不完善信息融合技术及其在移动机器人中的应用
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摘要
随着传感器、计算机以及信息等技术的迅猛发展,各种具有复杂应用背景的多传感器系统不断涌现,使得信息的获取、处理和融合变得多样化。多源信息融合技术如今已在军事和民用领域都得到了广泛的重视和应用,其理论和方法已经成为智能信息处理和控制的重要研究领域。
迄今为止,学者们针对实际应用提出了许多信息融合算法,其中的证据理论允许人们对不完善信息进行建模及推理,符合人类思维方式,得到了广泛的推崇。最近提出来的Dezert-Smarandache证据理论(DSmT)可以处理几乎所有类型的信息,其优势更是体现在对不确定、不精确以及高冲突信息的处理上,拥有完备的描述方法,是一种优秀的证据推理理论。
本文在简要介绍了Dempster-Shafer Theory (DST)、DSmT和中智理论后,根据信息的特征,对不完善信息进行了归类,并从定量融合和定性融合两个角度对基于证据理论的不完善信息融合技术进行了深入研究,针对不完善信息中的不同种类信息分别提出了相应的融合算法,并对这些算法进行了深入的分析和比较,构建了完整的多源不完善信息融合理论体系,为多源不完善信息融合技术的推广奠定了基础。
本文还对证据理论领域现有的融合框架进行了分析,提出了新的兼容其他融合结构的广义融合框架,并在此基础上构建出了广义证据推理机,定义了广义推理机的工作原理,使其可以推广到其他证据理论中。
随后本文将DSmT信息融合技术应用于移动机器人中,对声纳数据进行了分析,构建出了二维声纳数学模型,将本文提出的融合算法和广义证据推理机应用于机器人的信息融合系统中,同时提出了单体机器人的Multi-Agent系统结构,实现了移动机器人在复杂动态未知环境下的精确地图构建、定位以及自主导航,验证了信息融合技术在机器人技术应用中的有效性,同时也验证了单体Multi-Agent结构的优越性。同时还尝试构建了多机器人系统,初步实现了多机器人联合地图构建,为多移动机器人探测未知动态环境的深入研究奠定了基础。
本文还采用Visual Studio 2008为软件开发平台,结合OpenGL,开发了一套机器人实验及仿真平台,由于其通用性强,操作简便,人机交互界面友好,已成为移动机器人研究的良好工具。
With the rapid development of the sensor, computer and information technology, a variety of applications with complex background of multi-sensor system continue to emerge, making the obtaining, processing and integration of the information more diversified. The multi-source information fusion technology has been given more and more attentions and applied in both military and civilian fields. The theories and methods of multi-source information fusion technology has become the important research area of intelligent information processing and control.
So far, many researchers have brought forward a lot of information fusion algorithms. Among these algorithms, the evidence reasoning theory allows people to accord their thinking mode to model and reason with imperfect information. It has been widely spread. The recently proposed Dezert-Smarandache Theory is an excellent evidence reasoning theory with the capabilities of dealing with nearly all types of information, especially with uncertain, imprecise and high conflicting information.
After briefly introducing the DST (Dempster-Shafer Theory), DSmT and the neutrosophic theory, based on the information characteristics, the imperfect information is classified. The imperfect information technology based on evidence reasoning is studied further with two aspects of quantitative fusion and qualitative fusion. The responsible fusion algorithms for different types of information of the imperfect information are proposed with deeply a analyzing and comparing. The multi-source information fusion technology can be spread well with the complete multi-source information fusion theory system constructed in this article.
After analyzing the existent fusion frame in evidence reasoning field, a new general fusion frame compatible with other fusion structures. And the general evidence reasoning machine is constructed, and it can be extended into other evidence reasoning theories with the definitions of its working principles.
And then the DSmT information fusion technology is applied to mobile robot. The sonar data are analyzed and the planar sonar mathematic model is constructed. The algorithms and general evidence reasoning machine proposed in this paper is applied to the information fusion system of the robot. The multi-agent system architecture of single robot is proposed, too. With these achievements, the mobile robot can build accurate maps, positioning precisely, and autonomously navigating in complex dynamic unknown environment. The results of the experiments verify the validity of information fusion technology in robotics applications and the superiority of the monomer multi-agent contracture. And the multi-robot system is attempted to be construct. The joint multi-robot map building is achieved basically. A solid foundation for multi-robot detecting unknown dynamic environment has been set up.
A mobile robot experiment and emulation platform is developed with Visual Studio 2008 and OpenGL. It has become an excellent tool for mobile robot research with the advantages of versatility, easy operation and friendly Friendly interface.
迄今为止,学者们针对实际应用提出了许多信息融合算法,其中的证据理论允许人们对不完善信息进行建模及推理,符合人类思维方式,得到了广泛的推崇。最近提出来的Dezert-Smarandache证据理论(DSmT)可以处理几乎所有类型的信息,其优势更是体现在对不确定、不精确以及高冲突信息的处理上,拥有完备的描述方法,是一种优秀的证据推理理论。
本文在简要介绍了Dempster-Shafer Theory (DST)、DSmT和中智理论后,根据信息的特征,对不完善信息进行了归类,并从定量融合和定性融合两个角度对基于证据理论的不完善信息融合技术进行了深入研究,针对不完善信息中的不同种类信息分别提出了相应的融合算法,并对这些算法进行了深入的分析和比较,构建了完整的多源不完善信息融合理论体系,为多源不完善信息融合技术的推广奠定了基础。
本文还对证据理论领域现有的融合框架进行了分析,提出了新的兼容其他融合结构的广义融合框架,并在此基础上构建出了广义证据推理机,定义了广义推理机的工作原理,使其可以推广到其他证据理论中。
随后本文将DSmT信息融合技术应用于移动机器人中,对声纳数据进行了分析,构建出了二维声纳数学模型,将本文提出的融合算法和广义证据推理机应用于机器人的信息融合系统中,同时提出了单体机器人的Multi-Agent系统结构,实现了移动机器人在复杂动态未知环境下的精确地图构建、定位以及自主导航,验证了信息融合技术在机器人技术应用中的有效性,同时也验证了单体Multi-Agent结构的优越性。同时还尝试构建了多机器人系统,初步实现了多机器人联合地图构建,为多移动机器人探测未知动态环境的深入研究奠定了基础。
本文还采用Visual Studio 2008为软件开发平台,结合OpenGL,开发了一套机器人实验及仿真平台,由于其通用性强,操作简便,人机交互界面友好,已成为移动机器人研究的良好工具。
With the rapid development of the sensor, computer and information technology, a variety of applications with complex background of multi-sensor system continue to emerge, making the obtaining, processing and integration of the information more diversified. The multi-source information fusion technology has been given more and more attentions and applied in both military and civilian fields. The theories and methods of multi-source information fusion technology has become the important research area of intelligent information processing and control.
So far, many researchers have brought forward a lot of information fusion algorithms. Among these algorithms, the evidence reasoning theory allows people to accord their thinking mode to model and reason with imperfect information. It has been widely spread. The recently proposed Dezert-Smarandache Theory is an excellent evidence reasoning theory with the capabilities of dealing with nearly all types of information, especially with uncertain, imprecise and high conflicting information.
After briefly introducing the DST (Dempster-Shafer Theory), DSmT and the neutrosophic theory, based on the information characteristics, the imperfect information is classified. The imperfect information technology based on evidence reasoning is studied further with two aspects of quantitative fusion and qualitative fusion. The responsible fusion algorithms for different types of information of the imperfect information are proposed with deeply a analyzing and comparing. The multi-source information fusion technology can be spread well with the complete multi-source information fusion theory system constructed in this article.
After analyzing the existent fusion frame in evidence reasoning field, a new general fusion frame compatible with other fusion structures. And the general evidence reasoning machine is constructed, and it can be extended into other evidence reasoning theories with the definitions of its working principles.
And then the DSmT information fusion technology is applied to mobile robot. The sonar data are analyzed and the planar sonar mathematic model is constructed. The algorithms and general evidence reasoning machine proposed in this paper is applied to the information fusion system of the robot. The multi-agent system architecture of single robot is proposed, too. With these achievements, the mobile robot can build accurate maps, positioning precisely, and autonomously navigating in complex dynamic unknown environment. The results of the experiments verify the validity of information fusion technology in robotics applications and the superiority of the monomer multi-agent contracture. And the multi-robot system is attempted to be construct. The joint multi-robot map building is achieved basically. A solid foundation for multi-robot detecting unknown dynamic environment has been set up.
A mobile robot experiment and emulation platform is developed with Visual Studio 2008 and OpenGL. It has become an excellent tool for mobile robot research with the advantages of versatility, easy operation and friendly Friendly interface.
引文
[1]W. Burgard, D. Fox, D. Hennig, et al. Estimating the absolute position of a mobile robot using position probability grids. Procceeding of The National Conference on Artificial Intelligence. Oregon,1996:p.896-8901.
[2]U. S. DEPARTMENT OF DEFENSE. Data fusion lexicon. Published by Data Fusion Subpanel of the Joint Directors of Laboratories. ecnichal Panel for C3 (F.E. White, Code 4202, NOSC, San Diego, CA),1991.
[3]DSTO (Defence Science and Technology Organization) Data Fusion Special Interest Group, Data fusion lexicon. Department of Defence, Australia,7 p.,21 September.1994.
[4]E.L. Waltz, J. Llinas. Multisensor Data Fusion. Artech House, Inc. Norwood, MA, USA,1990.
[5]L. Wald. An European Proposal for Terms of Reference in Data Fusion. International Archives of Photogrammetry and Remote Sensing,1998, ⅩⅩⅫ(7):p.651-654.
[6]B. Solaiman, R. Debon, C. Roux. Informationfusion:application to data and model fusion forultrasound image segmentation. Special Issue ofIEEE-Transaction on biomedical Engineering on the"Biomedical Data Fusion" topic,1999,46(10):p.1171-1175.
[7]D.L. Hall, J. Llinas. An Introduction to Multisensor Fusion. Proceedings of the IEEE,1997,85(1): p.6-23.
[8]L. Valet, G. Mauris, P. Bolon. A statistical overview of recent literature in information fusion. IEEE AESS Systems Magazine,2001:p.7-14.
[9]F. Mastrogiovanni, A. Sgorbissa, R. Zaccaria. A Distributed Architecture for Symbolic Data Fusion. Proceedings of the 20th international joint conference on Artifical intelligence. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA,2007,7:p.2153-2158.
[10]A. Jalobeanu, J.A. Gutierrez. Multisource data fusion for bandlimited signals:a Bayesian perspective. AIP Conference Proceedings. Paris, France,2006,1:p.391-400.
[11]S. Challa, T. Gulrez, Z. Chaczko, et al. Opportunistic information fusion:a new paradigm for next eneration networked sensing systems. Proceedings of the 8th International Conference on Information Fusion,2005,1:p.8.
[12]B. Bell, E. Santos, S.M. Brown. MakingAdversary Decision Modeling Tractable with Intent Inference andInformation Fusion. Proceedings of the 11th Conference on ComputerGenerated Forces and Behavioral Representation,2002:p.535-542.
[13]B.V. Dasarathy. Information Fusion-what, where, why, when, and how?. Information Fusion, 2001,2:p.75-76.
[14]I. Bloch, A. Hunter, A. Appriou, et al. Fusion:General Concepts and Characteristics. International Journal of Intelligent Systems,2001,16:p.1107-1134.
[15]Y.B. Shalom, E. Tse. Tracking in a cluttered environment with probabilistic data association. Automatica,1975,11:p.451-460.
[16]D.L. Hall. Mathematical Techniques in Multisensor Data Fusion. Boston, London:Artech House. 1992.
[17]B.J. Black. Modeling organization configuration and decision processes for information. Monterrey. CA:Naval postgraduate school publishing corporation,1997:p.102-145.
[18]M. Collier. Information Warfare Modeling I. San Antonio, TX:Southwest Research Inst Publishing,1996.1996.
[19]D.E. Elam. Attacking the infrastructure:Exploring potential uses of offensive information warfare. CA:Naval postgraduate school publishing corporation,1996:p.3-9.
[20]D.J. Dishong. Studying the Effect of Information Warfare on C2 Decision Making. Monterey, CA:Naval postgraduate school publishing corporation,1996:p.44-48.
[21]R.J. Wood. Information Engineering the Foundation of Information Warfare. Air Force Coll. Maxwell AFB, AL.,1995.
[22]潘泉,于昕,程咏梅等.信息融合理论的基本方法与进展.自动化学报,2003,29(4)：599-615.
[23]L.J. De Vin, A.H.C. Ng, M. Sundberg, et al. Information fusion for decision support in manufacturing:studies from the defense sector. International Journal of Advanced Manufacturing Technology,2008,35(9):p.908-915.
[24]R. Datta, D. Joshi, J. Li, et al. Image Retrieval Ideas, Influences and Trends of the New Age. ACM Computing Surveys,2008,40.
[25]Z. Jia, A. Balasuriya, S. Challa. Sensor fusion-based visual target tracking for autonomous vehicles. Artificial Life and Robotics. Artificial Life and Robotics,2008,12(1-2):p.317-328.
[26]高方君.C3I多传感器信息融合系统.火力与指挥控制,2008,33(4)：1 17-119.
[27]Y. Bar-Shalom, S.S. Blackman, R.J. Fitzgerald. Hierarchical track association and fusion for a networked surveillance system. IEEE Transactions on Aerospace and Electronic Systems,2007, 43(1):p.392-400.
[28]H.A.P. Blom, E.A. Bloem. Bayesian tracking of two possibly unresolved maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems,2007,43(2):p.612—627.
[29]P.H. Foo, G.W. Ng, K.H. Ng, et al. Application of intent inference for surveillance and conformance
monitoring to aid human cognition. Proceedings of the 10th International Conference on Information Fusion. Quebec, Canada,2007:p.1-8.
[30]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,2007,20(1):p.49-61.
[31]P. Smets, B. Ristic. Kalman filter and joint tracking and classification based on belief functions in the TBM framework. Information Fusion, Special Issue on the Seventh International Conference on Information Fusion, Part Ⅱ,2007,8(1):p.16-27.
[32]A.K. Auβling, F.E. Schneider, D. Wildermuth, et al. A switching algorithm for tracking extended targets. Informatics in Control Automation and Robotics,2007,2:p.117-128.
[33]C. Wang, B. Hu, P. Li. Research on Intelligently Knowledge Integration Model of Industry Cluster in China Based on Information Fusion. Proceeding of 4th International Conference on Wireless communications, Networking and Mobile Computing. Dalian, China,2008:p.1-4.
[34]杨露菁,余华.多源信息融合理论与应用.北京邮电大学出版社.2006.
[35]刘同明,夏祖勋,解洪成.数据融合技术及其应用.北京：国防工业出版社.1998.
[36]康耀红.数据融合理论与应用.西安：西安电子科技大学出版社.1997.
[37]D.L. Hall, J. Llinas. An Introduction to Multisensor Data Fusion. Proc. of the IEEE,1997,85(1):p. 6-23.
[38]任彦.多传感器信息融合技术研究(博士学位论文).哈尔滨：哈尔滨工程大学.2004.
[39]M. Bedworth, J. O'Brien. The omnibus model:A new model of data fusion?. IEEE Aerospace and Electronic Systems Magazine,2000,15(4):p.30～36.
[40]C.B. Frankel, M.D. Bedworth. Control, estimation and abstraction in fusion architectures:lessons from human information processing. Proceeding of the 3rd International Conference on Information Fusion. Paris, France,2000,1:p. MOC5/3- MOC510 vol.1.
[41]何友,关欣,王国宏.多传感器信息融合研究进展与展望.宇航学报,2005,26(4)：524-530.
[42]P. Hannah, A. Starr, A. Ball. Decisions in condition monitoring —An examplar for data fusion architecture. Proceedings of 2000 International Conference on Information Fusion. France, Paris, 2000,1:p. MOD5/23-MOD5/30 vol.1.
[43]J. Esteban, A. Starr, R. Willetts, et al. A Review of data fusion models and architectures:towards engineering guidelines. Neural Computing and Applications,2005,14(273-281).
[44]L.C. Luo, M.G. Kay. Multisensor integration and fusion for intelligent machines and systems. US: Abbex Publishing Corporation,1995:p.321-456.
[45]B.V. Dasarathy. Multi-sensor, multi-source information fusion:architecture, algorithms, and applications-a panoramic overview. Proceeding of the 2nd IEEE International Conference on Computational Cybernetics. Vienna, Austria,2004:p.5-5.
[46]E.F. Nakamura, A.A.F. Loureiro, A.C. Frery. Information fusion for wireless sensor networks: Methods, models, and classifications. ACM Computing Surveys,2007,39(3).
[47]E. Shahabzian, D. Blodgett, P. Labbe. The extended OODA model for data fusion systems. Proceedings of 2001 International Conference on Information Fusion. Canada,2001:p.19-25.
[48]X. Huang, M. Wang. Multi-sensor data fusion structures in autonomous systems:a review. Proceeding of the 2003 IEEE International Symposium on Intelligent Control. Houston, USA, 2003,817-821.
[49]L.A. Klein. Sensor and Data Fusion Concepts and Applications,2nd edition. Society of Photo-Optical Instrumentation Engineers (SPIE).1999.
[50]K.D. SOMMER, O. KUEHN, F.P. LEON, et al. A bayesian approach to information fusion for evaluating the measurement uncertainty. Robotics and Autonomous Systems,2009,57:p. 339-344.
[51]B. Guo, M.S. Nixon, T.R. Damarla. Acoustic information fusion for ground vehicle classification. Proceeding of the 11th International Conference on Information Fusion. Cologne, Germany,2008: p.1-7.
[52]F. Johansson, G. Falkman. A Bayesian network approach to threat evaluation with application to an air defense scenario. Proceedings of 2008 11th International Conference on Information Fusion. Cologne,2008:p.1-7.
[53]F. Johansson, G. Falkman. Detection of vessel anomalies-a Bayesian network approach. Proceedings of the 3rd International Conference on Intelligent Sensors, Sensor Networks and Information Processing. Melbourne, Qld.,2007:p.395-400.
[54]高青.多传感器数据融合算法研究(博士学位论文).西安：西安电子科技大学,2008.
[55]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):p.932-943.
[56]B. Moshiri, A.M. Khalkhali, H.R. Momeni. Designing a home security system using sensor data fusion with DST and DSmT methods. Proceeding of the 10th International Conference on Information Fusion. Quebec, Canada,2007:p.1-6.
[57]G. Shafer. A Mathematical Theory of EvidencePrinceton, USA:Princeton University Press.1976.
[58]F. Smarandache, J. Dezert. An introduction to the theory of plausible and paradoxical reasoning. Lecture Notes In Computer Science,2003,2542:p.12-23.
[59]R.T. Anderson, G. Chowdhary, E.N. Johnson. Comparison of RBF and SHL Neural Network Based Adaptive Control. Journal of Intelligent & Robotic Systems,2009,54(1-3):p.183-199.
[60]F.J. Lin, P.H. Chou. Adaptive Control of Two-Axis Motion Control System Using Interval Type-2 Fuzzy Neural Network. IEEE Transactions on Industrial Electronics,2009,56(1):p.178-193.
[61]H. Lee, S. Hong, E. Kim. A new genetic feature selection with neural network ensemble. International Journal of Computer Mathematics,2009,86(7):p.1105-1117.
[62]F. Altiparmak, B. Dengiz, A.E. Smith. A General Neural Network Model for Estimating Telecommunications Network Reliability. IEEE Transactions on Reliability,2009,58(1):p.2-9.
[63]L.A. Zadeh. Fuzzy sets. Inform. Contr.,1965,8:p.338-353.
[64]C.S. Chiu, K.Y. Lian. Hybrid Fuzzy Model-Based Control of Nonholonomic Systems:A Unified Viewpoint. IEEE Transactions on Fuzzy Systems,2008,16(1):p.85-96.
[65]H. Gao, T. Chen. Stabilization of Nonlinear Systems Under Variable Sampling:A Fuzzy Control Approach. IEEE Transactions on Fuzzy Systems,2007,15(5):p.972-983.
[66]X. Chen. Real Wavelet Transform-Based Phase Information Extraction Method:Theory and Demonstrations. IEEE Transactions on Industrial Electronics,2009,56(3):p.891-899.
[67]S.L. Linfoot. Wavelet families for orthogonal wavelet division multiplex. Advances in Computational Mathematics,2008,44(18):p.1101-1102.
[68]K.M. Holt, D.L. Neuhoff. Deterministic Annealing for Entropy-Constrained Vector Quantizer Design. IEEE Transactions on Information Theory,2008,54(9):p.4305-4323.
[69]E. Learned-Miller, J. DeStefano. A Probabilistic Upper Bound on Differential Entropy. IEEE Transactions on Information Theory,2008,54(11):p.5223-5230.
[70]F. Melgani, Y. Bazi. Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization. IEEE transactions on information technology in biomedicine, 2008,12(5):p.667-677.
[71]V.I. Norkin, M.A. Keyzer. Asymptotic efficiency of kernel support vector machines (SVM). Cybernetics and Systems Analysis,2009,45(4):p.575-588.
[72]V.K. Finn. The Synthesis of Cognitive Procedures and the Problem of Induction. Automatic Documentation and Mathematical Linguistics,2009,43(3):p.149-195.
[73]Applying category theory to improve the performance of a neural architecture. Neurocomputing, 2009,72(13-15):p.3158-3173.
[74]K. Fu, L. Zhang. Strong limit theorems for random sets and fuzzy random sets with slowly varying weights. Information Sciences,2008,178(12):p.2648-2660.
[75]D.A. Alvarez. Nonspecificity for infinite random sets of indexable type. Fuzzy Sets and Systems, 2008,159(3):p.289-306.
[76]R.A.M. PEREIRA, R.A. RIBEIRO, P. SERRA. Rule Correlation And Choquet Integration In Fuzzy Inference Systems. International Journal of Uncertainty, Fuzziness, and Knowledge-based Systems,2008,16(5):p.601-626.
[77]A. Rico, O. Strauss, D. Mariano, et al. Choquet Integrals As Projection Operators For Quantified Tomographic Reconstruction. Fuzzy Sets and Systems,2009,160(2):p.198-211.
[78]E. Giuli, W. Tholen. A Topologist's View of Chu Spaces. Applied Categorical Structures,2007, 15(5-6):p.573-598.
[79]吕漫丽,孙灵芳.多传感器信息融合技术.自动化技术与应用,2008,27(2)：79-82.
[80]陈文辉,马铁华.多传感器信息融合技术的研究与进展.科技情报开发与经济,2006,16(19)：212-213.
[81]赵蕊,贺建军.多传感器融合技术.计算机测量与控制,2007,15(9)：1124-1126,1134.
[82]N.J. Nilsson. A mobius automation:an application of artificial intelligence techniques. Proceedings of the 1st international Joint Conference on Artificial Intelligence. Washington, DC, 1969:p.509-520.
[83]R.S. Mosher. Test and evaluation of a versatile walk- ing truck. Proceedings of Off-Road Mobility Sym-posium. Washington, DC,1968:p.359-378.
[84]徐国保,尹怡欣,周美娟.智能移动机器人技术现状及展望.机器人技术与应用,2007(2)：29-34.
[85]迟建男,徐心和.移动机器人即时定位与地图创建问题研究.机器人,2004,26(1)：92-96.
[86]罗荣华,洪炳榕.移动机器人同时定位与地图创建研究进展.机器人,2004,26(2)：182-186.
[87]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.控制理论与应用,2005,22(3)：455-460.
[88]王璐,蔡自兴.未知环境下移动机器人并发建图与定位的研究进展.机器人,2004,26(4)：380-384.
[89]王耀南,余洪山.未知环境下移动机器人同步地图创建与定位研究进展.控制理论与应用,2008,25(1)：57-65.
[90]蔡自兴,肖正,于金霞.动态环境中移动机器人地图构建的研究进展.控制工程,2007,14(3)：231-235.
[91]S. Noykov, C. Roumenin. Occupancy grids building by sonar and mobile robot. Robotics and Autonomous Systems,2006,55(2):p.162-175.
[92]L. Yenilmez, H. Temeltas. A new approach to map building by sensor data fusion:sequential principal component-SPC method. The International Journal of Advanced Manufacturing Technology,2007,34:p.168-178.
[93]G. Grisetti, G.D. Tipaldi, C. Stachniss, et al. Fast and accurate SLAM with Rao-Blackwellized particle filters. Robotics and Autonomous Systems,2007,55(1):p.30-38.
[94]X. Zhang, A. Rad, Y. Wong. A robust regression model for simultaneous localization and mapping in autonomous mobile robot. Journal of Intelligent and Robotic Systems,2008,53(2):p. 183-202.
[95]梁志伟,马旭东,戴先中等.基于分布式感知的移动机器人同时定位与地图创建.机器人,2009,31(1)：33-39.
[96]李新德,黄心汉,王敏.基于经典DSmT的Sonar栅格地图创建.计算机应用研究,2007,24(3)：209-212.
[97]A. Elfes, H. Moravec. High resolution maps from wide angle sonar. Proc of the IEEE Int Conf on Robotics and Automation. St. Louis MO,1985:p.116-121.
[98]N.C. Mitsou, C.S. Tzafestas. Temporal occupancy grid for mobile robot dynamic environment mapping. Proceeding of the 2007 Mediterranean Conference on Control and Automation. Athens, Greece,2007:p.1-8.
[99]D. Rodriguez-Losada, F. Matia, R. Galan. Building geometric feature based maps for indoor service robots. Robotics and Autonomous Systems,2006,54(7):p.546-558.
[100]T. Kwon, J. Song. Real-time building of a thinning-based topological map. Intelligent Service Robotics,2008,1(3):p.211-220.
[101]J.L. Blanco, J. Gonzalez, J.A. Fernandez-Madrigal. Fernandez-Madrigal. Consistent observation grouping for generating metric-topological maps that improves robot localization. Proceeding of the 2006 IEEE International Conference on Robotics and Automation. Orlando, USA,2006:p. 818-823.
[102]J.L. Blanco, J. Gonzalez, J.A. Fernandez-Madrigal. Subjective local maps for hybrid metric-topological SLAM. Robotics and Autonomous Systems,2009,57(1):p.64-74.
[103]Yong-Ju Lee, Byung-Doo Yim, Jae-Bok Song. Mobile Robot Localization based on Effective Combination of Vision and Range Sensors. International Journal of Control, Automation, and Systems,2009,7(1):p.97-104.
[104]B. Choi, J. Lee. Localization of a mobile robot based on an ultrasonic sensor using dynamic obstacles. Artificial life and robotics,2008,12(1-2):p.280-283.
[105]J. Borenstein, H.R. Everett, L. Feng, et al. Mobile Robot Positioning Sensors and Techniques. Journal of Robotic Systems,1997,14(4):p.231-249.
[106]X. Zeng, X. Huang, M. Wang, et al. The depth information estimation of microscope defocus image based-on Markov random field. Proceeding of 2008 IEEE Conference on Robotics, Automation and Mechatronics. Chengdu, China,2008:p.999-1004.
[107]M.W.M.G. Dissanayake, P. Newman, S. Clark, et al. A solution to the simultaneous localisation and map building (SLAM) problem. IEEE Trans on Robotics and Automation,2001,17(7):p. 229-241.
[108]J.E. Guivant, E.M. Nebot. Optimization of the Simultaneous Localization and Map Building Algorithm for Real Time Implementation. IEEE Transactions on Robotics and Automation,2001, 17(3):p.242-257.
[109]K. Demirli, M. Khoshnejad. Autonomous parallel parking of a car-like mobile robot by a neuro-fuzzy sensor-based controller. Fuzzy Sets and Systems,2009,160(19):p.2876-2891.
[110]B.A. Merhy, P. Payeur, E.M. Petriu. Application of Segmented 2-D Probabilistic Occupancy Maps for Robot Sensing and Navigation. IEEE Transactions on Instrumentation and Measurement, 2008,57(12):p.2827-2837.
[111]L. X., H. X., W. M., et al. A fusion Machine based on DSmT and PCR5 for robot's map reconstruction. International Journal of Information Acquisition,2006,3(3):p.1-11.
[112]蔡自兴,陈白帆,王璐等.异质多移动机器人协同技术研究的进展.智能系统学报,2007,2(3)：1-7.
[113]S.I. Roumeliotis, G.A. Bekey. Dist ributed multi-robot localization. IEEE Transactions on Robotics and Automation,2002,18(5):p.781-795.
[114]I. Rekleitis, G. Dudek, E. Milios. Probabilistic cooperative localization and mapping in practice. Proceeding of IEEE International Conference on Robotics and Automation, Taipei, China,2003, 2:p.1907-1912.
[115]P. Quintero-Alvarez, G. Ramirez, S. Zeghloul. A collision-free path-planning method for an articulated mobile robot. Applied bionics and biomechanics,2007,4(2):p.71-81.
[116]J.C. Latombe. Robot Motion Planning.. Boston, MA:Kluwer.1991.
[117]Pere. Automatic Planning of Manipulator Movements. IEEE Trans on System Man and Cybemetics,1981,11(11):p.681-698.
[118]R.A. Brooks. Solving the findpath problem by good representation of free space. IEEE Trans. on Sys. Mart. and Cybern,1983,13(3):p.190-197.
[119]M.K. Habib. Real Time Mapping and Dynamic Navigation for Mobile Robots. International Journal of Advanced Robotic Systems,2007,4(3):p.323-338.
[120]X. Sun, S. Koenig. The Fringe-Saving A* Search Algorithm-A Feasibility Study. Proceedings of the International Joint Conference on Artificial Intelligence. Hyderabad, India,2007:p. 2391-2397.
[121]A. Stentz. The focussed D* algorithm for realtime replanning. Proceedings of the Joint Conference on Artificial Intelligence,1995.
[122]L. Huang. Velocity planning for a mobile robot to track a moving target-a potential field approach. Robotics and Autonomous Systems,2009,57(1):p.55-63.
[123]O. Khatib. Real-Time Obstacle Avoidance for Manipulators and Mobile Robots. The International Journal of Robotics Research,1986,5(1):p.90-98.
[124]J.H. Holland. Genetic algorithm and the optimal allocations of trials. SIAM Journal of Computing, 1973,2(2):p.88-105.
[125]丁伟,孙华,曾建辉.基于多传感器信息融合的移动机器人导航综述.传感器与微系统,2006,25(7)：1-3.
[126]G. Bauzil, M. Briot, P. Ribes. A navigation sub2system using ultra-sonic sensors for the mobile robot Hilare.1st Int Conf. on Robot Vision and Sensory Control. Stanford-upon-Avon,U K,1981: p.47-58.
[127]李磊,曹志强,侯增广等.基于行为的轮式移动机器人导航控制.控制与决策,2004,19(6)：707-710.
[128]张永谦,王挺,安成万等.复合机构移动机器人红外和超声阵列信息处理方法.计算机工程与应用,2005,41(7)：34-36.
[129]J. Dezert, F. Smarandache. Advances and Applications of DSmT for Information Fusion Vol.1. Rehoboth:American Research Press.2004.
[130]J. Dezert, F. Smarandache. On the generation of hyper-powersets for the DSmT. Proceeding of the 6th International Conference on Information Fusion. Cairns, Australia,2003:p.1118-1125.
[131]T. M., I. A., T. T. On logical method for counting Dedekind numbers. Lect. Notes on Comp.Sci., 2001,2138:p.424-427.
[132]J. Dezert, F. Smarandache. Advances and Applications of DSmT for Information Fusion. Vol.2. Rehoboth:American Research Press.2006.
[133]L. Zadeh. A simple view of the Dempster-Shafer theory of evidence and its implications for the rule of combination. Berkeley Cognitive Science Report No.33, University of California, Berkeley, CA,1985.
[134]L. Zadeh. Review of Mathematical theory of evidence. AI Magazine,1984,5(3):p.81-83.
[135]L. Zadeh. On the validity of Dempster's rule of combination. Memo M 79/24, Univ. of California, Berkeley.1979.
[136]C.K. Murphy. Combining belief functions when evidence conflicts, Decision Support Systems, Elsevier Publisher. Decision Support Systems,2000,29:p.1-9.
[137]L. Zadeh. A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination. AI Magazine,1986,7(2):p.85-90.
[138]J. Dezert, F. Smarandache. Advances and Applications of DSmT for Information Fusion. Vol.3. Rehoboth:American Research Press.2008.
[139]F. Smarandache. A Unifying Field in Logics:Neutrosophic Logic, Neutrosophy, Neutrosophic Set, Neutrosophic Probability.3rd ed. Rehoboth, USA:American Research Press.2003.
[140]J. Dezert, F. Smarandache. Introduction to the fusion of quantitative and qualitative beliefs. Information & Security an International Journal,2006,20(1):p.9-49.
[141]X. Huang, P. Li, M. Wang. Evidence Reasoning Machine Based on DSmT for Mobile Robot SLAM in Unknown Dynamic Environment. Proceeding of 2009 IEEE International Conference on Robotics and Biomimetics. Guilin, China,2009.
[142]李鹏,黄心汉,王敏.基于混合DSm模型的移动机器人地图构建.华中科技大学学报(自然科学版),2008,36(Sup.Ⅰ)：174-176.
[143]P. Li, X. Huang, M. Wang. Robot Map Building in Unknown Dynamic Environment based on Hybrid Dezert-Smarandache Model. Information Technology Journal,2009,8(3):p.284-292.
[144]王卫华.未知环境中移动机器人创建地图的研究.[博士学位论文].上海：上海交通大学图书馆,2003.
[145]李鹏,黄心汉,王敏.基于混合DSm模型的移动机器人动态环境地图构建.机器人,2009,31(1)：40-46,52.
[146]P. Li, X. Huang, M. Wang. Multiple Mobile Robots Map Building Based on DSmT. Proceeding of 2008 IEEE Conference on Robotics, Automation and Mechatronics. Chengdu, China,2008:p. 509-514.
[147]李鹏,黄心汉,王敏.基于DSm混合模型的多机器人信息融合及地图构建.计算机研究与发展,2009,46(1)：70-76.
[148]L. Foner. Yenta:A Multi-Agent, Referral-based, Matchmaking System. Proceeding of the First International,Conference on Autonomous Agent,1997.
[149]Chia Hsun Chiang, Po Jui Chiang, Fei, J.C.-C., et al. A comparative study of implementing Fast Marching Method and A* SEARCH for mobile robot path planning in grid environment:Effect of map resolution. Proc. IEEE Workshop Adv. Rob. Soc. Impacts, ARSO. Hsinchu, Taiwan,2007:p. 1-6.
[150]P. Li, X. Huang, M. Wang. A hybrid method for dynamic local path planning. Procceding of International Conference on Networks Security, Wireless Communications and Trusted Computing. Wuhan, China,2009,1:p.317-320.
[151]李新德.多源不完善信息融合方法及其应用研究.[博士学位论文].武汉：华中科技大学图书馆,2007.
[152]X. Zeng, X. Huang, P. Li. The depth information estimation of microscope defocus image based-on Markov random field.2008 IEEE International Conferences on Robotics, Automation & Mechatronics. Chengdu, China:2008.
[153]B. Hu, C. Wang, P. Li. Research on Intelligently Knowledge Integration Model of Industry Cluster in China Based on Information Fusion.2008 International Conference on Wireless Communications, Networking and Mobile Computing, Dalian, China:2008.
[154]B. Hu, C. Wang, P. Li. Empirical Study of Knowledge Fusion Process within Chinese High-Tech Industry Clusters Based on Information Fusion Method. Journal of Information & Knowledge Management,2009,8(4):353-361.
[155]李鹏,黄心汉,王敏.DSmT框架下的自适应通用比例分配法则.计算机工程与应用,2010,46(6)：16-18.
[156]高健.DSmT信息融合技术及其在机器人地图创建中的应用.[博士学位论文].武汉：华中科技大学图书馆,2009.
[2]U. S. DEPARTMENT OF DEFENSE. Data fusion lexicon. Published by Data Fusion Subpanel of the Joint Directors of Laboratories. ecnichal Panel for C3 (F.E. White, Code 4202, NOSC, San Diego, CA),1991.
[3]DSTO (Defence Science and Technology Organization) Data Fusion Special Interest Group, Data fusion lexicon. Department of Defence, Australia,7 p.,21 September.1994.
[4]E.L. Waltz, J. Llinas. Multisensor Data Fusion. Artech House, Inc. Norwood, MA, USA,1990.
[5]L. Wald. An European Proposal for Terms of Reference in Data Fusion. International Archives of Photogrammetry and Remote Sensing,1998, ⅩⅩⅫ(7):p.651-654.
[6]B. Solaiman, R. Debon, C. Roux. Informationfusion:application to data and model fusion forultrasound image segmentation. Special Issue ofIEEE-Transaction on biomedical Engineering on the"Biomedical Data Fusion" topic,1999,46(10):p.1171-1175.
[7]D.L. Hall, J. Llinas. An Introduction to Multisensor Fusion. Proceedings of the IEEE,1997,85(1): p.6-23.
[8]L. Valet, G. Mauris, P. Bolon. A statistical overview of recent literature in information fusion. IEEE AESS Systems Magazine,2001:p.7-14.
[9]F. Mastrogiovanni, A. Sgorbissa, R. Zaccaria. A Distributed Architecture for Symbolic Data Fusion. Proceedings of the 20th international joint conference on Artifical intelligence. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA,2007,7:p.2153-2158.
[10]A. Jalobeanu, J.A. Gutierrez. Multisource data fusion for bandlimited signals:a Bayesian perspective. AIP Conference Proceedings. Paris, France,2006,1:p.391-400.
[11]S. Challa, T. Gulrez, Z. Chaczko, et al. Opportunistic information fusion:a new paradigm for next eneration networked sensing systems. Proceedings of the 8th International Conference on Information Fusion,2005,1:p.8.
[12]B. Bell, E. Santos, S.M. Brown. MakingAdversary Decision Modeling Tractable with Intent Inference andInformation Fusion. Proceedings of the 11th Conference on ComputerGenerated Forces and Behavioral Representation,2002:p.535-542.
[13]B.V. Dasarathy. Information Fusion-what, where, why, when, and how?. Information Fusion, 2001,2:p.75-76.
[14]I. Bloch, A. Hunter, A. Appriou, et al. Fusion:General Concepts and Characteristics. International Journal of Intelligent Systems,2001,16:p.1107-1134.
[15]Y.B. Shalom, E. Tse. Tracking in a cluttered environment with probabilistic data association. Automatica,1975,11:p.451-460.
[16]D.L. Hall. Mathematical Techniques in Multisensor Data Fusion. Boston, London:Artech House. 1992.
[17]B.J. Black. Modeling organization configuration and decision processes for information. Monterrey. CA:Naval postgraduate school publishing corporation,1997:p.102-145.
[18]M. Collier. Information Warfare Modeling I. San Antonio, TX:Southwest Research Inst Publishing,1996.1996.
[19]D.E. Elam. Attacking the infrastructure:Exploring potential uses of offensive information warfare. CA:Naval postgraduate school publishing corporation,1996:p.3-9.
[20]D.J. Dishong. Studying the Effect of Information Warfare on C2 Decision Making. Monterey, CA:Naval postgraduate school publishing corporation,1996:p.44-48.
[21]R.J. Wood. Information Engineering the Foundation of Information Warfare. Air Force Coll. Maxwell AFB, AL.,1995.
[22]潘泉,于昕,程咏梅等.信息融合理论的基本方法与进展.自动化学报,2003,29(4)：599-615.
[23]L.J. De Vin, A.H.C. Ng, M. Sundberg, et al. Information fusion for decision support in manufacturing:studies from the defense sector. International Journal of Advanced Manufacturing Technology,2008,35(9):p.908-915.
[24]R. Datta, D. Joshi, J. Li, et al. Image Retrieval Ideas, Influences and Trends of the New Age. ACM Computing Surveys,2008,40.
[25]Z. Jia, A. Balasuriya, S. Challa. Sensor fusion-based visual target tracking for autonomous vehicles. Artificial Life and Robotics. Artificial Life and Robotics,2008,12(1-2):p.317-328.
[26]高方君.C3I多传感器信息融合系统.火力与指挥控制,2008,33(4)：1 17-119.
[27]Y. Bar-Shalom, S.S. Blackman, R.J. Fitzgerald. Hierarchical track association and fusion for a networked surveillance system. IEEE Transactions on Aerospace and Electronic Systems,2007, 43(1):p.392-400.
[28]H.A.P. Blom, E.A. Bloem. Bayesian tracking of two possibly unresolved maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems,2007,43(2):p.612—627.
[29]P.H. Foo, G.W. Ng, K.H. Ng, et al. Application of intent inference for surveillance and conformance
monitoring to aid human cognition. Proceedings of the 10th International Conference on Information Fusion. Quebec, Canada,2007:p.1-8.
[30]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,2007,20(1):p.49-61.
[31]P. Smets, B. Ristic. Kalman filter and joint tracking and classification based on belief functions in the TBM framework. Information Fusion, Special Issue on the Seventh International Conference on Information Fusion, Part Ⅱ,2007,8(1):p.16-27.
[32]A.K. Auβling, F.E. Schneider, D. Wildermuth, et al. A switching algorithm for tracking extended targets. Informatics in Control Automation and Robotics,2007,2:p.117-128.
[33]C. Wang, B. Hu, P. Li. Research on Intelligently Knowledge Integration Model of Industry Cluster in China Based on Information Fusion. Proceeding of 4th International Conference on Wireless communications, Networking and Mobile Computing. Dalian, China,2008:p.1-4.
[34]杨露菁,余华.多源信息融合理论与应用.北京邮电大学出版社.2006.
[35]刘同明,夏祖勋,解洪成.数据融合技术及其应用.北京：国防工业出版社.1998.
[36]康耀红.数据融合理论与应用.西安：西安电子科技大学出版社.1997.
[37]D.L. Hall, J. Llinas. An Introduction to Multisensor Data Fusion. Proc. of the IEEE,1997,85(1):p. 6-23.
[38]任彦.多传感器信息融合技术研究(博士学位论文).哈尔滨：哈尔滨工程大学.2004.
[39]M. Bedworth, J. O'Brien. The omnibus model:A new model of data fusion?. IEEE Aerospace and Electronic Systems Magazine,2000,15(4):p.30～36.
[40]C.B. Frankel, M.D. Bedworth. Control, estimation and abstraction in fusion architectures:lessons from human information processing. Proceeding of the 3rd International Conference on Information Fusion. Paris, France,2000,1:p. MOC5/3- MOC510 vol.1.
[41]何友,关欣,王国宏.多传感器信息融合研究进展与展望.宇航学报,2005,26(4)：524-530.
[42]P. Hannah, A. Starr, A. Ball. Decisions in condition monitoring —An examplar for data fusion architecture. Proceedings of 2000 International Conference on Information Fusion. France, Paris, 2000,1:p. MOD5/23-MOD5/30 vol.1.
[43]J. Esteban, A. Starr, R. Willetts, et al. A Review of data fusion models and architectures:towards engineering guidelines. Neural Computing and Applications,2005,14(273-281).
[44]L.C. Luo, M.G. Kay. Multisensor integration and fusion for intelligent machines and systems. US: Abbex Publishing Corporation,1995:p.321-456.
[45]B.V. Dasarathy. Multi-sensor, multi-source information fusion:architecture, algorithms, and applications-a panoramic overview. Proceeding of the 2nd IEEE International Conference on Computational Cybernetics. Vienna, Austria,2004:p.5-5.
[46]E.F. Nakamura, A.A.F. Loureiro, A.C. Frery. Information fusion for wireless sensor networks: Methods, models, and classifications. ACM Computing Surveys,2007,39(3).
[47]E. Shahabzian, D. Blodgett, P. Labbe. The extended OODA model for data fusion systems. Proceedings of 2001 International Conference on Information Fusion. Canada,2001:p.19-25.
[48]X. Huang, M. Wang. Multi-sensor data fusion structures in autonomous systems:a review. Proceeding of the 2003 IEEE International Symposium on Intelligent Control. Houston, USA, 2003,817-821.
[49]L.A. Klein. Sensor and Data Fusion Concepts and Applications,2nd edition. Society of Photo-Optical Instrumentation Engineers (SPIE).1999.
[50]K.D. SOMMER, O. KUEHN, F.P. LEON, et al. A bayesian approach to information fusion for evaluating the measurement uncertainty. Robotics and Autonomous Systems,2009,57:p. 339-344.
[51]B. Guo, M.S. Nixon, T.R. Damarla. Acoustic information fusion for ground vehicle classification. Proceeding of the 11th International Conference on Information Fusion. Cologne, Germany,2008: p.1-7.
[52]F. Johansson, G. Falkman. A Bayesian network approach to threat evaluation with application to an air defense scenario. Proceedings of 2008 11th International Conference on Information Fusion. Cologne,2008:p.1-7.
[53]F. Johansson, G. Falkman. Detection of vessel anomalies-a Bayesian network approach. Proceedings of the 3rd International Conference on Intelligent Sensors, Sensor Networks and Information Processing. Melbourne, Qld.,2007:p.395-400.
[54]高青.多传感器数据融合算法研究(博士学位论文).西安：西安电子科技大学,2008.
[55]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):p.932-943.
[56]B. Moshiri, A.M. Khalkhali, H.R. Momeni. Designing a home security system using sensor data fusion with DST and DSmT methods. Proceeding of the 10th International Conference on Information Fusion. Quebec, Canada,2007:p.1-6.
[57]G. Shafer. A Mathematical Theory of EvidencePrinceton, USA:Princeton University Press.1976.
[58]F. Smarandache, J. Dezert. An introduction to the theory of plausible and paradoxical reasoning. Lecture Notes In Computer Science,2003,2542:p.12-23.
[59]R.T. Anderson, G. Chowdhary, E.N. Johnson. Comparison of RBF and SHL Neural Network Based Adaptive Control. Journal of Intelligent & Robotic Systems,2009,54(1-3):p.183-199.
[60]F.J. Lin, P.H. Chou. Adaptive Control of Two-Axis Motion Control System Using Interval Type-2 Fuzzy Neural Network. IEEE Transactions on Industrial Electronics,2009,56(1):p.178-193.
[61]H. Lee, S. Hong, E. Kim. A new genetic feature selection with neural network ensemble. International Journal of Computer Mathematics,2009,86(7):p.1105-1117.
[62]F. Altiparmak, B. Dengiz, A.E. Smith. A General Neural Network Model for Estimating Telecommunications Network Reliability. IEEE Transactions on Reliability,2009,58(1):p.2-9.
[63]L.A. Zadeh. Fuzzy sets. Inform. Contr.,1965,8:p.338-353.
[64]C.S. Chiu, K.Y. Lian. Hybrid Fuzzy Model-Based Control of Nonholonomic Systems:A Unified Viewpoint. IEEE Transactions on Fuzzy Systems,2008,16(1):p.85-96.
[65]H. Gao, T. Chen. Stabilization of Nonlinear Systems Under Variable Sampling:A Fuzzy Control Approach. IEEE Transactions on Fuzzy Systems,2007,15(5):p.972-983.
[66]X. Chen. Real Wavelet Transform-Based Phase Information Extraction Method:Theory and Demonstrations. IEEE Transactions on Industrial Electronics,2009,56(3):p.891-899.
[67]S.L. Linfoot. Wavelet families for orthogonal wavelet division multiplex. Advances in Computational Mathematics,2008,44(18):p.1101-1102.
[68]K.M. Holt, D.L. Neuhoff. Deterministic Annealing for Entropy-Constrained Vector Quantizer Design. IEEE Transactions on Information Theory,2008,54(9):p.4305-4323.
[69]E. Learned-Miller, J. DeStefano. A Probabilistic Upper Bound on Differential Entropy. IEEE Transactions on Information Theory,2008,54(11):p.5223-5230.
[70]F. Melgani, Y. Bazi. Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization. IEEE transactions on information technology in biomedicine, 2008,12(5):p.667-677.
[71]V.I. Norkin, M.A. Keyzer. Asymptotic efficiency of kernel support vector machines (SVM). Cybernetics and Systems Analysis,2009,45(4):p.575-588.
[72]V.K. Finn. The Synthesis of Cognitive Procedures and the Problem of Induction. Automatic Documentation and Mathematical Linguistics,2009,43(3):p.149-195.
[73]Applying category theory to improve the performance of a neural architecture. Neurocomputing, 2009,72(13-15):p.3158-3173.
[74]K. Fu, L. Zhang. Strong limit theorems for random sets and fuzzy random sets with slowly varying weights. Information Sciences,2008,178(12):p.2648-2660.
[75]D.A. Alvarez. Nonspecificity for infinite random sets of indexable type. Fuzzy Sets and Systems, 2008,159(3):p.289-306.
[76]R.A.M. PEREIRA, R.A. RIBEIRO, P. SERRA. Rule Correlation And Choquet Integration In Fuzzy Inference Systems. International Journal of Uncertainty, Fuzziness, and Knowledge-based Systems,2008,16(5):p.601-626.
[77]A. Rico, O. Strauss, D. Mariano, et al. Choquet Integrals As Projection Operators For Quantified Tomographic Reconstruction. Fuzzy Sets and Systems,2009,160(2):p.198-211.
[78]E. Giuli, W. Tholen. A Topologist's View of Chu Spaces. Applied Categorical Structures,2007, 15(5-6):p.573-598.
[79]吕漫丽,孙灵芳.多传感器信息融合技术.自动化技术与应用,2008,27(2)：79-82.
[80]陈文辉,马铁华.多传感器信息融合技术的研究与进展.科技情报开发与经济,2006,16(19)：212-213.
[81]赵蕊,贺建军.多传感器融合技术.计算机测量与控制,2007,15(9)：1124-1126,1134.
[82]N.J. Nilsson. A mobius automation:an application of artificial intelligence techniques. Proceedings of the 1st international Joint Conference on Artificial Intelligence. Washington, DC, 1969:p.509-520.
[83]R.S. Mosher. Test and evaluation of a versatile walk- ing truck. Proceedings of Off-Road Mobility Sym-posium. Washington, DC,1968:p.359-378.
[84]徐国保,尹怡欣,周美娟.智能移动机器人技术现状及展望.机器人技术与应用,2007(2)：29-34.
[85]迟建男,徐心和.移动机器人即时定位与地图创建问题研究.机器人,2004,26(1)：92-96.
[86]罗荣华,洪炳榕.移动机器人同时定位与地图创建研究进展.机器人,2004,26(2)：182-186.
[87]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.控制理论与应用,2005,22(3)：455-460.
[88]王璐,蔡自兴.未知环境下移动机器人并发建图与定位的研究进展.机器人,2004,26(4)：380-384.
[89]王耀南,余洪山.未知环境下移动机器人同步地图创建与定位研究进展.控制理论与应用,2008,25(1)：57-65.
[90]蔡自兴,肖正,于金霞.动态环境中移动机器人地图构建的研究进展.控制工程,2007,14(3)：231-235.
[91]S. Noykov, C. Roumenin. Occupancy grids building by sonar and mobile robot. Robotics and Autonomous Systems,2006,55(2):p.162-175.
[92]L. Yenilmez, H. Temeltas. A new approach to map building by sensor data fusion:sequential principal component-SPC method. The International Journal of Advanced Manufacturing Technology,2007,34:p.168-178.
[93]G. Grisetti, G.D. Tipaldi, C. Stachniss, et al. Fast and accurate SLAM with Rao-Blackwellized particle filters. Robotics and Autonomous Systems,2007,55(1):p.30-38.
[94]X. Zhang, A. Rad, Y. Wong. A robust regression model for simultaneous localization and mapping in autonomous mobile robot. Journal of Intelligent and Robotic Systems,2008,53(2):p. 183-202.
[95]梁志伟,马旭东,戴先中等.基于分布式感知的移动机器人同时定位与地图创建.机器人,2009,31(1)：33-39.
[96]李新德,黄心汉,王敏.基于经典DSmT的Sonar栅格地图创建.计算机应用研究,2007,24(3)：209-212.
[97]A. Elfes, H. Moravec. High resolution maps from wide angle sonar. Proc of the IEEE Int Conf on Robotics and Automation. St. Louis MO,1985:p.116-121.
[98]N.C. Mitsou, C.S. Tzafestas. Temporal occupancy grid for mobile robot dynamic environment mapping. Proceeding of the 2007 Mediterranean Conference on Control and Automation. Athens, Greece,2007:p.1-8.
[99]D. Rodriguez-Losada, F. Matia, R. Galan. Building geometric feature based maps for indoor service robots. Robotics and Autonomous Systems,2006,54(7):p.546-558.
[100]T. Kwon, J. Song. Real-time building of a thinning-based topological map. Intelligent Service Robotics,2008,1(3):p.211-220.
[101]J.L. Blanco, J. Gonzalez, J.A. Fernandez-Madrigal. Fernandez-Madrigal. Consistent observation grouping for generating metric-topological maps that improves robot localization. Proceeding of the 2006 IEEE International Conference on Robotics and Automation. Orlando, USA,2006:p. 818-823.
[102]J.L. Blanco, J. Gonzalez, J.A. Fernandez-Madrigal. Subjective local maps for hybrid metric-topological SLAM. Robotics and Autonomous Systems,2009,57(1):p.64-74.
[103]Yong-Ju Lee, Byung-Doo Yim, Jae-Bok Song. Mobile Robot Localization based on Effective Combination of Vision and Range Sensors. International Journal of Control, Automation, and Systems,2009,7(1):p.97-104.
[104]B. Choi, J. Lee. Localization of a mobile robot based on an ultrasonic sensor using dynamic obstacles. Artificial life and robotics,2008,12(1-2):p.280-283.
[105]J. Borenstein, H.R. Everett, L. Feng, et al. Mobile Robot Positioning Sensors and Techniques. Journal of Robotic Systems,1997,14(4):p.231-249.
[106]X. Zeng, X. Huang, M. Wang, et al. The depth information estimation of microscope defocus image based-on Markov random field. Proceeding of 2008 IEEE Conference on Robotics, Automation and Mechatronics. Chengdu, China,2008:p.999-1004.
[107]M.W.M.G. Dissanayake, P. Newman, S. Clark, et al. A solution to the simultaneous localisation and map building (SLAM) problem. IEEE Trans on Robotics and Automation,2001,17(7):p. 229-241.
[108]J.E. Guivant, E.M. Nebot. Optimization of the Simultaneous Localization and Map Building Algorithm for Real Time Implementation. IEEE Transactions on Robotics and Automation,2001, 17(3):p.242-257.
[109]K. Demirli, M. Khoshnejad. Autonomous parallel parking of a car-like mobile robot by a neuro-fuzzy sensor-based controller. Fuzzy Sets and Systems,2009,160(19):p.2876-2891.
[110]B.A. Merhy, P. Payeur, E.M. Petriu. Application of Segmented 2-D Probabilistic Occupancy Maps for Robot Sensing and Navigation. IEEE Transactions on Instrumentation and Measurement, 2008,57(12):p.2827-2837.
[111]L. X., H. X., W. M., et al. A fusion Machine based on DSmT and PCR5 for robot's map reconstruction. International Journal of Information Acquisition,2006,3(3):p.1-11.
[112]蔡自兴,陈白帆,王璐等.异质多移动机器人协同技术研究的进展.智能系统学报,2007,2(3)：1-7.
[113]S.I. Roumeliotis, G.A. Bekey. Dist ributed multi-robot localization. IEEE Transactions on Robotics and Automation,2002,18(5):p.781-795.
[114]I. Rekleitis, G. Dudek, E. Milios. Probabilistic cooperative localization and mapping in practice. Proceeding of IEEE International Conference on Robotics and Automation, Taipei, China,2003, 2:p.1907-1912.
[115]P. Quintero-Alvarez, G. Ramirez, S. Zeghloul. A collision-free path-planning method for an articulated mobile robot. Applied bionics and biomechanics,2007,4(2):p.71-81.
[116]J.C. Latombe. Robot Motion Planning.. Boston, MA:Kluwer.1991.
[117]Pere. Automatic Planning of Manipulator Movements. IEEE Trans on System Man and Cybemetics,1981,11(11):p.681-698.
[118]R.A. Brooks. Solving the findpath problem by good representation of free space. IEEE Trans. on Sys. Mart. and Cybern,1983,13(3):p.190-197.
[119]M.K. Habib. Real Time Mapping and Dynamic Navigation for Mobile Robots. International Journal of Advanced Robotic Systems,2007,4(3):p.323-338.
[120]X. Sun, S. Koenig. The Fringe-Saving A* Search Algorithm-A Feasibility Study. Proceedings of the International Joint Conference on Artificial Intelligence. Hyderabad, India,2007:p. 2391-2397.
[121]A. Stentz. The focussed D* algorithm for realtime replanning. Proceedings of the Joint Conference on Artificial Intelligence,1995.
[122]L. Huang. Velocity planning for a mobile robot to track a moving target-a potential field approach. Robotics and Autonomous Systems,2009,57(1):p.55-63.
[123]O. Khatib. Real-Time Obstacle Avoidance for Manipulators and Mobile Robots. The International Journal of Robotics Research,1986,5(1):p.90-98.
[124]J.H. Holland. Genetic algorithm and the optimal allocations of trials. SIAM Journal of Computing, 1973,2(2):p.88-105.
[125]丁伟,孙华,曾建辉.基于多传感器信息融合的移动机器人导航综述.传感器与微系统,2006,25(7)：1-3.
[126]G. Bauzil, M. Briot, P. Ribes. A navigation sub2system using ultra-sonic sensors for the mobile robot Hilare.1st Int Conf. on Robot Vision and Sensory Control. Stanford-upon-Avon,U K,1981: p.47-58.
[127]李磊,曹志强,侯增广等.基于行为的轮式移动机器人导航控制.控制与决策,2004,19(6)：707-710.
[128]张永谦,王挺,安成万等.复合机构移动机器人红外和超声阵列信息处理方法.计算机工程与应用,2005,41(7)：34-36.
[129]J. Dezert, F. Smarandache. Advances and Applications of DSmT for Information Fusion Vol.1. Rehoboth:American Research Press.2004.
[130]J. Dezert, F. Smarandache. On the generation of hyper-powersets for the DSmT. Proceeding of the 6th International Conference on Information Fusion. Cairns, Australia,2003:p.1118-1125.
[131]T. M., I. A., T. T. On logical method for counting Dedekind numbers. Lect. Notes on Comp.Sci., 2001,2138:p.424-427.
[132]J. Dezert, F. Smarandache. Advances and Applications of DSmT for Information Fusion. Vol.2. Rehoboth:American Research Press.2006.
[133]L. Zadeh. A simple view of the Dempster-Shafer theory of evidence and its implications for the rule of combination. Berkeley Cognitive Science Report No.33, University of California, Berkeley, CA,1985.
[134]L. Zadeh. Review of Mathematical theory of evidence. AI Magazine,1984,5(3):p.81-83.
[135]L. Zadeh. On the validity of Dempster's rule of combination. Memo M 79/24, Univ. of California, Berkeley.1979.
[136]C.K. Murphy. Combining belief functions when evidence conflicts, Decision Support Systems, Elsevier Publisher. Decision Support Systems,2000,29:p.1-9.
[137]L. Zadeh. A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination. AI Magazine,1986,7(2):p.85-90.
[138]J. Dezert, F. Smarandache. Advances and Applications of DSmT for Information Fusion. Vol.3. Rehoboth:American Research Press.2008.
[139]F. Smarandache. A Unifying Field in Logics:Neutrosophic Logic, Neutrosophy, Neutrosophic Set, Neutrosophic Probability.3rd ed. Rehoboth, USA:American Research Press.2003.
[140]J. Dezert, F. Smarandache. Introduction to the fusion of quantitative and qualitative beliefs. Information & Security an International Journal,2006,20(1):p.9-49.
[141]X. Huang, P. Li, M. Wang. Evidence Reasoning Machine Based on DSmT for Mobile Robot SLAM in Unknown Dynamic Environment. Proceeding of 2009 IEEE International Conference on Robotics and Biomimetics. Guilin, China,2009.
[142]李鹏,黄心汉,王敏.基于混合DSm模型的移动机器人地图构建.华中科技大学学报(自然科学版),2008,36(Sup.Ⅰ)：174-176.
[143]P. Li, X. Huang, M. Wang. Robot Map Building in Unknown Dynamic Environment based on Hybrid Dezert-Smarandache Model. Information Technology Journal,2009,8(3):p.284-292.
[144]王卫华.未知环境中移动机器人创建地图的研究.[博士学位论文].上海：上海交通大学图书馆,2003.
[145]李鹏,黄心汉,王敏.基于混合DSm模型的移动机器人动态环境地图构建.机器人,2009,31(1)：40-46,52.
[146]P. Li, X. Huang, M. Wang. Multiple Mobile Robots Map Building Based on DSmT. Proceeding of 2008 IEEE Conference on Robotics, Automation and Mechatronics. Chengdu, China,2008:p. 509-514.
[147]李鹏,黄心汉,王敏.基于DSm混合模型的多机器人信息融合及地图构建.计算机研究与发展,2009,46(1)：70-76.
[148]L. Foner. Yenta:A Multi-Agent, Referral-based, Matchmaking System. Proceeding of the First International,Conference on Autonomous Agent,1997.
[149]Chia Hsun Chiang, Po Jui Chiang, Fei, J.C.-C., et al. A comparative study of implementing Fast Marching Method and A* SEARCH for mobile robot path planning in grid environment:Effect of map resolution. Proc. IEEE Workshop Adv. Rob. Soc. Impacts, ARSO. Hsinchu, Taiwan,2007:p. 1-6.
[150]P. Li, X. Huang, M. Wang. A hybrid method for dynamic local path planning. Procceding of International Conference on Networks Security, Wireless Communications and Trusted Computing. Wuhan, China,2009,1:p.317-320.
[151]李新德.多源不完善信息融合方法及其应用研究.[博士学位论文].武汉：华中科技大学图书馆,2007.
[152]X. Zeng, X. Huang, P. Li. The depth information estimation of microscope defocus image based-on Markov random field.2008 IEEE International Conferences on Robotics, Automation & Mechatronics. Chengdu, China:2008.
[153]B. Hu, C. Wang, P. Li. Research on Intelligently Knowledge Integration Model of Industry Cluster in China Based on Information Fusion.2008 International Conference on Wireless Communications, Networking and Mobile Computing, Dalian, China:2008.
[154]B. Hu, C. Wang, P. Li. Empirical Study of Knowledge Fusion Process within Chinese High-Tech Industry Clusters Based on Information Fusion Method. Journal of Information & Knowledge Management,2009,8(4):353-361.
[155]李鹏,黄心汉,王敏.DSmT框架下的自适应通用比例分配法则.计算机工程与应用,2010,46(6)：16-18.
[156]高健.DSmT信息融合技术及其在机器人地图创建中的应用.[博士学位论文].武汉：华中科技大学图书馆,2009.