基于组合定位技术的多智能车辆合作编队仿真技术研究
|
摘要
在当今智能车研发领域,多智能车通过个体间的相互协作可以完成单一智能车无法完成的复杂任务,其中多智能车编队技术是多智能车系统的一个重要研究方向。智能车在执行一些复杂任务过程中,如安全巡逻、护卫等,保持某种队形具有重要意义。因此,在研究编队控制中如何让智能车根据要完成的目标,形成指定的队形,具有重要的理论研究意义和实用价值。其中定位技术是编队的基础和关键技术之一。全球定位系统GPS (Global Position System)长期误差小,但短时误差较大,航位推算DR (Dead Reckoning)系统短期精度好,但长期精度较差,存在误差漂移。车载GPS-DR组合定位技术可以通过数据融合提供高精度和高频率以及高可靠性的定位数据。但由于使用GPS的局限性,本文通过将视觉数据和激光测距数据引入到GPS-DR组合定位技术中,研究出了一种新的组合定位方法,其基本思想是利用视觉传感器和激光测距传感器感知环境创建环境地图、建立避障路径规划、进行GPS/DR定位,利用已经创建的环境地图校正基于运动模型的车辆位姿估计误差,提高定位精度；同时根据车辆可靠的位姿,创建出精度更高的地图。
本文在传统的组合定位基础上,提出了一种基于迭代测量更新的中心差分粒子滤波器算法来代替其中的扩展卡尔曼滤波器(EKF),并迭代融合新的观测数据使提议分布更加接近后验概率分布,而能够精确估计智能车辆的位姿,进而更新特征地图的位置。该算法在保证车辆定位精度的同时减少了计算的复杂度,提高了系统的估计性能,增加了迭代算法的稳定性。仿真实验的结果验证了方法的有效性。本文所提出的方法为智能车辆在室外未知环境下的定位与地图创建提供了一种新思路。
本文针对传统合同网协议在多智能车编队领域中动态多任务分配问题时开销大、速度慢、无法适应环境的动态变化等缺点,通过对Agent的能力进行量化描述,综合了Agent的性格特征、他信度和熟人度,并利用CBR技术对投标对象进行了限制,利用虚拟Leader为编队车辆设计所需要的目标点,并采用匈牙利算法将这些目标点最优分配给各个编队车辆,通过将社会势场力作为MotorScheme的一种行为,设计了动态环境下基于拍卖理论的多智能车辆合作编队控制方法,实现了多个智能车之间采用合适的策略协调合作形成各种不同的编队队形,并且在所设计的多智能车协作编队仿真系统平台上进行了验证与分析。
仿真实验证明了本文所提算法的可行性和有效性。
In today's intelligent vehicles research field, Multi-intelligent vehicles through the individual of mutual cooperation can complete complex tasks which a single intelligence vehicle can't complete, multi-intelligent vehicles formation system is one of the important research direction many intelligent vehicle technology. In the implementation of the process of some complex tasks, such as security guards, patrol, searches, secures etc, to keep a certain formation have the important meaning. Therefore, the study of how to control formation according to the goal, forming a designated formation for intelligent vehicles has important theoretical significance and practical value. With the location technology is the basis and the key technology for formation. Global Positioning System (GPS) has good long-term error precision, but short-term error is bigger, Dead Reckoning (DR) System will have good short-term precision, but long poor precision. On-board GPS-DR combination positioning technology can give some high precision and high frequency and high reliability of the location data through the data fusion. But because of the limitations of using GPS, this paper introduced visual data into the GPS-DR combination positioning technology and developed a new combination Localization method. The basic idea is to use visual sensor to perceive environment to create an environment map, establish obstacle avoidance path planning and complete GPS/DR positioning, use the environment map to correct vehicle pose estimation error and improve the localization precision; create a higher precision environment map according to the reliable vehicle pose.
This paper provided a Iterated Central Difference Kalman Filter(ICDF) to compute the proposal distribution in Rao-Blackwellized Particle filter instead of the Extended Kalman Filter and fusion with new observation to obtain the Posteriori Probability, Estimate the position of the Vehicle and update the features of the environment by ICDKF. This algorithm decreases Computational-complexity, improves the Estimation of Performance and the stability of Iterated algorithm without decrease Accuracy. Simulation results are used to validate the effectiveness of the proposed algorithm.
Because of the big cost, slower negotiation procession, lower adaption to the dynamic changed environment and other shortcomings for traditional contract nets agreement which applied in the fields of dynamic formation multi-tasking assign for Multi-intelligent vehicles, This paper integrated the Agent's character, confidence level and acquaintances of degrees, used the CBR technology to limit bidder, used the virtual Leader as the point of the target to design formation of this vehicles, and optimum allocated these target point to every formation vehicles through the Hungary Algorithm and the social potential field works for a MotorScheme behavior, designed multi-intelligent vehicles cooperation formation control method through the auction theory under the dynamic environment, realized some different formation for multi-intelligent vehicles through some proper strategy of cooperation and coordination, and designed a multi-agent simulation system platform for testing and analysising some strategy of cooperation formation.
The simulation results show that the proposed algorithm is feasible and effective.
本文在传统的组合定位基础上,提出了一种基于迭代测量更新的中心差分粒子滤波器算法来代替其中的扩展卡尔曼滤波器(EKF),并迭代融合新的观测数据使提议分布更加接近后验概率分布,而能够精确估计智能车辆的位姿,进而更新特征地图的位置。该算法在保证车辆定位精度的同时减少了计算的复杂度,提高了系统的估计性能,增加了迭代算法的稳定性。仿真实验的结果验证了方法的有效性。本文所提出的方法为智能车辆在室外未知环境下的定位与地图创建提供了一种新思路。
本文针对传统合同网协议在多智能车编队领域中动态多任务分配问题时开销大、速度慢、无法适应环境的动态变化等缺点,通过对Agent的能力进行量化描述,综合了Agent的性格特征、他信度和熟人度,并利用CBR技术对投标对象进行了限制,利用虚拟Leader为编队车辆设计所需要的目标点,并采用匈牙利算法将这些目标点最优分配给各个编队车辆,通过将社会势场力作为MotorScheme的一种行为,设计了动态环境下基于拍卖理论的多智能车辆合作编队控制方法,实现了多个智能车之间采用合适的策略协调合作形成各种不同的编队队形,并且在所设计的多智能车协作编队仿真系统平台上进行了验证与分析。
仿真实验证明了本文所提算法的可行性和有效性。
In today's intelligent vehicles research field, Multi-intelligent vehicles through the individual of mutual cooperation can complete complex tasks which a single intelligence vehicle can't complete, multi-intelligent vehicles formation system is one of the important research direction many intelligent vehicle technology. In the implementation of the process of some complex tasks, such as security guards, patrol, searches, secures etc, to keep a certain formation have the important meaning. Therefore, the study of how to control formation according to the goal, forming a designated formation for intelligent vehicles has important theoretical significance and practical value. With the location technology is the basis and the key technology for formation. Global Positioning System (GPS) has good long-term error precision, but short-term error is bigger, Dead Reckoning (DR) System will have good short-term precision, but long poor precision. On-board GPS-DR combination positioning technology can give some high precision and high frequency and high reliability of the location data through the data fusion. But because of the limitations of using GPS, this paper introduced visual data into the GPS-DR combination positioning technology and developed a new combination Localization method. The basic idea is to use visual sensor to perceive environment to create an environment map, establish obstacle avoidance path planning and complete GPS/DR positioning, use the environment map to correct vehicle pose estimation error and improve the localization precision; create a higher precision environment map according to the reliable vehicle pose.
This paper provided a Iterated Central Difference Kalman Filter(ICDF) to compute the proposal distribution in Rao-Blackwellized Particle filter instead of the Extended Kalman Filter and fusion with new observation to obtain the Posteriori Probability, Estimate the position of the Vehicle and update the features of the environment by ICDKF. This algorithm decreases Computational-complexity, improves the Estimation of Performance and the stability of Iterated algorithm without decrease Accuracy. Simulation results are used to validate the effectiveness of the proposed algorithm.
Because of the big cost, slower negotiation procession, lower adaption to the dynamic changed environment and other shortcomings for traditional contract nets agreement which applied in the fields of dynamic formation multi-tasking assign for Multi-intelligent vehicles, This paper integrated the Agent's character, confidence level and acquaintances of degrees, used the CBR technology to limit bidder, used the virtual Leader as the point of the target to design formation of this vehicles, and optimum allocated these target point to every formation vehicles through the Hungary Algorithm and the social potential field works for a MotorScheme behavior, designed multi-intelligent vehicles cooperation formation control method through the auction theory under the dynamic environment, realized some different formation for multi-intelligent vehicles through some proper strategy of cooperation and coordination, and designed a multi-agent simulation system platform for testing and analysising some strategy of cooperation formation.
The simulation results show that the proposed algorithm is feasible and effective.
引文
[1]杨东凯,吴今培,张其善.智能交通系统(ITS)的发展及其模型化研究[J],北京航空航天大学学报,2000,26(1)：22-25
[2]Strategic plan for intelligence vehicle-highway systes in the United States, Intelligent Vehicle Highway of America,1992.
[3]杨兆升.智能运输系统概论[M].北京：清华大学出版社,2004.9
[4]董绪荣,张守信,华仲春GPS/DR组合导航定位及其应用[M].北京：国防科技大学出版社,1998.6
[5]常青,杨东凯,寇艳红,张其善.车辆导航定位方法及其应用[M].北京：机械工业出版社,2005.5
[6]Kopits E, Cropper M. Traffic fatalities and economic growth. Research Report of the World Banks, (3035), April,2003
[7]彭飞.智能车辆定位导航系统的研究[博士论文].北京：北京航空航天大学,2000.
[8]黄卫,陈里得.智能运输系统(ITS)概论,北京：人民交通出版社,1999.9.123-152
[9]赵亦林.乍辆定位与导航系统.电子工业出版社,1998.
[10]致和,王万义译.GPS原理与应用[M].北京：电子工业出版社,2002
[11]Kerr TH. KERR T. Decentralized Filtering and Redundancy Management for Multisensor Navigation. IEEE Transaction on Aerospace and Electronic Systems,1987,23(1):83-119
[12]王惠南.GPS导航原理与应用.北京：科学出版社,2003.
[13]高社生,李华星.INS/SAR组合导航定位技术与应用.西安：西北工业大学出版社2004,270-292
[14]袁建平,罗建军,岳晓奎.卫星导航原理与应用.北京：中国宇航出版社2003：305-306
[15]M.A.P. Taylor. Intelligent Transport Systems:Emerging Technologies and Methods in Transportation anf Traffic. Transportation Research.2004,C(3-4):167-169
[16]M.A.Quddus. High Integrity map matching algorithms for advanced transport Telematics applications. PhD thesis of University of London.2006:29-34
[17]彭飞,智能车辆定位导航系统的研究,北京航空航天大学博士论文,2000
[18]杨东凯,ITS系统分析与自动车辆定位,北京航空航天大学博士论文,2000
[19]王常荣,北京市公交系统汽车定位导航系统的设计.北京航空航天大学硕士论文,2000
[20]柏钢,面向ITS的车辆导航与定位技术的应用,东南大学博士论文,1999
[21]卢大伟,刘炳云,王庆,智能GPS/DR组合导航技术在TRACK-1型车载导航仪中的应用,导航,2000,第3期：108-112
[22]张小国,王庆,万德钧,车载组合导航系统中的分级地图匹配算法,中国惯性技术学报,2000,8(3)：46-51
[23]陈则王,袁信,面向ITS的车辆导航技术与定位技术,交通与计算机,2001,19(6)：23-25
[24]张三同,陆地车辆定位定向导航系统的研制与多传感器综合技术的研究,北京理工大学博士论文,1998
[25]向怀坤,车辆导航系统关键技术研究,北京工业大学博士学位论文,2003,6
[26]徐田来,车载组合导航信息融合算法研究与系统实现,哈尔滨工业大学博士学位论文,2007,12
[27]郑贵省,GPS/DR车载组合定位系统数据融合算法研究,天津大学博士学位论文,2005,5
[28]周勇,智能车辆中的几个关键技术研究,上海交通大学博士学位论文,2007,9
[29]F. Peyret, D. Betaille, G. Hintzy. High-Precision Application of GPS in The field of Real-time Equipment Positioning. Automation in Construction.2000,9:299-314
[30]张裕松.我国北斗导航系统定位原理及应用前景探讨.电子技术参考,2002(1)：6-14
[31]M.Kam, C.Rorres, W.Chang, X.Zhu. Performance and Geometric Interpretation for Decision Fusion with Memory. IEEE Transactions on Systems, Man, and Cybernetic-Part A:Systems and Humans,1999,29(1):52-62
[32]S.Ben-yacoub, Y. Abdeljaoued, E. Mayoraz. Fusion of Face and Speech Data for Person Identity Verification. IEEE Transactions on Neural Networks,1999,10(5):1065-1074
[33]J. Figue, M. Grabisch, M.P. Charbonnel. A Method for Still Image Interpretation Relying on A Multi-Algorithms Fusion Scheme:Application to Human Face Characterization. Fzzy sets and Systems,1999,103(2),317-337
[34]C. Rago, P.Willett, M. Alford. Predetection Fusion:Resolution Cell Grid Effects. IEEE Transactions on Aerospace and Eletronic Systems,1999,35(3):778-789
[35]A.M. Aziz, M. Tummala, R.Cristi. Fuzzy Logic Data Correlation Approach in Multi-sensor-multi-target Tracking Systems. Signal Processing,1999,76(2):195-209
[36]J.A. Fayman, P. Pirjanian, H. Christensen, E. Rivlin. Exploiting Process Integration and Composition in the Context of Active Vision. IEEE Transactions on Systems, Man, and Cybernetic Part C:Applications and Reviews,1999,29(1):73-86
[37]X.Tang. Multiple Competitive Learning Network Fusion for Object Classification. IEEE Transactions on Systems, man,and Cybernetic Part B:Cybernetics,1998,28(4):532-543
[38]高社生,李华星,INS/SAR组合导航定位技术与应用,西安：西北工业大学出版社,2004,270-292
[39]戴亚平,刘征等译,多传感器数据融合理论及其应用,北京：北京理工大学出版 社,2004,2-50
[40]康耀红著,数据融合理论与应用,西安：西安电子科技大学出版社,1997,1-20
[41]高健,DSmT信息融合技术及其在智能车地图创建中的应用,华中科技大学博士论文,2009,5
[42]杨兆升,杨庆芳,冯金巧.路段平均速度组合融合算法及其应用.吉林工业大学学报(工学版),2004,34(4)：675-678
[43]P. C. Lin, H. Komsuoglu, D.E. Koditschek. Sensor data fusion for body state estimation in a hexapod robot with dynamical gaits. IEEE Transactions on Robotics,2006,22(5): 932-943
[44]Q. Yang, J. Sun. An underwater autonomous robot based on multi-sensor data fusion. In Proceeding of the 6th World Congress on Intelligent Control and Automation. Dalin, China: 2006.9139-9143
[45]B. Guo, M.S. Nixon, T.R. Damarla. Acoustic information fusion for ground vehicle classification. In Proceeding of the 11th International Conference on information Fusion. Cologne, Germany:2008.1-7
[46]R. Jurgens, W. Becker. Perception of angular displacement without landmarks:evidence for Bayesian fusion of vestibular, optokinetic, podokinesthetic, and cognitive information. Experimental Brain Research,2006,174(3):528-543
[47]G. Kmberova, R. mandelbaum, M. Mintz. Statistical deeision theory for mobileRobotics: theory and application. In:proeeeding of the 1996 IEEE/SICE/RSJIntemational Conferenceon Multisensor Fusion and hitegration forl ntelligent Systems. Washington, USA:1996.17-24
[48]F. Chausse, S.M. Baek, S. Bonnet, et al. Experimental comparison of EKF and constraint manifold particle filter for robot location. In Proceeding of the 2008 IEEE International Conference on Multi-sensor Fusion and Integration for Intelligent Systems. Seoul, Korea: 2008.399-404
[49]罗荣华.基于粒子滤波器的家庭智能车同时定位与地图创建研究。哈尔滨工业大学博士学位论文,2005,10
[50]Li Maohai, Hong Bingrong, Cai Zesu. Novel indoor mobile robot navigation using monocular vision. Engineering Applications of Artificial Intelligence, Elsevier Science 2008:485-497
[51]R.C. Luo, M.G. Kay. Multisensor integration and fusion in intelligent systems. IEEE Transactions on Systems, Man and Cybernetics,1989,19(5):901-931
[52]A. Noureldin, A. El-Shafie, M. R. Taha. Optimizing neuron-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation. Engineering Applications of Artificial Intelligence,207,20(1):49-61
[53]S. R. Mounce, A. Khan, A.S. Wood, et al. Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system. Information Fusion, 2003,4(3):217-229
[54]M. Wenz, H. Worn, Event-based production rules for data aggregation in wireless sensor networks. In Proceeding of the 2006 IEEE International Coference on Multi-sensor Fusion and Integration for Intelligent Systems. Heidelberg, Germany:2006.59-64
[55]B. Moshiri, A. M. Khalkhali, H.R. Momeni. Designing a home security system using sensor data fusion with DST and DSmT methods. In Proceeding of the 10th international Conference on Information Fusion. Quebec, Canada:2007.1-6
[56]Z. Liang, W. Chen, D. Wang, et al. A technique for damage diagnosis of bridge structure based on information fusion. In:Proceeding of the Nondestructive Evaluation and Health Monitoring of Aerospace Materials, Composities, and Civil Infrastructure IV. San Diego, USA:2005.408-415
[57]LValetGMaurisPBolon, et al. A fuzzy rule-based interactive fusion system for seismic data analysis. Information Fusion,2003,4(2):123-133
[58]王卫华,陈卫东,席裕庚.移动智能车地图创建中的不确定传感信息处理.自动化学报,2003,29(2)：267-274
[59]F. Smarandache, J. Dezert. Advances and Applications of DSmT for information Fusion. Vol.1. Rehoboth, USA:American Research Press,2004
[60]H. Wang. A new multiwavelet-based approach to image fusion. Journal of Mathematical Imaging and Vision,2004,21(2):177-192
[61]Y. Guo, B. wu, C. Luo, et al. Correlation voting fusion strategy for part of speech tagging. In Proceeding of the 8th international conference on Signal processing, Beijing, china.31-34
[62]原新,朱奇丹,兰海.基于粗糙集理论的多传感器信息融合。哈尔滨工业大学学报,2006,38(10)：1669-1672
[63]I. V. Maslov, I. Gertner. Multi-sensor fusion:an evolutionary algorithm approach. Information Fusion,2006,7(3):304-330
[64]N. Xiong, P. Svensson. Multi-sensor management for information fusion:issues and approaches. Information Fusion,2002,3(2):163-186
[65]R.E. Kalman. A New Approach to Linear Filtering and Prediction Problem. Journal of Basic Eng(ASME),1960,82D:95-108
[66]S. Vathsal. Spacecraft Attitude Determination Using a Second-Order Nonlinear Filter. Journal of Guidance, Control, and Dynamics.1987,10(6):559-566
[67]S. J. Julier, J. K. Uhlmann, H. F. Durrant-Whyte. A new Approach for Filtering Nonlinear system. Proceedings of the American Control Confirence, USA,1995:1628-1632
[68]S. J. Julier, J. K. Uhlmann, H. F. Durrant-Whyte. A new method for Nonlinear Transformation of Means and Covariances in Filters and Estimator. IEEE Trans. Automatic Control,2000,45(3):477-283
[69]E. Wan, V. Merwe. The Unscented Kalman Filter for Nonlinear Estimation. Proceedings of Symposium 2000 on Adaptive systems for signal processing, Communication and Control (AS-SPCC). IEEE Lake Louise, Alberta, Canada,2000:1255-1260
[70]N. Bergman, L. Ljung, F. Gustafsson. Terrain Navigation Using Bayesian Statistics. IEEE Control System Magazine,1999,19(3):33-40
[71]H. Carvalho, P. D. Moral. Optimal Nonlinear Filtering in GPS/INS Integration. IEEE Trans on AES,1997,33(3):835-849
[72]P. J. Nordlund, F. Gustafsson. Sequential Monte Carlo Filtering Techniques Applied to Integrated Navigation Systems. Proc. of the American Control Conference, Arlington, Virginia, U.S.A,2001:4375-4380
[73]A. S. Babak. Approximate Nonlinear Filtering with Applications to Navigation, PhD Thesis of Linkoping University,2001:53-71
[74][74] K. Nishiyama. Fast and Effective Generation of the Proposal Distribution for Particle Filters. Signal Processing,2005(12):2412-2417
[75]F. Gustafsson, F. Gunnarsson, N. Bergman. Particle Filters for Positioning, Navigation and Tracking. IEEE Transaction on Signal Processing,2002,50(2):425-436
[76]J. F. Ru, X.R. Li. Interacting Multiple Model Algorithm with Maximum Likelihood Estimation for FDI. International Symposium on Intelligent Control. Houston, Texas,2003: 661-666
[77]X.R. Li, Y. Bar-Shalom. Performance prediction of the IMM algorithms. IEEE Trans, on AES,1993,29(3):755-770
[78]X.R. Li, Y. Bar-Shalom. Model-Set Adaptation in Multiple-model Estimation for Hybrid Systems. Proc.1992 American Control Conference.1992:1794-1799
[79][79] T. Kirubarajan, Y. Bar-Shalom. Kalman Filter versus IMM Estimerator:When Do We Need the Latter? IEEE Transactions on Aerospace and Electronic Systems.2003,39(4): 1452-1456
[80]K. A. Fisher, P. S. Maybeck. Multiple Models Adaptive Estimation with Filter Apawning. IEEE Trans. Aerospace and Electronic Systems.2002,38(3):755-767
[81]王璐,蔡自兴.未知环境中移动智能车并发建图与定位(CML)的研究进展.智能车2004,26(4)：380-384
[82]E. Nobot. Simultaneous Localization and Mapping.2002 Summer School, Australian Centre for Field Robotics. The University of Sydney. July 2002:231-237
[83]Kwang Wee Lee, Sardha Wijesoma, Javier Ibanez Guzman. A Constrained SLAM Approach to Robust and Accurate Localization of Autonomous Ground Vehicles. Robotics and Autonomous Systems.2007:1-14
[84]J. Kim. Autonomous Navigation for Airborne Applications. PhD thesis,2004:67-74
[85]Peter. Stromback. Integrating GPS and INS with a Tightly Coupled Kalman Filter Using Proximity Information for Compensating Signal Loss. PhD Thesis, March 2003:63-70
[86]徐景硕,王维江.容错多传感器组合导航系统发展综述.航空电子技术.2006,37(3)：25-28
[87]M. M. Reda, A. Noureldin, N. El-Sheimy. Improving INS/GPS Positioning Accuracy during GPS Outages Using Fuzzy Logic. Proc.Inst.Nav. GPS/GNSS Conf. Sep., 2003:449-508.
[88]Tucker Balch. Behavior-based Formation Control for Multi-robot Teams. IEEE Transactions on Robotics and Automation.1998,14(6):927-939
[89]Zhiqiang, Cao, Liangjun Xie, Bin Zhang, et al. Formation Constrained Multi-robot system in unknown Environments. Proceeding of the 2003 IEEE International Conference on Robotics & Automation, Taiwan,2003:735-740
[90]Donald D. Dudenhoeffer, Michael P. Jones. A formation Behavior for Large-scale Micro-robot force deployment. Proceeding of the 2000 Winter Simulation Conference,2000:972-982
[91]J. Guo, Z. Lin and M. Cao, "Adaptive leader-follower formation control for autonomous mobile robots", in Proc. of 2010 American Control Conference, Maryland, USA.
[92]Eduardo G.H-M. and Eduardo A. B. Convergence and collision avoidance information control:A survey of the Artificial Potential Functions Approach. Multi-Agent Systems-Modeling, Control, Programming, Simulations and Applications. InTech, April 2011,103-126
[93]DUDEN HOEFFER D D, MICHAEL P J. A formation behavior for large-scale micro-robot force deployment A]. Proceedings of the 2000 Winter Simulation Conference[C]. New York,2000
[94]GE S S, FUA C H, LIM K W. Multi-robot formations:queues and artificial potential trenches[A]. Proceedings of the IEEE International Conference on Robotics and Automation[C]. New Orleans,2004
[95]贾秋玲,闫建国,王新民.基于势函数的多智能车系统的编队控制[J].机器人,2006,28(2)：111-114
[96]LIANG Y, LEE H H. Decentralized formation control and obstacle avoidance for multiple robots with nonholonomic constraints[A]. Proceedings of the 2006 American Control Conference[C]. Minneapolis,2006
[97]OGREN, FIORELL I E, L EONARD N E. Formations with a mission:stable coordination of vehicle group maneuvers[A]. Proceedings of the 15th International Symposium on Mathematical Theory of Networks and Systems[C]. Notre Dame,2002
[98]LEONARD N E, FIORELL I E. Virtual leaders, artificial potentials and coordinated control of groups[A]. Proceedings of the 40th IEEE International Conference on Decision and Control[C]. Orlando,2001
[99]BARAS J S, TAN X B, PEDRAM H. Decentralized control of autonomous vehicles[A]. Proceedings of the 42nd IEEE Conference on Decision and Control[C]. Hawaii,2003
[100]Luca Consolinia, Fabio Morbidib, Domenico Prattichizzob, Mario Tosquesc. Leader-follower formation control of nonholonomic mobile robots with input constraints. Automatica 44(2008) 1343-1349
[101]J. Shao, G. Xie, L. Wang. Leader-following formation control of multiple mobile vehicles. IET Control Theory.2007,1(2):545-552
[102]DUNBAR W B. Model predictive control:Extension to coordinated multi-vehicle formations and real-time implementation[R]. Pasadena:California Institute of Technology, 2001
[103]DUNBAR W B, MURRA Y R M. Model predictive control of coordinated multi-vehicle formations[A]. Proceedings of the IEEE Conference on Decision and Control[C]. Las Vegas,2002
[104]DUNBAR W B, MURRA Y R M. Distributed receding horizon control with application to multi-vehicle formation stabilization[J]. Automatica,2006,42(4):549-558
[105]Kwang-Kyo Oh, Hyo-Sung Ahn. Formation control of mobile agents based on inter-agent distance dynamics. Automatica 47(2011):2306-2312
[106]Hernandez-Martinez, E. & Aranda-Bricaire, E. (2010). Decentralized formation control of multi-agent robots systems based on formation graphs, XIV Latinamerican Congress of Automatic Control and ⅪⅩ ACCA Congress, IFAC, Santiago, Chile, pp.1-6.
[107]D. Gu, "A Differential Game Approach to Formation Control", IEEE Trans. Contr. Syst. Technol, vol.16, no.1, pp.85-93, Jan 2008
[108]Shamsi, F.; Abdollahi, F.; Nikravesh, S.K.Y.; Time varying formation control using feedback information differential game approach. Electrical Engineering (ICEE),2011 19th Iranian Conference on.2011:1-6
[109]Ohko, T., K. Hiraki And Y. Anzai, Addressee learning and message interception for communication load reduction in multi-robot environments. Distributed Artificial Intelligence meets machine Learning.1997, Springer-Verlag, Berlin, pp:248-258
[110]Zhou Pu-Cheng, Han Yu-Sheng and Xue Mo-Gen. Extended Contract Net Protocol for Multi-Robot Dynamic Task Allocation. Information Technology Journal.2007,6 (5):733-738
[111]周浦城,洪炳镕,王月海.动态环境下多机器人合作追捕研究.机器人,2005,27(4),289-295
[112]胡晶晶,曹元大,胡军.基于英式拍卖协商协议的多智能体任务分配.计算机集成制造系统,2006,12(5)：795-799
[113]Zlot, R. and A. Stentz, Market-based multirobot coordination for complex tasks. International Journal of Robotics Research,2006,25:73-101
[114]王月海,洪炳镕.机器人部队运动多目标合作追捕算法[J].西安交通大学学报,2003,37(6)：573-576
[115]Hernandez-Martinez, E. & Aranda-Bricaire, E. (2009). Multi-agent formation control with collision avoidance based on discontinuous vector fields,35th Annual Conference of the IEEE Industrial Electronics Society, IEEE, Porto, Portugal, pp.2283-2288.
[116]Russell Gayle, William Moss, Ming C. Lin, Dinesh Manocha:Multi-robot coordination using generalized social potential fields. ICRA 2009:106-113
[117]蓝艇, 《群体机器人的行为决策和群体智能》,华东理工大学硕士论文,2009年
[118]杨立苹,《基于行为的多机器人追捕问题研究》,哈尔滨工业大学硕士论文,2005年
[119]曹志强,《未知环境下多机器人协调与控制的队形问题研究》,中国科学院自动化研究所博士论文,2002年。
[120]郑贵省,《GPS/DR车载组合定位系统数据融合算法研究》,天津大学博士论文,2005
[121]高健,《DSmT信息融合技术及其在机器人地图创建中的应用》,华中科技大学博士论文,2009
[122]杨易,《智能车辆组合定位与路径导航技术研究》,湖南大学博士论文,2006
[123]徐田来,《车载组合导航信息融合算法研究与系统实现》,哈尔滨工业大学博士论文,2007
[124]曹梦龙,《车载导航系统自主重构技术与信息融合算法研究》,哈尔滨工业大学博士论文,2009
[125]李士明,《基于GPS的车辆定位仿真技术研究》,山东理工大学硕士论文,2009
[126]冀常鹏,《基于多GPS天线智能车辆导航/调度系统的研究》,辽宁工程技术大学硕士论文,2004
[127]祖丽楠,《多机器人系统自主协作控制与强化学习研究》,吉林大学博士论文,2006
[128]王锋辉,《面向区域智能运输的多智能车辆协作研究》,上海交通大学博士论文,2009
[129]黄智,《车载导航系统组合定位技术研究》,湖南大学博士论文,2006
[130]陈凤东,《基于认知地图的移动机器人自主导航技术》,哈尔滨工业大学博士论文,2009
[131]李怡斌,《ITS智能车辆关键技术研究》,天津大学博士论文,2008
[132]石朝侠,《基于多机器人协作的未知环境下路径探索研究》,哈尔滨工业大学博士论文,2007
[133]赵梅,《改进的粒子滤波在组合导航中的应用研究》,北京交通大学硕士论文,2006
[134]王志刚,《车载导航GPS/DR/MM组合定位技术研究》,武汉大学硕士论文,2005
[135]许云斌,《基于车载组合导航系统的信息融合算法研究》,青岛科技大学硕士论文,2009
[136]赵倩,《车载GPS导航系统动态滤波算法应用研究》,江苏大学硕士论文,2009
[137]曹衡,《车载GPS/DR/GIS组合导航系统的研究与设计》,长沙理工大学硕士论文,2008
[138]王文杰,《基于视觉的道路区域和车道线识别技术在智能车辆导航中的应用研究》,山东大学硕士论文,2009
[139]杨宏,李亚安,李国辉,袁润平,《一种改进中心差分卡尔曼滤波方法》,计算机工程与应用,2010.7
[140]闫路平,《多机器人合作追捕目标问题研究》,哈尔滨工业大学硕士论文,2008
[141]周浦城,韩裕生,薛模根,洪炳镕,《基于运动图式的多机器人合作追捕》,计算机工程,2008,
[142]焦吉成,《对匈牙利算法的改进及其应用实例》,山东冶金,2000,
[2]Strategic plan for intelligence vehicle-highway systes in the United States, Intelligent Vehicle Highway of America,1992.
[3]杨兆升.智能运输系统概论[M].北京：清华大学出版社,2004.9
[4]董绪荣,张守信,华仲春GPS/DR组合导航定位及其应用[M].北京：国防科技大学出版社,1998.6
[5]常青,杨东凯,寇艳红,张其善.车辆导航定位方法及其应用[M].北京：机械工业出版社,2005.5
[6]Kopits E, Cropper M. Traffic fatalities and economic growth. Research Report of the World Banks, (3035), April,2003
[7]彭飞.智能车辆定位导航系统的研究[博士论文].北京：北京航空航天大学,2000.
[8]黄卫,陈里得.智能运输系统(ITS)概论,北京：人民交通出版社,1999.9.123-152
[9]赵亦林.乍辆定位与导航系统.电子工业出版社,1998.
[10]致和,王万义译.GPS原理与应用[M].北京：电子工业出版社,2002
[11]Kerr TH. KERR T. Decentralized Filtering and Redundancy Management for Multisensor Navigation. IEEE Transaction on Aerospace and Electronic Systems,1987,23(1):83-119
[12]王惠南.GPS导航原理与应用.北京：科学出版社,2003.
[13]高社生,李华星.INS/SAR组合导航定位技术与应用.西安：西北工业大学出版社2004,270-292
[14]袁建平,罗建军,岳晓奎.卫星导航原理与应用.北京：中国宇航出版社2003：305-306
[15]M.A.P. Taylor. Intelligent Transport Systems:Emerging Technologies and Methods in Transportation anf Traffic. Transportation Research.2004,C(3-4):167-169
[16]M.A.Quddus. High Integrity map matching algorithms for advanced transport Telematics applications. PhD thesis of University of London.2006:29-34
[17]彭飞,智能车辆定位导航系统的研究,北京航空航天大学博士论文,2000
[18]杨东凯,ITS系统分析与自动车辆定位,北京航空航天大学博士论文,2000
[19]王常荣,北京市公交系统汽车定位导航系统的设计.北京航空航天大学硕士论文,2000
[20]柏钢,面向ITS的车辆导航与定位技术的应用,东南大学博士论文,1999
[21]卢大伟,刘炳云,王庆,智能GPS/DR组合导航技术在TRACK-1型车载导航仪中的应用,导航,2000,第3期：108-112
[22]张小国,王庆,万德钧,车载组合导航系统中的分级地图匹配算法,中国惯性技术学报,2000,8(3)：46-51
[23]陈则王,袁信,面向ITS的车辆导航技术与定位技术,交通与计算机,2001,19(6)：23-25
[24]张三同,陆地车辆定位定向导航系统的研制与多传感器综合技术的研究,北京理工大学博士论文,1998
[25]向怀坤,车辆导航系统关键技术研究,北京工业大学博士学位论文,2003,6
[26]徐田来,车载组合导航信息融合算法研究与系统实现,哈尔滨工业大学博士学位论文,2007,12
[27]郑贵省,GPS/DR车载组合定位系统数据融合算法研究,天津大学博士学位论文,2005,5
[28]周勇,智能车辆中的几个关键技术研究,上海交通大学博士学位论文,2007,9
[29]F. Peyret, D. Betaille, G. Hintzy. High-Precision Application of GPS in The field of Real-time Equipment Positioning. Automation in Construction.2000,9:299-314
[30]张裕松.我国北斗导航系统定位原理及应用前景探讨.电子技术参考,2002(1)：6-14
[31]M.Kam, C.Rorres, W.Chang, X.Zhu. Performance and Geometric Interpretation for Decision Fusion with Memory. IEEE Transactions on Systems, Man, and Cybernetic-Part A:Systems and Humans,1999,29(1):52-62
[32]S.Ben-yacoub, Y. Abdeljaoued, E. Mayoraz. Fusion of Face and Speech Data for Person Identity Verification. IEEE Transactions on Neural Networks,1999,10(5):1065-1074
[33]J. Figue, M. Grabisch, M.P. Charbonnel. A Method for Still Image Interpretation Relying on A Multi-Algorithms Fusion Scheme:Application to Human Face Characterization. Fzzy sets and Systems,1999,103(2),317-337
[34]C. Rago, P.Willett, M. Alford. Predetection Fusion:Resolution Cell Grid Effects. IEEE Transactions on Aerospace and Eletronic Systems,1999,35(3):778-789
[35]A.M. Aziz, M. Tummala, R.Cristi. Fuzzy Logic Data Correlation Approach in Multi-sensor-multi-target Tracking Systems. Signal Processing,1999,76(2):195-209
[36]J.A. Fayman, P. Pirjanian, H. Christensen, E. Rivlin. Exploiting Process Integration and Composition in the Context of Active Vision. IEEE Transactions on Systems, Man, and Cybernetic Part C:Applications and Reviews,1999,29(1):73-86
[37]X.Tang. Multiple Competitive Learning Network Fusion for Object Classification. IEEE Transactions on Systems, man,and Cybernetic Part B:Cybernetics,1998,28(4):532-543
[38]高社生,李华星,INS/SAR组合导航定位技术与应用,西安：西北工业大学出版社,2004,270-292
[39]戴亚平,刘征等译,多传感器数据融合理论及其应用,北京：北京理工大学出版 社,2004,2-50
[40]康耀红著,数据融合理论与应用,西安：西安电子科技大学出版社,1997,1-20
[41]高健,DSmT信息融合技术及其在智能车地图创建中的应用,华中科技大学博士论文,2009,5
[42]杨兆升,杨庆芳,冯金巧.路段平均速度组合融合算法及其应用.吉林工业大学学报(工学版),2004,34(4)：675-678
[43]P. C. Lin, H. Komsuoglu, D.E. Koditschek. Sensor data fusion for body state estimation in a hexapod robot with dynamical gaits. IEEE Transactions on Robotics,2006,22(5): 932-943
[44]Q. Yang, J. Sun. An underwater autonomous robot based on multi-sensor data fusion. In Proceeding of the 6th World Congress on Intelligent Control and Automation. Dalin, China: 2006.9139-9143
[45]B. Guo, M.S. Nixon, T.R. Damarla. Acoustic information fusion for ground vehicle classification. In Proceeding of the 11th International Conference on information Fusion. Cologne, Germany:2008.1-7
[46]R. Jurgens, W. Becker. Perception of angular displacement without landmarks:evidence for Bayesian fusion of vestibular, optokinetic, podokinesthetic, and cognitive information. Experimental Brain Research,2006,174(3):528-543
[47]G. Kmberova, R. mandelbaum, M. Mintz. Statistical deeision theory for mobileRobotics: theory and application. In:proeeeding of the 1996 IEEE/SICE/RSJIntemational Conferenceon Multisensor Fusion and hitegration forl ntelligent Systems. Washington, USA:1996.17-24
[48]F. Chausse, S.M. Baek, S. Bonnet, et al. Experimental comparison of EKF and constraint manifold particle filter for robot location. In Proceeding of the 2008 IEEE International Conference on Multi-sensor Fusion and Integration for Intelligent Systems. Seoul, Korea: 2008.399-404
[49]罗荣华.基于粒子滤波器的家庭智能车同时定位与地图创建研究。哈尔滨工业大学博士学位论文,2005,10
[50]Li Maohai, Hong Bingrong, Cai Zesu. Novel indoor mobile robot navigation using monocular vision. Engineering Applications of Artificial Intelligence, Elsevier Science 2008:485-497
[51]R.C. Luo, M.G. Kay. Multisensor integration and fusion in intelligent systems. IEEE Transactions on Systems, Man and Cybernetics,1989,19(5):901-931
[52]A. Noureldin, A. El-Shafie, M. R. Taha. Optimizing neuron-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation. Engineering Applications of Artificial Intelligence,207,20(1):49-61
[53]S. R. Mounce, A. Khan, A.S. Wood, et al. Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system. Information Fusion, 2003,4(3):217-229
[54]M. Wenz, H. Worn, Event-based production rules for data aggregation in wireless sensor networks. In Proceeding of the 2006 IEEE International Coference on Multi-sensor Fusion and Integration for Intelligent Systems. Heidelberg, Germany:2006.59-64
[55]B. Moshiri, A. M. Khalkhali, H.R. Momeni. Designing a home security system using sensor data fusion with DST and DSmT methods. In Proceeding of the 10th international Conference on Information Fusion. Quebec, Canada:2007.1-6
[56]Z. Liang, W. Chen, D. Wang, et al. A technique for damage diagnosis of bridge structure based on information fusion. In:Proceeding of the Nondestructive Evaluation and Health Monitoring of Aerospace Materials, Composities, and Civil Infrastructure IV. San Diego, USA:2005.408-415
[57]LValetGMaurisPBolon, et al. A fuzzy rule-based interactive fusion system for seismic data analysis. Information Fusion,2003,4(2):123-133
[58]王卫华,陈卫东,席裕庚.移动智能车地图创建中的不确定传感信息处理.自动化学报,2003,29(2)：267-274
[59]F. Smarandache, J. Dezert. Advances and Applications of DSmT for information Fusion. Vol.1. Rehoboth, USA:American Research Press,2004
[60]H. Wang. A new multiwavelet-based approach to image fusion. Journal of Mathematical Imaging and Vision,2004,21(2):177-192
[61]Y. Guo, B. wu, C. Luo, et al. Correlation voting fusion strategy for part of speech tagging. In Proceeding of the 8th international conference on Signal processing, Beijing, china.31-34
[62]原新,朱奇丹,兰海.基于粗糙集理论的多传感器信息融合。哈尔滨工业大学学报,2006,38(10)：1669-1672
[63]I. V. Maslov, I. Gertner. Multi-sensor fusion:an evolutionary algorithm approach. Information Fusion,2006,7(3):304-330
[64]N. Xiong, P. Svensson. Multi-sensor management for information fusion:issues and approaches. Information Fusion,2002,3(2):163-186
[65]R.E. Kalman. A New Approach to Linear Filtering and Prediction Problem. Journal of Basic Eng(ASME),1960,82D:95-108
[66]S. Vathsal. Spacecraft Attitude Determination Using a Second-Order Nonlinear Filter. Journal of Guidance, Control, and Dynamics.1987,10(6):559-566
[67]S. J. Julier, J. K. Uhlmann, H. F. Durrant-Whyte. A new Approach for Filtering Nonlinear system. Proceedings of the American Control Confirence, USA,1995:1628-1632
[68]S. J. Julier, J. K. Uhlmann, H. F. Durrant-Whyte. A new method for Nonlinear Transformation of Means and Covariances in Filters and Estimator. IEEE Trans. Automatic Control,2000,45(3):477-283
[69]E. Wan, V. Merwe. The Unscented Kalman Filter for Nonlinear Estimation. Proceedings of Symposium 2000 on Adaptive systems for signal processing, Communication and Control (AS-SPCC). IEEE Lake Louise, Alberta, Canada,2000:1255-1260
[70]N. Bergman, L. Ljung, F. Gustafsson. Terrain Navigation Using Bayesian Statistics. IEEE Control System Magazine,1999,19(3):33-40
[71]H. Carvalho, P. D. Moral. Optimal Nonlinear Filtering in GPS/INS Integration. IEEE Trans on AES,1997,33(3):835-849
[72]P. J. Nordlund, F. Gustafsson. Sequential Monte Carlo Filtering Techniques Applied to Integrated Navigation Systems. Proc. of the American Control Conference, Arlington, Virginia, U.S.A,2001:4375-4380
[73]A. S. Babak. Approximate Nonlinear Filtering with Applications to Navigation, PhD Thesis of Linkoping University,2001:53-71
[74][74] K. Nishiyama. Fast and Effective Generation of the Proposal Distribution for Particle Filters. Signal Processing,2005(12):2412-2417
[75]F. Gustafsson, F. Gunnarsson, N. Bergman. Particle Filters for Positioning, Navigation and Tracking. IEEE Transaction on Signal Processing,2002,50(2):425-436
[76]J. F. Ru, X.R. Li. Interacting Multiple Model Algorithm with Maximum Likelihood Estimation for FDI. International Symposium on Intelligent Control. Houston, Texas,2003: 661-666
[77]X.R. Li, Y. Bar-Shalom. Performance prediction of the IMM algorithms. IEEE Trans, on AES,1993,29(3):755-770
[78]X.R. Li, Y. Bar-Shalom. Model-Set Adaptation in Multiple-model Estimation for Hybrid Systems. Proc.1992 American Control Conference.1992:1794-1799
[79][79] T. Kirubarajan, Y. Bar-Shalom. Kalman Filter versus IMM Estimerator:When Do We Need the Latter? IEEE Transactions on Aerospace and Electronic Systems.2003,39(4): 1452-1456
[80]K. A. Fisher, P. S. Maybeck. Multiple Models Adaptive Estimation with Filter Apawning. IEEE Trans. Aerospace and Electronic Systems.2002,38(3):755-767
[81]王璐,蔡自兴.未知环境中移动智能车并发建图与定位(CML)的研究进展.智能车2004,26(4)：380-384
[82]E. Nobot. Simultaneous Localization and Mapping.2002 Summer School, Australian Centre for Field Robotics. The University of Sydney. July 2002:231-237
[83]Kwang Wee Lee, Sardha Wijesoma, Javier Ibanez Guzman. A Constrained SLAM Approach to Robust and Accurate Localization of Autonomous Ground Vehicles. Robotics and Autonomous Systems.2007:1-14
[84]J. Kim. Autonomous Navigation for Airborne Applications. PhD thesis,2004:67-74
[85]Peter. Stromback. Integrating GPS and INS with a Tightly Coupled Kalman Filter Using Proximity Information for Compensating Signal Loss. PhD Thesis, March 2003:63-70
[86]徐景硕,王维江.容错多传感器组合导航系统发展综述.航空电子技术.2006,37(3)：25-28
[87]M. M. Reda, A. Noureldin, N. El-Sheimy. Improving INS/GPS Positioning Accuracy during GPS Outages Using Fuzzy Logic. Proc.Inst.Nav. GPS/GNSS Conf. Sep., 2003:449-508.
[88]Tucker Balch. Behavior-based Formation Control for Multi-robot Teams. IEEE Transactions on Robotics and Automation.1998,14(6):927-939
[89]Zhiqiang, Cao, Liangjun Xie, Bin Zhang, et al. Formation Constrained Multi-robot system in unknown Environments. Proceeding of the 2003 IEEE International Conference on Robotics & Automation, Taiwan,2003:735-740
[90]Donald D. Dudenhoeffer, Michael P. Jones. A formation Behavior for Large-scale Micro-robot force deployment. Proceeding of the 2000 Winter Simulation Conference,2000:972-982
[91]J. Guo, Z. Lin and M. Cao, "Adaptive leader-follower formation control for autonomous mobile robots", in Proc. of 2010 American Control Conference, Maryland, USA.
[92]Eduardo G.H-M. and Eduardo A. B. Convergence and collision avoidance information control:A survey of the Artificial Potential Functions Approach. Multi-Agent Systems-Modeling, Control, Programming, Simulations and Applications. InTech, April 2011,103-126
[93]DUDEN HOEFFER D D, MICHAEL P J. A formation behavior for large-scale micro-robot force deployment A]. Proceedings of the 2000 Winter Simulation Conference[C]. New York,2000
[94]GE S S, FUA C H, LIM K W. Multi-robot formations:queues and artificial potential trenches[A]. Proceedings of the IEEE International Conference on Robotics and Automation[C]. New Orleans,2004
[95]贾秋玲,闫建国,王新民.基于势函数的多智能车系统的编队控制[J].机器人,2006,28(2)：111-114
[96]LIANG Y, LEE H H. Decentralized formation control and obstacle avoidance for multiple robots with nonholonomic constraints[A]. Proceedings of the 2006 American Control Conference[C]. Minneapolis,2006
[97]OGREN, FIORELL I E, L EONARD N E. Formations with a mission:stable coordination of vehicle group maneuvers[A]. Proceedings of the 15th International Symposium on Mathematical Theory of Networks and Systems[C]. Notre Dame,2002
[98]LEONARD N E, FIORELL I E. Virtual leaders, artificial potentials and coordinated control of groups[A]. Proceedings of the 40th IEEE International Conference on Decision and Control[C]. Orlando,2001
[99]BARAS J S, TAN X B, PEDRAM H. Decentralized control of autonomous vehicles[A]. Proceedings of the 42nd IEEE Conference on Decision and Control[C]. Hawaii,2003
[100]Luca Consolinia, Fabio Morbidib, Domenico Prattichizzob, Mario Tosquesc. Leader-follower formation control of nonholonomic mobile robots with input constraints. Automatica 44(2008) 1343-1349
[101]J. Shao, G. Xie, L. Wang. Leader-following formation control of multiple mobile vehicles. IET Control Theory.2007,1(2):545-552
[102]DUNBAR W B. Model predictive control:Extension to coordinated multi-vehicle formations and real-time implementation[R]. Pasadena:California Institute of Technology, 2001
[103]DUNBAR W B, MURRA Y R M. Model predictive control of coordinated multi-vehicle formations[A]. Proceedings of the IEEE Conference on Decision and Control[C]. Las Vegas,2002
[104]DUNBAR W B, MURRA Y R M. Distributed receding horizon control with application to multi-vehicle formation stabilization[J]. Automatica,2006,42(4):549-558
[105]Kwang-Kyo Oh, Hyo-Sung Ahn. Formation control of mobile agents based on inter-agent distance dynamics. Automatica 47(2011):2306-2312
[106]Hernandez-Martinez, E. & Aranda-Bricaire, E. (2010). Decentralized formation control of multi-agent robots systems based on formation graphs, XIV Latinamerican Congress of Automatic Control and ⅪⅩ ACCA Congress, IFAC, Santiago, Chile, pp.1-6.
[107]D. Gu, "A Differential Game Approach to Formation Control", IEEE Trans. Contr. Syst. Technol, vol.16, no.1, pp.85-93, Jan 2008
[108]Shamsi, F.; Abdollahi, F.; Nikravesh, S.K.Y.; Time varying formation control using feedback information differential game approach. Electrical Engineering (ICEE),2011 19th Iranian Conference on.2011:1-6
[109]Ohko, T., K. Hiraki And Y. Anzai, Addressee learning and message interception for communication load reduction in multi-robot environments. Distributed Artificial Intelligence meets machine Learning.1997, Springer-Verlag, Berlin, pp:248-258
[110]Zhou Pu-Cheng, Han Yu-Sheng and Xue Mo-Gen. Extended Contract Net Protocol for Multi-Robot Dynamic Task Allocation. Information Technology Journal.2007,6 (5):733-738
[111]周浦城,洪炳镕,王月海.动态环境下多机器人合作追捕研究.机器人,2005,27(4),289-295
[112]胡晶晶,曹元大,胡军.基于英式拍卖协商协议的多智能体任务分配.计算机集成制造系统,2006,12(5)：795-799
[113]Zlot, R. and A. Stentz, Market-based multirobot coordination for complex tasks. International Journal of Robotics Research,2006,25:73-101
[114]王月海,洪炳镕.机器人部队运动多目标合作追捕算法[J].西安交通大学学报,2003,37(6)：573-576
[115]Hernandez-Martinez, E. & Aranda-Bricaire, E. (2009). Multi-agent formation control with collision avoidance based on discontinuous vector fields,35th Annual Conference of the IEEE Industrial Electronics Society, IEEE, Porto, Portugal, pp.2283-2288.
[116]Russell Gayle, William Moss, Ming C. Lin, Dinesh Manocha:Multi-robot coordination using generalized social potential fields. ICRA 2009:106-113
[117]蓝艇, 《群体机器人的行为决策和群体智能》,华东理工大学硕士论文,2009年
[118]杨立苹,《基于行为的多机器人追捕问题研究》,哈尔滨工业大学硕士论文,2005年
[119]曹志强,《未知环境下多机器人协调与控制的队形问题研究》,中国科学院自动化研究所博士论文,2002年。
[120]郑贵省,《GPS/DR车载组合定位系统数据融合算法研究》,天津大学博士论文,2005
[121]高健,《DSmT信息融合技术及其在机器人地图创建中的应用》,华中科技大学博士论文,2009
[122]杨易,《智能车辆组合定位与路径导航技术研究》,湖南大学博士论文,2006
[123]徐田来,《车载组合导航信息融合算法研究与系统实现》,哈尔滨工业大学博士论文,2007
[124]曹梦龙,《车载导航系统自主重构技术与信息融合算法研究》,哈尔滨工业大学博士论文,2009
[125]李士明,《基于GPS的车辆定位仿真技术研究》,山东理工大学硕士论文,2009
[126]冀常鹏,《基于多GPS天线智能车辆导航/调度系统的研究》,辽宁工程技术大学硕士论文,2004
[127]祖丽楠,《多机器人系统自主协作控制与强化学习研究》,吉林大学博士论文,2006
[128]王锋辉,《面向区域智能运输的多智能车辆协作研究》,上海交通大学博士论文,2009
[129]黄智,《车载导航系统组合定位技术研究》,湖南大学博士论文,2006
[130]陈凤东,《基于认知地图的移动机器人自主导航技术》,哈尔滨工业大学博士论文,2009
[131]李怡斌,《ITS智能车辆关键技术研究》,天津大学博士论文,2008
[132]石朝侠,《基于多机器人协作的未知环境下路径探索研究》,哈尔滨工业大学博士论文,2007
[133]赵梅,《改进的粒子滤波在组合导航中的应用研究》,北京交通大学硕士论文,2006
[134]王志刚,《车载导航GPS/DR/MM组合定位技术研究》,武汉大学硕士论文,2005
[135]许云斌,《基于车载组合导航系统的信息融合算法研究》,青岛科技大学硕士论文,2009
[136]赵倩,《车载GPS导航系统动态滤波算法应用研究》,江苏大学硕士论文,2009
[137]曹衡,《车载GPS/DR/GIS组合导航系统的研究与设计》,长沙理工大学硕士论文,2008
[138]王文杰,《基于视觉的道路区域和车道线识别技术在智能车辆导航中的应用研究》,山东大学硕士论文,2009
[139]杨宏,李亚安,李国辉,袁润平,《一种改进中心差分卡尔曼滤波方法》,计算机工程与应用,2010.7
[140]闫路平,《多机器人合作追捕目标问题研究》,哈尔滨工业大学硕士论文,2008
[141]周浦城,韩裕生,薛模根,洪炳镕,《基于运动图式的多机器人合作追捕》,计算机工程,2008,
[142]焦吉成,《对匈牙利算法的改进及其应用实例》,山东冶金,2000,