probabilistic Framework[A].Proceedings of the 2003 IEEE International Conference on Robotics and Automation[C].USA:IEEE,2003.1913-1920
[4]蔡自兴,崔益安.多机器人覆盖技术研究进展[J].控制与决策,2008,23(5):481-486.
[5]CHOSET H..Coverage for robotics:a survey of recent results[J].Annals of Mathematics and Artificial Intelligence,2001,31:113-126.
[6]邱雪娜,刘士荣,宋加涛等.不确定动态环境下移动机器人的完全遍历路径规划[J].机器人.2006,28(6):586-592.
[7]郝宗波,洪炳镕.多简单机器人协作覆盖规划研究[J].机器人,2007,29(1):18-22.
[8]GABRIELY Y,RIMON E.Spanning-tree based coverage of continuous areas[J].Annals of Mathematics and Artificial Intelligence,2001,31(1-4),77-98.
[9]OHYA A.,NAGASHIMA Y.,YUTA S.Exploring unknown environment and map construction using ultrasonicsensing of normal direction of walls[A]..Proceedings of the IEEE International Conference on Robotics and Automation[C].IEEE,San Diego,USA:1994.485-492.
[10]YAMAUCHI B.,A frontier-based approach for autonomous exploration[A].Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation[C].Monterey,CA:IEEE,1997.146-151.
[11]WULLSCHLEGER F.H.,ARRAS K.O.,AND VESTLI S.J.A flexible exploration framework for map building[A].Proceedings of the Third European Workshop on Advanced Mobile Robots[C].Zurich,Switzerland:1999.49-56.
[12]MAKARENKO A.A.,WILLIAMS S.B.,BOURGAULT F,and DURRANT-WHYTE H.F.An experiment in integrated exploration[A].Proceedings of IEEE/RSJ Int.Conf.on Intelligent Robots and Systems[C].Lausanne,Switzerland:IEEE,2002.534-539
[13]周光明,贾梦雷,陈宗海.移动机器人未知环境自主探测的一种高效算法[J].上海交通大学学报,2005,39(6):936-940
[14]TOVAR B.,MURRIETA-CID R.,ESTEVES C..Robot motion planning for map building[A].Proceedings of the IEEE/RSJ Int.Conference on Intelligent Robots and Systems[C].Lausanne,Switzerland:IEEE,2002:673-680.
[15]GONZALEZ-BANOS H.,EFRAT A.,LATOMBE J.C.,MAO E.,and MURALI T.M.Planning robot motion strategies for efficient model construction[A].In Hollerbach J.and Koditschek D.,editors,The Ninth Int.Symp.on Robotics Research[C],Salt Lake City,UT:Springer-Verlag,1999.345-352.
[16]GONZALEZ-BANOS H.H.and LATOMBE J.C.Navigation strategies for exploring indoor environments[J].International Journal of Robotics Research,2002,21(10):829-848.
[17]张恒,樊晓平.移动机器人同步定位与地图构建过程中的轨迹规划研究[J].机器人.2006.28(3):285-290.
[18]FREDA L.,ORIOLO G.,VENDITTELLI M.Probabilistic strategies for sensor-based exploration[A].WAC International Symposium on Robotics and Applications[C],Spain:2004.63-68.
[19]FREDA L.,ORIOLO G.Frontier-based probabilistic strategies for sensor-based exploration[A].Proceedings of the 2005 IEEE Int.Conf.on Robotics and Automation[C]Barcelona,Spain:2005.2892-3898.
[20]MACKAY D.J.C.Bayesian methods for adaptive models[D].Pasade ha:California Institute of Technology,1991.
[21]SIM R.,ROY N.Global A-Optimal Robot Exploration in SLAM[A].Proceedings of the IEEE International Conference on Robotics and Automation[C],Piscataway,USA:IEEE,2005.http://www.cs.ubc.ca/～simra/publications/sim_roy_icra05.pdf.
[22]HUANG Y F,GUPTA K.RRT-SLAM for Motion Planning with Motion and Map Uncertainty for Robot Exploration[A].IEEE/RSJ International Conference on Intelligent Robots and Systems[C].Nice,France:IEEE,2008:1077-1082.
[23]孟偲,王田苗.一种移动机器人全局最优路径规划算法[J].机器人.2008,33(3):217-222.
[24]ISLER V.,KANNAN S.,AND DANIILIDIS K.Local exploration algorithms:Online algorithms and probabilistic framework[A].Proceedings IEEE Int.Conf.on Robotics and Automation[C].Taipei,Taiwan:IEEE,2003:12-17.
[25]STACHNISS C.,GRISETTI G.,and BURGARD W.Information gain-based exploration using rao-blackwellized particle filters[A].Proceedings of Robotics:Science and Systems[C],Cambridge,U.K.2005.hap://www.roboticsproceedings.org/rss01/p09.pdf.
[26]LIU Y,DONG J,SUN F C.An Efficient Navigation Strategy for Mobile Robots with Uncertainty Estimation[A].Proceedings of the 7th World Congress on Intelligent Control and Automation[A].Chongqing,China:IEEE,2008:5174-5179.
[27]DOUCET A.,DE FREITAS J.F.G.,MURPHY V.,and RUSSEL S.Raoblackwellized partcile filtering for dynamic bayesian networks[A].Proceedings of the Conf.on Uncertainty in Artificial Intelligence(UAI)[C],Stanford,CA,USA,2000:176-183.
[28]REKLEITIS,I.,MEGER,D.,DUDEK,G.Simultaneous planning localization,and mapping in a camera sensor network[J].Robotics and Autonomous Systems(RAS) Journal,2005,special issue on Planning and Uncertainty in Robotics:http://cim.mcgill.ca/～dmeger/publications/SNetSPLAM.pdf.
[29]YI X C,ZHANG H,HAI D et al.A particle filter based active loop closing approach to autonomous robot exploration and mapping[A].Proceedings of 2008 IEEE International Conference on Mechatronics and Automation[C].Takamatsu,Japan:IEEE,2008:824-830.
[30]DUDEK,G.,JENKIN,M.,MILIOS,E.WILKES,D..A taxonomy for swarm robots[A],Proceedings of the 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems[C].Yokohama,Japan:IEEE,1993,1,441-447.
[31]FARINELLI,A.,IOCCHI,L.,NARDI,D..Multirobot systems:a classification focused on coordination[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B:Cybernetics.2004,34(5):2015-2028.
[32]谭民,王硕,曹志强.多机器人系统[M].北京:清华大学出版社,2005.
[33]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):786-794.
[34]STENTZ A.and BRUMITT B L..Dynamic Mission Planning for Multiple Mobile Robots[A],Proceedings of the 1996 IEEE International Conference on Robotics &Automation[C].Minneapolis,MN,USA:IEEE,1996,No.3,2396-2401
[35]SINGH S.and THAYER S.Immunology Directed Methods for Distributed Robotics:A Novel,Immunity-Based Architecture for Robust Control & Coordination[A].Proceedings of SPIE:Mobile Robots ⅩⅥ,vol.4573[C].IEEE,2001.http://www.ri.cmu.edu/pubs/pub_3845.html
[36]SIMMONS R.,APFELBAUM D.,BURGARD W.,FOX D.,THRUN S.,YOUNES H.. Coordination for multi-robot exploration and mapping[A].Proceedings of the National Conference on Artificial Intelligence[A].Austin,TX:AAAI,2000.http://citeseer.ist.psu.edu/simmons00coordination.html.
[37]姜健,赵杰,臧希喆.基于并列选择遗传算法的多机器人协作探测[J].计算机工程与设计,2008,29(5):1218-1221.
[38]LATIMER IV D.,SRINIVASA S.,LEE-SHUE V.Towards Sensor Based Coverage with Robot Teams[A].Proceedings of the 2002 IEEE International Conference on Robotics &Automation[C].Washinton,DC:IEEE,2002.961-967.
[39]WAGNER,M.LINDENBAUM,A.BRUCKSTEIN.Distributed covering by ant-robots using evaporating traces[J].IEEE Transactions on Robotics and Automation,1999,15(5):918-933.
[40]THAYER S.M.,SINGH S.P.N.Development of an immunology-based multi-robot coordination algorithm for exploration and mapping domains[A].Proceedings of the 2002IEEE International Conference on Intelligent Robots and Systems[C].EPFL,Switzerland:IEEE,2002,2735-2739.
[41]YAMAUCHI.B.Frontier-based exploration using multiple robots[A].In Second International Conference on Autonomous Agents[C].Minneapolis,MN:1998.47-53.
[42]NAVARRO-SERMENT L.E.,PAREDIS C.J.J.,KHOSLA P.K.,A Beacon System for the Localization of Distributed Robotic Teams[A].Proceedings of the International Conference on Field and Service Robotics[J],Pittsburgh,PA:IEEE,1999,http://www.ri.cmu.edu/pubs/pub_3122.html
[43]REKLEITIS,I.,DUDEK,G.MILIOS,E.Experiments in Free-Space Triangulation Using Cooperative Localization[A].Proceedings of the 2003 IEEE International Conference on Intelligent Robots and Systems[C].Las Vegas:IEEE,2003.1777-1782.
[44]REKLEITIS,I.M.;DUDEK,G.;MILIOS,E.E..Multi-robot collaboration for robust exploration.[A].Proceedings of the 2000 IEEE International Conference on Robotics &Automation[C].San Francisco:IEEE,2000.3164-3169.
[45]ZLOT R.,STENTZ A.R.,DIAS M.B.,THAYER S.Multi-Robot Exploration Controlled by a Market Economy[A].Proceedings of the 2002 IEEE International Conference on Robotics & Automation[C].Washington,DC:IEEE,2002:3016-3023
[46]KO J.,STEWART B.,FOX D.,KONOL1GE K.,LIMKETKAI B.A practical,decision-theoretic approach to multi-robot mapping and exploration[A].Proceedings of the 2003 IEEE International Conference on Intelligent Robots.and Systems[C].Las Vegas:IEEE,2003:3232-3238.
[47]Robotics Developer Studio.http://msdn.microsoft.com/zh-cn/library/bb483024(en-us).aspx [2009-6-9].
[48]MobileSim.http://robots.mobilerobots.com/wiki/MobileSim.[2009-6-9].
[49]MobotSim.http://www.mobotsoft.com/[2009-6-9].
[50]OpenGL http://www.opengl.org/documentation/[2009-7-1].
[51]张翮.全方位移动机器人的运动建模与控制[D].杭州:浙江大学,2005.
[52]庄严,王伟,王珂,徐晓东.移动机器人基于激光测距和单目视觉的室内同时定位和地图构建[J].自动化学报,2005,31(6):925-933.
[53]ZADEH L.A.Fuzzy sets[J].Information and Control.1965:8(3):338-353.
[54]吴晓莉,林哲辉.MATLAB辅助模糊系统设计[M].西安:西安电子科技大学出版社,2002.
[55]王士同.模糊系统、模糊神经网络及应用程序设计[M].上海:上海科学技术文献出版社,1997.
[56]熊和金,陈德军.智能信息处理[M].北京:国防工业出版社,2006.
[57]熊蓉,褚健,吴俊.基于点线相合的机器人增量式地图构建[J]控制理论与应用,2007,24(2):170-176.
[58]THURN S.,Burgard W.,FOX D.A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots[J].Autonomous Robots,1999,5(3-4):253-271.
[59]HOWARD A.MATARK M.J.SUKHATME G.S.Localization for mobile robot teams using maximum likelihood estimation[A].IEEE International conference on Intelligent Robots and System[C].Lausanne,Swiss,2002:434-439.
[60]王正志,薄涛.进化计算[M].长沙:国防科技大学出版社,2000.
[61]任子武.基于进化计算的前向神经网络学习算法研究[D].哈尔滨:哈尔滨工业大学,2008.
[62]王凌.智能优化算法及其应用[M].北京:清华大学出版社,2001.
[63]LEUNG Y.,GAO Y.XU Z.B..Degree of population diversity-a perspective on prematureconvergence in genetic algorithms and its Markov chain analysis[J].IEEE Transactions on Neural Networks,2002,8(5):1165-1176.
[64]KUREICHICK V.,MELIKHOV A.N.,.MIAGHICK V.V,et al.Some New Features in the Genetic Solution of the Traveling Salesman Problem[A].In lan Parmee and M.J.Denham eds.Adaptive computing in Engineering Design and Control 96(ACEDC'96),2nd International Conference of the Integration of Genetic Algorithms and Neural Network Computing and Related Adaptive Computing.Current Engineering Practice, Plymouth, UK, 1996.
[65] FONLUPT C., PREUX P., ROBILLIARD D.. Preventing Premature Convergence via Cooperating Genetic Algorithms[A]. Proceedings Mandel'97[C]. Morgan Kaufmann,1997.
[66] ROCHA M, NEVES,J..Preventing Premature Convergence to Local Optima in Genetic Algorithms via Random[A]. IEA 99[C],Springer-Verlag Berlin ,1999:127-136.
[67] PELIKAN M, GOLDBERG DE, LOBO FG. A survey of optimization by building and using probabilistic models[J]. Computational Optimization and Applications,2002,21(1):5-20.
[68]周树德,孙增圻．分布估计算法综述[J] 自动化学报,2007,33(2)：113-124．
[69] BALUJA, S. Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning[R].Technical report CMU-CS-96-163, Pittsburgh, PA: Carnegie Mellon University, 1994.
[70] MUHLENBEIN H. The equation for response to selection and its use for prediction[J]. Evolutionary Computation, 1997,5(3):303-346.
[71] HARIK G R, LOBO F G, GOLDBERG D E. The compact genetic algorithm [J]. IEEE Transactions on Evolutionary Computation. 1999,3(4):287-297.
[72] HOFELD M,RUDOLPH G Towards a theory of population-based incremental learning[A]. Proceedings of the 4th IEEE conference on evolutionary computation[C]. Indianapolis:IEEE, 1997:1-5.
[73] DE BONET J S,ISBELL CL,VIOLA J P.MIMIC: Finding optima by estimating probability densities. Advances in Neural Information Processing Systems[M], Cambridge:MIT Press, 1997:424-430
[74] ZHANG Q., MUHLENBEIN H. On the convergence of a class of estimation of distribution algorithms[J]. IEEE Transactions on Evolutionary Computation. 2003,8(2): 127-136.
[75] SUGIHARA K, SMITH J. Genetic Algorithms for Adaptive Motion Planning of an Autonomous Mobile Robot[A]. IEEE International Symposium on Computational Intelligence in Robotics and Automation[C], 1997:138-143.
[76] TU J,YANG S. Genetic algorithm based path planning for a mobile robot[A]. IEEE International Conference on Robotics and Automation[C]. 2003,1:1121-1126.
[77] RYAN C. Reducing Premature Convergence in Evolutionary Algorithms[D]. Dublin:NationaI University of Ireland, 1996.
[78] POSIK P..Preventing Premature Convergence in a Simple EDA Via Global Step Size Setting[A]. Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X[C]. Springer-Verlag,2008: 549 - 558.
[79] YUAN B., GALLAGHER M. On the importance of diversity maintenance in estimation of distribution algorithms[A]. Proceedings of the Genetic and Evolutionary Computation Conference[C]. New York:ACM,2005:719 - 726.
[80] YUAN B., GALLAGHER M.: A mathematical modelling technique for the analysis of the dynamics of a simple continuous EDA[A].IEEE Congress on Evolutionary Computation[C]Vancouver, Canada: IEEE,2006: 1585-1591.
[81] GRAHL J., BOSMAN P.A.N., ROTHLAUF F.: The correlation-triggered adaptive variance scaling IDEA[A]. Proceedings of the 8th annual conference on Genetic and Evolutionary Computation Conference [C]. New York:ACM,2006:397-404.
[82] BOSMAN P.A.N., GRAHL J., ROTHLAUF F.SDR: A better trigger for adaptive variance scaling innormal EDAs[A]. Proceedings of the 9th annual conference on Genetic and Evolutionary Computation[C]. New York:ACM,2007: 492-499.
[83] POSIK, P. Truncation selection and gaussian EDA:Bounds for sustainable progress in high-dimensional spaces[A]. Evo workshops[C]. Heidelberg,Springer,2009:525-534.
[84] BARRAQUAND J, LATOMBE J.C. Robot motion planning: a distributed representation approach[J]. International Journal of Robotics Research, 1991,10(6): 628 - 649
[85] LOZANO-PEREZ T. Spatial Planning: A Configuration Space Approach[J]. IEEE Transactions on Computers, 1983, C-32(2):108-120.
[86] KHATIB, O. Real-time obstacle avoidance for manipulators and mobile robots[J]. International Journal of Robotics Research. 1986,5(1):90-98.
[87] CORMEN T. H, LEISERSON C. E., RIVEST R. L., Stein C.Introduction to Algorithms, Second Edition[M]. MIT Press and McGraw-Hill, 2001. Section 22.3: Depth-first search:540-549.
[88] DIJKSTRA E. W. A note on two problems in connexion with graphs[J]. In Numerische Mathematik, 1959,1:269-271.
[89] DECHTER R. PEARL J. Generalized best-first search strategies and the optimality of A*[J]. Journal of the ACM,1985,32(3): 505 - 536
[90] STENTZ A. Optimal and efficient path planning for partially-known environments[A]. IEEE International Conference on Robotics and Automation[C]. San Diego:IEEE,1994:3310-3317.
[91]STENTZ A.T.The Focussed D~* Algorithm for Real-Time Replanning[A].Proceedings of the International Joint Conference on Artificial Intelligence[C].1995:1652-1659.
[92]AOKI T.MATSUNO M.SUZUKI,T.OKUMA,S.Motion planning for multiple obstacles avoidance of autonomous mobile robot using hierarchical fuzzy rules[A].IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems[C].Las Vegas:IEEE,1994:265-271.
[93]蔡自兴,郑敏捷,邹小兵.基于激光雷达的移动机器人实时避障策略[J].中南大学学报(自然科学版),2006,37(2):324-329.
[94]郑敏捷,蔡自兴,于金霞.一种动态环境下的移动机器人避障策略[J].高技术通讯,2006,16(8):813-819.
[95]黄炳强,曹广益.基于模糊人工势场的移动机器人路径规划[J].上海理工大学学报,2006,28(4):347-350.
[96]张汝波,顾国昌.水下智能机器人模糊局部规划器设计[J].机器人,1996,18(3):158-162.
[97]付宜利,顾晓宇,王树国.基于模糊控制的自主机器人路径规划策略研究[J].机器人,2004,26(6):548-552.
[98]庄晓东,孟庆春.动态环境中基于模糊概念的机器人路径搜索方法[J].机器人,2001,23(5):397-399,458.
[99]陈宗海,陈锋.一种不确定环境下移动机器人的避障规划算法[J].机器人,2002,24(2):358-361.
[100]陈华志,谢存禧,曾德怀.基于神经网络的移动机器人路径规划算法的仿真[J].华南理工大学学报:自然科学版,2003,31(6):56-59.
[101]孙树栋.遗传算法在机器人路径规划中的应用研究[J].西北工业大学学报,1998:16(1):79-83.
[102]李枚毅,蔡自兴.改进的进化编程及其在机器人路径规划中的应用[J].机器人,2000,22(6):490-494.
[103]陈刚,沈林成.复杂环境下路径规划问题的遗传路径规划方法[J].机器人,2001,23(1):40-44,50.
[104]曹俊,朱如鹏.一种改善遗传算法早熟现象的方法[J].上海大学学报:自然科学版.2003,9(3):229-231,237.
[105]邓莉,鲁瑞华.一种改进的抑制早熟收敛的模糊遗传算法[J].计算机科学,2007,34(11):150-153.
[106]巩敦卫,朱美强,郭西进,李明.一种新的基于混沌变异解决早熟收敛的遗传算法[J].控制与决策,2003,18(6):686-689.
[107]KHABIT O.Real-time obstacle avoidance for manipulatiors and mobile robots[J].The International Journal of Robotics Research,1985,5(2):500-505.
[108]BORENSTEIN J.and KOREN Y.Real-time obstacle avoidance for fast mobile robots[J].IEEE Transactions on Systems,Man and Cybernetics.1989,19(5):1179-1187.
[109]KOREN Y.and BORENSTEIN J.Potential field methods and their inherent limitations for mobile robot navigation[A].Proceedings of IEEE Conference on Robotics and Automation[C].Sacramento USA:IEEE.1991:1398-1404.
[110]BORENSTEIN J.and KOREN Y.The vector field histogram-fast obstacle avoidance for mobile robots[J].IEEE Journal of Robotics and Automation.1991,7(3):278-288.
[111]ULRICH I.and BORENSTEIN J..VFH+:Reliable obstacle avoidance for fast mobile robots[A].Proceedings of the 1998 IEEE International Conference on Robotics and Automation[C].1998,5:1572-1577.
[112]ULRICH I.and BORENSTEIN J.VFH*:Local obstacle avoidance with look-ahead verification[A].Proceedings of the 2000 IEEE International Conference on Robotics and Automation[C].2000(4):2505-2511.
[113]熊举峰.群机器人分布控制及优化[D],长沙:中南大学,2009.
[114]BALCH T,ARKIN R C.Behavior-based formation control for multi-robot teams[J].IEEE Transactions on Robotics and Automation,1999,14(6):926-939.
[115]董胜龙,陈卫东,席裕庚.多移动机器人编队的分布式控制系统[J].机器人,2000,22(6):433-438.
[116]韩学东,洪炳熔,孟伟.多机器人任意队形分布式控制研究[J].机器人,2003,25(1):66-72.
[117]柳林,刘斐,季秀才,卢惠林,海丹,郑志强.全向移动机器人编队分布式控制研究[J].机器人,2007,29(1):23-28.
[118]高云园,郭云飞,韦巍.协作多机器人用于未知环境完全探测和地图构建[J].仪器仪表学报,2007,28(7):1259-1264.
[119]张洪峰,王硕,谭民,王龙.基于动态分区方法的多机器人协作地图构建[J].机器 人,2003,25(2):156-162.
[2]陈卫东,张飞.移动机器人的同步自定位与地图创建研究进展[J].控制理论与应用,2005,22(3):455-460.
[3]ISLER V,KANNAN S,DANIILIDIS K.Local Exploration:Online Algorithms and a Probabilistic Framework[A].Proceedings of the 2003 IEEE International Conference on Robotics and Automation[C].USA:IEEE,2003.1913-1920
[4]蔡自兴,崔益安.多机器人覆盖技术研究进展[J].控制与决策,2008,23(5):481-486.
[5]CHOSET H..Coverage for robotics:a survey of recent results[J].Annals of Mathematics and Artificial Intelligence,2001,31:113-126.
[6]邱雪娜,刘士荣,宋加涛等.不确定动态环境下移动机器人的完全遍历路径规划[J].机器人.2006,28(6):586-592.
[7]郝宗波,洪炳镕.多简单机器人协作覆盖规划研究[J].机器人,2007,29(1):18-22.
[8]GABRIELY Y,RIMON E.Spanning-tree based coverage of continuous areas[J].Annals of Mathematics and Artificial Intelligence,2001,31(1-4),77-98.
[9]OHYA A.,NAGASHIMA Y.,YUTA S.Exploring unknown environment and map construction using ultrasonicsensing of normal direction of walls[A]..Proceedings of the IEEE International Conference on Robotics and Automation[C].IEEE,San Diego,USA:1994.485-492.
[10]YAMAUCHI B.,A frontier-based approach for autonomous exploration[A].Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation[C].Monterey,CA:IEEE,1997.146-151.
[11]WULLSCHLEGER F.H.,ARRAS K.O.,AND VESTLI S.J.A flexible exploration framework for map building[A].Proceedings of the Third European Workshop on Advanced Mobile Robots[C].Zurich,Switzerland:1999.49-56.
[12]MAKARENKO A.A.,WILLIAMS S.B.,BOURGAULT F,and DURRANT-WHYTE H.F.An experiment in integrated exploration[A].Proceedings of IEEE/RSJ Int.Conf.on Intelligent Robots and Systems[C].Lausanne,Switzerland:IEEE,2002.534-539
[13]周光明,贾梦雷,陈宗海.移动机器人未知环境自主探测的一种高效算法[J].上海交通大学学报,2005,39(6):936-940
[14]TOVAR B.,MURRIETA-CID R.,ESTEVES C..Robot motion planning for map building[A].Proceedings of the IEEE/RSJ Int.Conference on Intelligent Robots and Systems[C].Lausanne,Switzerland:IEEE,2002:673-680.
[15]GONZALEZ-BANOS H.,EFRAT A.,LATOMBE J.C.,MAO E.,and MURALI T.M.Planning robot motion strategies for efficient model construction[A].In Hollerbach J.and Koditschek D.,editors,The Ninth Int.Symp.on Robotics Research[C],Salt Lake City,UT:Springer-Verlag,1999.345-352.
[16]GONZALEZ-BANOS H.H.and LATOMBE J.C.Navigation strategies for exploring indoor environments[J].International Journal of Robotics Research,2002,21(10):829-848.
[17]张恒,樊晓平.移动机器人同步定位与地图构建过程中的轨迹规划研究[J].机器人.2006.28(3):285-290.
[18]FREDA L.,ORIOLO G.,VENDITTELLI M.Probabilistic strategies for sensor-based exploration[A].WAC International Symposium on Robotics and Applications[C],Spain:2004.63-68.
[19]FREDA L.,ORIOLO G.Frontier-based probabilistic strategies for sensor-based exploration[A].Proceedings of the 2005 IEEE Int.Conf.on Robotics and Automation[C]Barcelona,Spain:2005.2892-3898.
[20]MACKAY D.J.C.Bayesian methods for adaptive models[D].Pasade ha:California Institute of Technology,1991.
[21]SIM R.,ROY N.Global A-Optimal Robot Exploration in SLAM[A].Proceedings of the IEEE International Conference on Robotics and Automation[C],Piscataway,USA:IEEE,2005.http://www.cs.ubc.ca/～simra/publications/sim_roy_icra05.pdf.
[22]HUANG Y F,GUPTA K.RRT-SLAM for Motion Planning with Motion and Map Uncertainty for Robot Exploration[A].IEEE/RSJ International Conference on Intelligent Robots and Systems[C].Nice,France:IEEE,2008:1077-1082.
[23]孟偲,王田苗.一种移动机器人全局最优路径规划算法[J].机器人.2008,33(3):217-222.
[24]ISLER V.,KANNAN S.,AND DANIILIDIS K.Local exploration algorithms:Online algorithms and probabilistic framework[A].Proceedings IEEE Int.Conf.on Robotics and Automation[C].Taipei,Taiwan:IEEE,2003:12-17.
[25]STACHNISS C.,GRISETTI G.,and BURGARD W.Information gain-based exploration using rao-blackwellized particle filters[A].Proceedings of Robotics:Science and Systems[C],Cambridge,U.K.2005.hap://www.roboticsproceedings.org/rss01/p09.pdf.
[26]LIU Y,DONG J,SUN F C.An Efficient Navigation Strategy for Mobile Robots with Uncertainty Estimation[A].Proceedings of the 7th World Congress on Intelligent Control and Automation[A].Chongqing,China:IEEE,2008:5174-5179.
[27]DOUCET A.,DE FREITAS J.F.G.,MURPHY V.,and RUSSEL S.Raoblackwellized partcile filtering for dynamic bayesian networks[A].Proceedings of the Conf.on Uncertainty in Artificial Intelligence(UAI)[C],Stanford,CA,USA,2000:176-183.
[28]REKLEITIS,I.,MEGER,D.,DUDEK,G.Simultaneous planning localization,and mapping in a camera sensor network[J].Robotics and Autonomous Systems(RAS) Journal,2005,special issue on Planning and Uncertainty in Robotics:http://cim.mcgill.ca/～dmeger/publications/SNetSPLAM.pdf.
[29]YI X C,ZHANG H,HAI D et al.A particle filter based active loop closing approach to autonomous robot exploration and mapping[A].Proceedings of 2008 IEEE International Conference on Mechatronics and Automation[C].Takamatsu,Japan:IEEE,2008:824-830.
[30]DUDEK,G.,JENKIN,M.,MILIOS,E.WILKES,D..A taxonomy for swarm robots[A],Proceedings of the 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems[C].Yokohama,Japan:IEEE,1993,1,441-447.
[31]FARINELLI,A.,IOCCHI,L.,NARDI,D..Multirobot systems:a classification focused on coordination[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B:Cybernetics.2004,34(5):2015-2028.
[32]谭民,王硕,曹志强.多机器人系统[M].北京:清华大学出版社,2005.
[33]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):786-794.
[34]STENTZ A.and BRUMITT B L..Dynamic Mission Planning for Multiple Mobile Robots[A],Proceedings of the 1996 IEEE International Conference on Robotics &Automation[C].Minneapolis,MN,USA:IEEE,1996,No.3,2396-2401
[35]SINGH S.and THAYER S.Immunology Directed Methods for Distributed Robotics:A Novel,Immunity-Based Architecture for Robust Control & Coordination[A].Proceedings of SPIE:Mobile Robots ⅩⅥ,vol.4573[C].IEEE,2001.http://www.ri.cmu.edu/pubs/pub_3845.html
[36]SIMMONS R.,APFELBAUM D.,BURGARD W.,FOX D.,THRUN S.,YOUNES H.. Coordination for multi-robot exploration and mapping[A].Proceedings of the National Conference on Artificial Intelligence[A].Austin,TX:AAAI,2000.http://citeseer.ist.psu.edu/simmons00coordination.html.
[37]姜健,赵杰,臧希喆.基于并列选择遗传算法的多机器人协作探测[J].计算机工程与设计,2008,29(5):1218-1221.
[38]LATIMER IV D.,SRINIVASA S.,LEE-SHUE V.Towards Sensor Based Coverage with Robot Teams[A].Proceedings of the 2002 IEEE International Conference on Robotics &Automation[C].Washinton,DC:IEEE,2002.961-967.
[39]WAGNER,M.LINDENBAUM,A.BRUCKSTEIN.Distributed covering by ant-robots using evaporating traces[J].IEEE Transactions on Robotics and Automation,1999,15(5):918-933.
[40]THAYER S.M.,SINGH S.P.N.Development of an immunology-based multi-robot coordination algorithm for exploration and mapping domains[A].Proceedings of the 2002IEEE International Conference on Intelligent Robots and Systems[C].EPFL,Switzerland:IEEE,2002,2735-2739.
[41]YAMAUCHI.B.Frontier-based exploration using multiple robots[A].In Second International Conference on Autonomous Agents[C].Minneapolis,MN:1998.47-53.
[42]NAVARRO-SERMENT L.E.,PAREDIS C.J.J.,KHOSLA P.K.,A Beacon System for the Localization of Distributed Robotic Teams[A].Proceedings of the International Conference on Field and Service Robotics[J],Pittsburgh,PA:IEEE,1999,http://www.ri.cmu.edu/pubs/pub_3122.html
[43]REKLEITIS,I.,DUDEK,G.MILIOS,E.Experiments in Free-Space Triangulation Using Cooperative Localization[A].Proceedings of the 2003 IEEE International Conference on Intelligent Robots and Systems[C].Las Vegas:IEEE,2003.1777-1782.
[44]REKLEITIS,I.M.;DUDEK,G.;MILIOS,E.E..Multi-robot collaboration for robust exploration.[A].Proceedings of the 2000 IEEE International Conference on Robotics &Automation[C].San Francisco:IEEE,2000.3164-3169.
[45]ZLOT R.,STENTZ A.R.,DIAS M.B.,THAYER S.Multi-Robot Exploration Controlled by a Market Economy[A].Proceedings of the 2002 IEEE International Conference on Robotics & Automation[C].Washington,DC:IEEE,2002:3016-3023
[46]KO J.,STEWART B.,FOX D.,KONOL1GE K.,LIMKETKAI B.A practical,decision-theoretic approach to multi-robot mapping and exploration[A].Proceedings of the 2003 IEEE International Conference on Intelligent Robots.and Systems[C].Las Vegas:IEEE,2003:3232-3238.
[47]Robotics Developer Studio.http://msdn.microsoft.com/zh-cn/library/bb483024(en-us).aspx [2009-6-9].
[48]MobileSim.http://robots.mobilerobots.com/wiki/MobileSim.[2009-6-9].
[49]MobotSim.http://www.mobotsoft.com/[2009-6-9].
[50]OpenGL http://www.opengl.org/documentation/[2009-7-1].
[51]张翮.全方位移动机器人的运动建模与控制[D].杭州:浙江大学,2005.
[52]庄严,王伟,王珂,徐晓东.移动机器人基于激光测距和单目视觉的室内同时定位和地图构建[J].自动化学报,2005,31(6):925-933.
[53]ZADEH L.A.Fuzzy sets[J].Information and Control.1965:8(3):338-353.
[54]吴晓莉,林哲辉.MATLAB辅助模糊系统设计[M].西安:西安电子科技大学出版社,2002.
[55]王士同.模糊系统、模糊神经网络及应用程序设计[M].上海:上海科学技术文献出版社,1997.
[56]熊和金,陈德军.智能信息处理[M].北京:国防工业出版社,2006.
[57]熊蓉,褚健,吴俊.基于点线相合的机器人增量式地图构建[J]控制理论与应用,2007,24(2):170-176.
[58]THURN S.,Burgard W.,FOX D.A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots[J].Autonomous Robots,1999,5(3-4):253-271.
[59]HOWARD A.MATARK M.J.SUKHATME G.S.Localization for mobile robot teams using maximum likelihood estimation[A].IEEE International conference on Intelligent Robots and System[C].Lausanne,Swiss,2002:434-439.
[60]王正志,薄涛.进化计算[M].长沙:国防科技大学出版社,2000.
[61]任子武.基于进化计算的前向神经网络学习算法研究[D].哈尔滨:哈尔滨工业大学,2008.
[62]王凌.智能优化算法及其应用[M].北京:清华大学出版社,2001.
[63]LEUNG Y.,GAO Y.XU Z.B..Degree of population diversity-a perspective on prematureconvergence in genetic algorithms and its Markov chain analysis[J].IEEE Transactions on Neural Networks,2002,8(5):1165-1176.
[64]KUREICHICK V.,MELIKHOV A.N.,.MIAGHICK V.V,et al.Some New Features in the Genetic Solution of the Traveling Salesman Problem[A].In lan Parmee and M.J.Denham eds.Adaptive computing in Engineering Design and Control 96(ACEDC'96),2nd International Conference of the Integration of Genetic Algorithms and Neural Network Computing and Related Adaptive Computing.Current Engineering Practice, Plymouth, UK, 1996.
[65] FONLUPT C., PREUX P., ROBILLIARD D.. Preventing Premature Convergence via Cooperating Genetic Algorithms[A]. Proceedings Mandel'97[C]. Morgan Kaufmann,1997.
[66] ROCHA M, NEVES,J..Preventing Premature Convergence to Local Optima in Genetic Algorithms via Random[A]. IEA 99[C],Springer-Verlag Berlin ,1999:127-136.
[67] PELIKAN M, GOLDBERG DE, LOBO FG. A survey of optimization by building and using probabilistic models[J]. Computational Optimization and Applications,2002,21(1):5-20.
[68]周树德,孙增圻．分布估计算法综述[J] 自动化学报,2007,33(2)：113-124．
[69] BALUJA, S. Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning[R].Technical report CMU-CS-96-163, Pittsburgh, PA: Carnegie Mellon University, 1994.
[70] MUHLENBEIN H. The equation for response to selection and its use for prediction[J]. Evolutionary Computation, 1997,5(3):303-346.
[71] HARIK G R, LOBO F G, GOLDBERG D E. The compact genetic algorithm [J]. IEEE Transactions on Evolutionary Computation. 1999,3(4):287-297.
[72] HOFELD M,RUDOLPH G Towards a theory of population-based incremental learning[A]. Proceedings of the 4th IEEE conference on evolutionary computation[C]. Indianapolis:IEEE, 1997:1-5.
[73] DE BONET J S,ISBELL CL,VIOLA J P.MIMIC: Finding optima by estimating probability densities. Advances in Neural Information Processing Systems[M], Cambridge:MIT Press, 1997:424-430
[74] ZHANG Q., MUHLENBEIN H. On the convergence of a class of estimation of distribution algorithms[J]. IEEE Transactions on Evolutionary Computation. 2003,8(2): 127-136.
[75] SUGIHARA K, SMITH J. Genetic Algorithms for Adaptive Motion Planning of an Autonomous Mobile Robot[A]. IEEE International Symposium on Computational Intelligence in Robotics and Automation[C], 1997:138-143.
[76] TU J,YANG S. Genetic algorithm based path planning for a mobile robot[A]. IEEE International Conference on Robotics and Automation[C]. 2003,1:1121-1126.
[77] RYAN C. Reducing Premature Convergence in Evolutionary Algorithms[D]. Dublin:NationaI University of Ireland, 1996.
[78] POSIK P..Preventing Premature Convergence in a Simple EDA Via Global Step Size Setting[A]. Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X[C]. Springer-Verlag,2008: 549 - 558.
[79] YUAN B., GALLAGHER M. On the importance of diversity maintenance in estimation of distribution algorithms[A]. Proceedings of the Genetic and Evolutionary Computation Conference[C]. New York:ACM,2005:719 - 726.
[80] YUAN B., GALLAGHER M.: A mathematical modelling technique for the analysis of the dynamics of a simple continuous EDA[A].IEEE Congress on Evolutionary Computation[C]Vancouver, Canada: IEEE,2006: 1585-1591.
[81] GRAHL J., BOSMAN P.A.N., ROTHLAUF F.: The correlation-triggered adaptive variance scaling IDEA[A]. Proceedings of the 8th annual conference on Genetic and Evolutionary Computation Conference [C]. New York:ACM,2006:397-404.
[82] BOSMAN P.A.N., GRAHL J., ROTHLAUF F.SDR: A better trigger for adaptive variance scaling innormal EDAs[A]. Proceedings of the 9th annual conference on Genetic and Evolutionary Computation[C]. New York:ACM,2007: 492-499.
[83] POSIK, P. Truncation selection and gaussian EDA:Bounds for sustainable progress in high-dimensional spaces[A]. Evo workshops[C]. Heidelberg,Springer,2009:525-534.
[84] BARRAQUAND J, LATOMBE J.C. Robot motion planning: a distributed representation approach[J]. International Journal of Robotics Research, 1991,10(6): 628 - 649
[85] LOZANO-PEREZ T. Spatial Planning: A Configuration Space Approach[J]. IEEE Transactions on Computers, 1983, C-32(2):108-120.
[86] KHATIB, O. Real-time obstacle avoidance for manipulators and mobile robots[J]. International Journal of Robotics Research. 1986,5(1):90-98.
[87] CORMEN T. H, LEISERSON C. E., RIVEST R. L., Stein C.Introduction to Algorithms, Second Edition[M]. MIT Press and McGraw-Hill, 2001. Section 22.3: Depth-first search:540-549.
[88] DIJKSTRA E. W. A note on two problems in connexion with graphs[J]. In Numerische Mathematik, 1959,1:269-271.
[89] DECHTER R. PEARL J. Generalized best-first search strategies and the optimality of A*[J]. Journal of the ACM,1985,32(3): 505 - 536
[90] STENTZ A. Optimal and efficient path planning for partially-known environments[A]. IEEE International Conference on Robotics and Automation[C]. San Diego:IEEE,1994:3310-3317.
[91]STENTZ A.T.The Focussed D~* Algorithm for Real-Time Replanning[A].Proceedings of the International Joint Conference on Artificial Intelligence[C].1995:1652-1659.
[92]AOKI T.MATSUNO M.SUZUKI,T.OKUMA,S.Motion planning for multiple obstacles avoidance of autonomous mobile robot using hierarchical fuzzy rules[A].IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems[C].Las Vegas:IEEE,1994:265-271.
[93]蔡自兴,郑敏捷,邹小兵.基于激光雷达的移动机器人实时避障策略[J].中南大学学报(自然科学版),2006,37(2):324-329.
[94]郑敏捷,蔡自兴,于金霞.一种动态环境下的移动机器人避障策略[J].高技术通讯,2006,16(8):813-819.
[95]黄炳强,曹广益.基于模糊人工势场的移动机器人路径规划[J].上海理工大学学报,2006,28(4):347-350.
[96]张汝波,顾国昌.水下智能机器人模糊局部规划器设计[J].机器人,1996,18(3):158-162.
[97]付宜利,顾晓宇,王树国.基于模糊控制的自主机器人路径规划策略研究[J].机器人,2004,26(6):548-552.
[98]庄晓东,孟庆春.动态环境中基于模糊概念的机器人路径搜索方法[J].机器人,2001,23(5):397-399,458.
[99]陈宗海,陈锋.一种不确定环境下移动机器人的避障规划算法[J].机器人,2002,24(2):358-361.
[100]陈华志,谢存禧,曾德怀.基于神经网络的移动机器人路径规划算法的仿真[J].华南理工大学学报:自然科学版,2003,31(6):56-59.
[101]孙树栋.遗传算法在机器人路径规划中的应用研究[J].西北工业大学学报,1998:16(1):79-83.
[102]李枚毅,蔡自兴.改进的进化编程及其在机器人路径规划中的应用[J].机器人,2000,22(6):490-494.
[103]陈刚,沈林成.复杂环境下路径规划问题的遗传路径规划方法[J].机器人,2001,23(1):40-44,50.
[104]曹俊,朱如鹏.一种改善遗传算法早熟现象的方法[J].上海大学学报:自然科学版.2003,9(3):229-231,237.
[105]邓莉,鲁瑞华.一种改进的抑制早熟收敛的模糊遗传算法[J].计算机科学,2007,34(11):150-153.
[106]巩敦卫,朱美强,郭西进,李明.一种新的基于混沌变异解决早熟收敛的遗传算法[J].控制与决策,2003,18(6):686-689.
[107]KHABIT O.Real-time obstacle avoidance for manipulatiors and mobile robots[J].The International Journal of Robotics Research,1985,5(2):500-505.
[108]BORENSTEIN J.and KOREN Y.Real-time obstacle avoidance for fast mobile robots[J].IEEE Transactions on Systems,Man and Cybernetics.1989,19(5):1179-1187.
[109]KOREN Y.and BORENSTEIN J.Potential field methods and their inherent limitations for mobile robot navigation[A].Proceedings of IEEE Conference on Robotics and Automation[C].Sacramento USA:IEEE.1991:1398-1404.
[110]BORENSTEIN J.and KOREN Y.The vector field histogram-fast obstacle avoidance for mobile robots[J].IEEE Journal of Robotics and Automation.1991,7(3):278-288.
[111]ULRICH I.and BORENSTEIN J..VFH+:Reliable obstacle avoidance for fast mobile robots[A].Proceedings of the 1998 IEEE International Conference on Robotics and Automation[C].1998,5:1572-1577.
[112]ULRICH I.and BORENSTEIN J.VFH*:Local obstacle avoidance with look-ahead verification[A].Proceedings of the 2000 IEEE International Conference on Robotics and Automation[C].2000(4):2505-2511.
[113]熊举峰.群机器人分布控制及优化[D],长沙:中南大学,2009.
[114]BALCH T,ARKIN R C.Behavior-based formation control for multi-robot teams[J].IEEE Transactions on Robotics and Automation,1999,14(6):926-939.
[115]董胜龙,陈卫东,席裕庚.多移动机器人编队的分布式控制系统[J].机器人,2000,22(6):433-438.
[116]韩学东,洪炳熔,孟伟.多机器人任意队形分布式控制研究[J].机器人,2003,25(1):66-72.
[117]柳林,刘斐,季秀才,卢惠林,海丹,郑志强.全向移动机器人编队分布式控制研究[J].机器人,2007,29(1):23-28.
[118]高云园,郭云飞,韦巍.协作多机器人用于未知环境完全探测和地图构建[J].仪器仪表学报,2007,28(7):1259-1264.
[119]张洪峰,王硕,谭民,王龙.基于动态分区方法的多机器人协作地图构建[J].机器 人,2003,25(2):156-162.