基于传感器管理的移动机器人融合算法研究
|
摘要
由于多机器人系统具备单机器人系统无法比拟的优势,如强鲁棒性、自适应性和高效率。因而,多机器人系统是当前机器人技术领域的一个研究热点,将管理学、社会学、生物学和分布式人工智能学等领域的方法和理论引入机器人学的研究中,从系统的角度探讨多机器人系统的合作行为、组织形式、信息交互和进化机制等问题。本文以多机器人系统为研究背景,结合传感器管理技术,对多机器人系统中信息的融合,多机器人协调中的任务分配与规划等问题进行了深入研究,提供了有效地解决方案,主要工作和贡献如下：
本文首先综述了多源信息融合、传感器管理以及多机器人协调控制的研究现状,分析比较了当前该领域的主要研究问题、主要研究方法和关键技术,总结了各自的特点和发展趋势,其中,对外部环境感知和多源信息融合是移动机器人协调控制的基本能力。为此本文介绍了研究中所涉及的基础理论和实验中使用的机器人本体上用于感知外部信息的各种传感器。
接着对融合算法进行了两点改进。一方面,DSmT融合算法的效果依赖于广义基本信度分配,即gbba。而广义基本信度分配值的获取都是由专家根据自身的经验提出的,具有很大的主观性。而粗糙集理论仅仅需要传感器的测量数据,而无需其它主观信息,即可归纳出数据间的内在联系。将粗糙集理论与DSmT理论结合起来,根据粗糙集的数据归纳特性,得到gbba的客观获取算法,为下一步推理,即融合过程提供客观依据。另一方面,与DST理论相比,DSmT理论能够很好地解决证据冲突时信息融合的问题,却带来了“焦元爆炸”,致使推理过程的计算量大大增加。同时考虑到DST理论良好的数学基础和冲突情况下的融合效果,本文提出DST-DSmT智能融合算法,力图结合两种理论的优点。在DST融合算法和DSmT融合算法相互转换的过程中,给出了对矛盾焦元的处理方法。该方法充分利用包含冲突的原始信息,目的在于充分反映冲突焦元所提供的信息,以期降低对最终融合结果的影响。
为了充分发挥融合系统的功能及性能,需要在环境条件容许的情况下,对有限的传感器资源进行科学合理的分配。首先考虑了传感器在机器人本体上的分布特点,针对不同的信息源在融合过程中的可靠程度以及其对融合结果的影响大小是不同的,提出信息源支持度(MES)概念,充分考虑了焦元之间的相关程度来确定多源系统的信息源的重心,避免了仅仅依据焦元信度分配的数值平均来进行决策的局限性。计算系统中每个信息源与系统信息源重心之间的关系,去除了与当前时刻无关或影响不大的传感器,大大减少了参与融合的传感器数量,从而降低系统计算复杂程度。在此基础上,针对多源信息融合系统中存在大量不确定信息的特点,结合线性规划方法,建立了目标函数、约束条件和优化准则,运用规划论的优化方法得到系统中传感器的管理决策,同时也提高了获得正确融合结果的可能。
在上述研究的基础上,对多机器人系统提出动态分区的协作探索策略。设计运动协调混合式的体系结构,给出了行为管理、行为模块和行为决策的功能设计,并设计了4种常用的行为模块。设计了任务协调混合分层的体系结构,并给出了多机器人系统任务协调工作流程。每个机器人根据运动协调机制自主运动,同时任务协调机制也实现了机器人的自主任务分配。在机器人已经完成任务或者不能完成任务时,机器人之间能够根据不确定度变化量的大小自主协商,将任务分配给能最大程度提高效率的的机器人,有效地避免了过多的机器人集中于相同的子任务导致冲突加剧的问题。
最后通过在Pioneer2-DXe机器人平台上的仿真获得的实验结果和数据,验证了算法的鲁棒性和可靠性。
With a large range of advantages, such as robustness, adaptation and high efficiency, a multi-robot system can outperform a single robot and tend to be accepted in many applications where a single robot system has been thought not be suitable. As a result, the multi-robot system has been paid much attention. Nowadays as a rising subject, the cooperative robotics integrate the theories of management seience, soeiology, biology and distributed AI etc. It discusses many topics systematically, such as cooperative behaviors, arehitecture, communication and evolution of the robot system. Based on multi-sensor management, the dissertation gives a through and systematic research on the information fusion of multi-robot system, the tasks distribution and programming of multi-robot coordination. The main contributions are as follows:
Firstly, the paper suvreys the developments of multi-source information, cooperative robotics, sensor management and several influential multi-robot systems. We introduce the main research methods, the characteristics and key technology following the main topics about multi-mobile robots. The paper introduces the involved theories too.
Then the two improvements are studied on the fusion algorithm. On the one hand, the effect of DSmT fusion algorithm depends on the general basic belief assignment called gbba. The gbba is obtained by the experience of experts. It's based on their own knowledge and easy to cause subjectivity and conflict. The Rough Set Theory needs only sensor data without any subjective information to sum up the inter link between the data. Integreting RST theory and DSmT theory, an objective algorithm on gbba has no difficulty to establishied with the summarized character on data of RST. It will provide an objective basis for the further reasoning and fusion. On the other hand, compared with the DST theory, DSmT theory can resolve the conflict evidences in the fusion information successfully. But DSmT theory brought the problem about "explosion of focus elements". It results an increasing calculation in the fusion greatly. Taking into account the excellent mathematics foundation of DST theory and good fusion result with low conflict situations, DST-DSmT intelligent algorithm is presented in the paper to integrate the advantages of both theories. During the conversion process between DST and DSmT fusion algorithm, an approach processing the conflict focus enlements is given. The method takes full advantage of the conflict in the original information to reflect the information provided by the conflict focues enlements fully.It would decrease the impact on the final fusion result.
In order to make the best use of multi-source fusion system, the sensor management becomes an important part of data fusion system.Firstly, considering the distribution of sensors on the robot, different information sources have different reliability and impact in the fusion. The concept of Measurement of Evidence Support, MES is proposed in the paper. The correlation of the focus elements is fully taken into account to determine the core of the information sources in the multi-source system. It has avoided the limitation just depending on the average of the general basic belief assignment to make decisions. A distance is obtained according to the relationship between every information source and the core of the sources in the multi-source system. Some sensors which are irrelevant or have little effect in the fusion have been filted to greatly reduce the number of the sensor in the fusion. Therefore the computation complexity is reduced sharply as well. There exists a great deal of uncertain information in the multi-source information fusion system. An objective function, constraints and optimization function are established with linear methods. The decision about the sensors in the system will be obtained with the optimization method. At the same time the possiblity of the correct fusion result will be improved.
Based on the above studies, the collaborative exploration strategy for dynamic partitioning is proposed on multi-robot system. A modular hybrid structure for practical application is designed and made applicable theoretic methods for behavior management, behavior process and behavior decision to the structure.A Layered hybrid architecture for multi-task coordiantion is designed. And the task coordinate flow is given to the multi-robot system. Each robot has its own motion occording to the coordination mechanism, and the multi-task coordination mechanisms make the autonomy of the robot task allocation come to reality. When the robot has completed the task, or can not continue its task, the robots can be self-consultation to get the maximum benefits according to the change degreee of the uncertainty. It's effective to avoid too many robots putting togenther their focus on the same sub-task, which will lead to intensification of the conflicts.
At last, we mend the filter process so as to improve the practicability and reliability. Simulations has been done to prove the practicability and reliability of the method.
本文首先综述了多源信息融合、传感器管理以及多机器人协调控制的研究现状,分析比较了当前该领域的主要研究问题、主要研究方法和关键技术,总结了各自的特点和发展趋势,其中,对外部环境感知和多源信息融合是移动机器人协调控制的基本能力。为此本文介绍了研究中所涉及的基础理论和实验中使用的机器人本体上用于感知外部信息的各种传感器。
接着对融合算法进行了两点改进。一方面,DSmT融合算法的效果依赖于广义基本信度分配,即gbba。而广义基本信度分配值的获取都是由专家根据自身的经验提出的,具有很大的主观性。而粗糙集理论仅仅需要传感器的测量数据,而无需其它主观信息,即可归纳出数据间的内在联系。将粗糙集理论与DSmT理论结合起来,根据粗糙集的数据归纳特性,得到gbba的客观获取算法,为下一步推理,即融合过程提供客观依据。另一方面,与DST理论相比,DSmT理论能够很好地解决证据冲突时信息融合的问题,却带来了“焦元爆炸”,致使推理过程的计算量大大增加。同时考虑到DST理论良好的数学基础和冲突情况下的融合效果,本文提出DST-DSmT智能融合算法,力图结合两种理论的优点。在DST融合算法和DSmT融合算法相互转换的过程中,给出了对矛盾焦元的处理方法。该方法充分利用包含冲突的原始信息,目的在于充分反映冲突焦元所提供的信息,以期降低对最终融合结果的影响。
为了充分发挥融合系统的功能及性能,需要在环境条件容许的情况下,对有限的传感器资源进行科学合理的分配。首先考虑了传感器在机器人本体上的分布特点,针对不同的信息源在融合过程中的可靠程度以及其对融合结果的影响大小是不同的,提出信息源支持度(MES)概念,充分考虑了焦元之间的相关程度来确定多源系统的信息源的重心,避免了仅仅依据焦元信度分配的数值平均来进行决策的局限性。计算系统中每个信息源与系统信息源重心之间的关系,去除了与当前时刻无关或影响不大的传感器,大大减少了参与融合的传感器数量,从而降低系统计算复杂程度。在此基础上,针对多源信息融合系统中存在大量不确定信息的特点,结合线性规划方法,建立了目标函数、约束条件和优化准则,运用规划论的优化方法得到系统中传感器的管理决策,同时也提高了获得正确融合结果的可能。
在上述研究的基础上,对多机器人系统提出动态分区的协作探索策略。设计运动协调混合式的体系结构,给出了行为管理、行为模块和行为决策的功能设计,并设计了4种常用的行为模块。设计了任务协调混合分层的体系结构,并给出了多机器人系统任务协调工作流程。每个机器人根据运动协调机制自主运动,同时任务协调机制也实现了机器人的自主任务分配。在机器人已经完成任务或者不能完成任务时,机器人之间能够根据不确定度变化量的大小自主协商,将任务分配给能最大程度提高效率的的机器人,有效地避免了过多的机器人集中于相同的子任务导致冲突加剧的问题。
最后通过在Pioneer2-DXe机器人平台上的仿真获得的实验结果和数据,验证了算法的鲁棒性和可靠性。
With a large range of advantages, such as robustness, adaptation and high efficiency, a multi-robot system can outperform a single robot and tend to be accepted in many applications where a single robot system has been thought not be suitable. As a result, the multi-robot system has been paid much attention. Nowadays as a rising subject, the cooperative robotics integrate the theories of management seience, soeiology, biology and distributed AI etc. It discusses many topics systematically, such as cooperative behaviors, arehitecture, communication and evolution of the robot system. Based on multi-sensor management, the dissertation gives a through and systematic research on the information fusion of multi-robot system, the tasks distribution and programming of multi-robot coordination. The main contributions are as follows:
Firstly, the paper suvreys the developments of multi-source information, cooperative robotics, sensor management and several influential multi-robot systems. We introduce the main research methods, the characteristics and key technology following the main topics about multi-mobile robots. The paper introduces the involved theories too.
Then the two improvements are studied on the fusion algorithm. On the one hand, the effect of DSmT fusion algorithm depends on the general basic belief assignment called gbba. The gbba is obtained by the experience of experts. It's based on their own knowledge and easy to cause subjectivity and conflict. The Rough Set Theory needs only sensor data without any subjective information to sum up the inter link between the data. Integreting RST theory and DSmT theory, an objective algorithm on gbba has no difficulty to establishied with the summarized character on data of RST. It will provide an objective basis for the further reasoning and fusion. On the other hand, compared with the DST theory, DSmT theory can resolve the conflict evidences in the fusion information successfully. But DSmT theory brought the problem about "explosion of focus elements". It results an increasing calculation in the fusion greatly. Taking into account the excellent mathematics foundation of DST theory and good fusion result with low conflict situations, DST-DSmT intelligent algorithm is presented in the paper to integrate the advantages of both theories. During the conversion process between DST and DSmT fusion algorithm, an approach processing the conflict focus enlements is given. The method takes full advantage of the conflict in the original information to reflect the information provided by the conflict focues enlements fully.It would decrease the impact on the final fusion result.
In order to make the best use of multi-source fusion system, the sensor management becomes an important part of data fusion system.Firstly, considering the distribution of sensors on the robot, different information sources have different reliability and impact in the fusion. The concept of Measurement of Evidence Support, MES is proposed in the paper. The correlation of the focus elements is fully taken into account to determine the core of the information sources in the multi-source system. It has avoided the limitation just depending on the average of the general basic belief assignment to make decisions. A distance is obtained according to the relationship between every information source and the core of the sources in the multi-source system. Some sensors which are irrelevant or have little effect in the fusion have been filted to greatly reduce the number of the sensor in the fusion. Therefore the computation complexity is reduced sharply as well. There exists a great deal of uncertain information in the multi-source information fusion system. An objective function, constraints and optimization function are established with linear methods. The decision about the sensors in the system will be obtained with the optimization method. At the same time the possiblity of the correct fusion result will be improved.
Based on the above studies, the collaborative exploration strategy for dynamic partitioning is proposed on multi-robot system. A modular hybrid structure for practical application is designed and made applicable theoretic methods for behavior management, behavior process and behavior decision to the structure.A Layered hybrid architecture for multi-task coordiantion is designed. And the task coordinate flow is given to the multi-robot system. Each robot has its own motion occording to the coordination mechanism, and the multi-task coordination mechanisms make the autonomy of the robot task allocation come to reality. When the robot has completed the task, or can not continue its task, the robots can be self-consultation to get the maximum benefits according to the change degreee of the uncertainty. It's effective to avoid too many robots putting togenther their focus on the same sub-task, which will lead to intensification of the conflicts.
At last, we mend the filter process so as to improve the practicability and reliability. Simulations has been done to prove the practicability and reliability of the method.
引文
[1]SUBHASH CHALLA and DON KOKS, Bayesian and Dempster-Shafer fusion, Sadhana Vol.29, Part 2, April 2004, pp.145-174、.Ayrulu, B., and Barshan, B.2002. Reliability measure assignment to sonar for robust target differentiation. Pattern Recognition 35(6):1403-1419.
[2]E, C., and Borenstein, J. Characterization of a 2d laser scanner for mobile robot obstacle negotiation. In Proceedings IEEE International Conference on Robotics and Automation (ICRA),2512-2518.2002.
[3]White F E. Data fusion lexicon. Joint directors of laboratories, Technical Panel for C3, Data fusion sub-panel, naval ocean systems center, San Diego, CA, USA,1987
[4]White F E. A model for data fusion. In:Proc.1st National Symposium on Sensor Fusion. Orlando, FL, vol.2, Apr.5-8,1988
[5]M.Bedworth, J.O'Brien.The omnibus model:a new model of data fusion? IEEE Aerospace and Electronic Systems Magazine,2000,15(4):30-36
[6]C.B.Frankel, M.D.Bedworth. Control, estimation and abstraction in fusion architectures:lessons from human information processing. In:Proceeaing of the 3rd International Conference on Information Fusion. Paris, France:2000. MOC5/3-MOC5/1
[7]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
[8]Shafer G A Mathematical Theory of Evidence. Princeton N J:Princeton University Press,1976.133-185
[9]DEZERT J. Foundations for a New Theory of Plausible and Paradoxical Reasoning [J]. Information and Security,2002,9:13-57.
[10]Zadeh L A. Fuzzy sets. Information and Control,1965,8:338-353
[11]Simon S. Haykin. Neural networks and learning machines, Person Education Ltd.USA. 2008.736-780
[12]J.M.Nash.Optimal allocation of tracking resource.Proceedings IEEE Conference on Decision and control, p.1177-1180.
[13]R.Fung, E.Horvitz, P.Rothman.Decision Theoretic Approach to Sensor Management. DTIC#AB B172227, Feb 1993, Wright-Patterson AFB.
[14]Raj Malhotra.Temporal Considerable in Sensor Management. NAECON'95, Proceedings of IEEE 1995 National Aerospace and Electronics Conference, Dayton, OH,May 22-26,1995.Vol.1(A96-13576 02-62),New York,Institute of Electrical and Electonics Engineers,Inc,1995, p.86-93.
[15]David A.Castanon.Optimal search strategies in dynamic hypothesis testing.IEEE Trans.On system, Man and Cybernetics, Vol.25, No.7, July 1995, p.1130-1138.
[16]R.Wasburn, A.Chao, D.Castanon, D.Bertsekas, R.Malhotra.Stochastic Dynamic Programming for Far-Sighted Sensor Management 1977 IRIS National Symposium on Sensor and Data Fusion,1997.
[17]Liu Xianxing,Pan Quan,Zhang Hongcai,Dai guozhong. Study on Algorithm of Sensor Manaement Based on Functions of efficiency and Waste.Chinese Journal of Aeronautics,Vol.13, No.1,2000, p.39-40.
[18]P.L.Rothman,S.G.Bier.Evaluation of Sensor Management Systems.Proceedings of the IEEE 1989 National Aerospace and Electronics Conference,NAECON 1989, Vol.4, Dayton, OH, May 22-26 1989,p.1747-1752.
[19]J.M.Manyika, H.Durrant-Whyte.On Sensor Management in Decentralized Data Fusion. Proceedings of the 31st Conference on Decision and Control, Vol.4, Tucson, AZ, December 16-18 1992, p.3506-3507.
[20]A.Gaskell,P.Probert.Sensor Models and a Framework for Sensor Management.Sensor Fusion VI Proceedings of the SPIE-The International Society for Optical Engineering, Vol.2059, Boston, MA,September 7-8 1993, p.2-13.
[21]B.D.Leon,P.R.Heller.An Expert system and Simulaion Approach for Sensor Management and Control in Distributed Surveillance Network.Applications of Artificial Intelligence V. Proceedings of the SPIE-The International Society for Optical Engineering, Vol.786, Orlando,FL,May 18-20 1987, p.41-51.
[22]Hui, Patrick J.EXNAV:An Intelligence Sensor Processor.Proceeding NAECON 1988, IEEE,p.1219-1219.
[23]Cowan, Rosa A.Improved Tracking and Data fusion through Sensor Management and Control. Proceeding Data Fusion Symp.1987, p.661-665.
[24]J.M.Mplina Lopez, F.J.Jimenez Rodriguze, J.R.Casar Corredera. FuzzyReasoning for Multisensor Management.1995 IEEE International Conference on Systems, Man, and Cybernetics, Vol2, Vancouver, British Corumbia, Canada, October22-25 1995, p.1398-1403.
[25]J.Zhongliang,X.Hong,W.xueqin.Information Fusion and Tracking of Maneuvering Targets with Artificial Neutral Networks.Proceedings of the 1994 IEEE International Conference on Neural Networks,Vol.5,Orlando,FL,June 27-July2 1994, p.3403-3408.
[26]Hintz Kenneth J.,McIntyre,Greg.Goal lattices for sensor management.Proceedings of the 1999 Signal Processing,Sensor Fusion, and Target Recognition Ⅷ.Orlando, FL,USA(SPIE proceedings.Vol.3365),1999, p.249-255.
[27]McIntyre Gregory A., Hintz Kenneth J.Sensor management simulation and comparative study. Signal Processing, Sensor Fusion,and Target Recognition VI,Proceedings of the conference, Orlando, FL,Apr.21-24,1997(A97-39676 10-63),Bellingham, WA,Society of Photo-Optical
[28]Schmaedeke Wayne.Information based sensor management.Signal Processing,Sensor Fusion,and Target Recognition II,Proceedings of the Meeting, Orlando, FL, Apr.12-14, 1993 (A94-2976109-63),Bellingham,WA,Society of Photo-Optical Instrumentation Engineers (SPIE Proceedings.Vol.1955),1993, p.156-164.
[29]Friedman Avner, Kastella Keith, Schmaedeke Wayne. Sensor Management and Nonlinear Filtering Research.NASA no.19990032046.
[30]K.Kastella.Event-Averaged Maximum Likelihood Estimation and Mean-Field Theory in Multitarget Tracking. IEEE Transcation on Automatic Control, Vol,140, No.6, p.1070-1074.
[31]W.Schmaedeke and Kastella..Event-Averaged Maximum Likelihood Estimation and Information Based on Sensor Management.Signal Processing, Sensor Fusion, and Target Recognition III. Proceedings of the SPIE-The International Society for Optical Engineering, Vol.2232,Orlando,FL, April 4-6 1994, p.91-96.
[32]K.Kastella.A Maximum Likelihood Estimator for Report-to-Track Association.Signal and Data Processing of Small Targets 1993.Proceedings of the SPIE-The International Society for Optical Engineering,Vol.2059,Boston,MA,September 7-8 1993, p.2-13.
[33]Pierre Dodin, Julien.Verliac, Vincent Nimier.Analysis of the Multisensor Multitarget Tracking Resource Allocation Problem.The 3rd Intermational Conference of Information Fusion, Paris, France, July 10-13rd,2000, WECE-3.
[34]R.Mahler.Measurement models for ambiguous evidence using conditional random sets. Proceedings of the SPIE-The International Society for Optical Engineering.Vol.3068, 1997,p.40-51.
[35]I.R.Goodman, R.P.S.Mahler, H:T.Nguyen.Mathematics of Data Fusion.Academic Publishers,1997.
[36]Wang Guohong,He you,Yang Zhi,Mao Shi yi,Ludajin.Adaptive Sensor management in Multisensor Data Fusion System.Chinese Journal of Electronics,Vol.8,No.2,1999, p.136-139.
[37]刘先省.传感器管理方法研究[D].西安：西北工业大学博士学位论文,2000年.
[38]田康生,朱光喜.目标跟踪中的传感器管理[J],传感器技术,2003,22(3)：27-29.
[39]T. Ueyama, T. Fukuda, A. Sakal et al. Hierarchical Control Arehitecture with Learning and Adaptation Ability for Cellular Robotic system [J]. Distributed Autonomous Robotic systems,1994:17-28
[40]Alami R, Fleury S, Herrb M, et al. Multi-robot cooperration in the MARTHA project. IEEE Robotics& Automation Magazine,1998(1):36-47
[41]丁澄颖,何衍,蒋静坪.基于蚁群算法的多机器人协作策略.机器人,25(5)：414-418,2003.
[42]http://www.cs.ualberta.ca/-kube/-research.html
[43]http://www.-robotics.usc.edu/-barry/NerdHerd.html
[44]J.Deneubours, S. Goss, G. Sandini, F. Eferrari, and P.Dario.Self-organizing collection and transport of objects in unpredictable environment, In Proceedings of Japan-USA SymPosium on Flexibility Automation,PP:1093-1098,1990.
[45]L.Steel.Cooperation between distributed agents through self-organzing. Decentralized Artificial Intelligence, Y. Demazeau and J-P.Muller, Eds.Amsterdam, The Netherlands: Elsevief,1990
[46]T.Ueyama, T.Fukuda, F.Arai, Ykawauchi, Y. Kauto, S.Matsumura, and T.Uesugi. Communication architecture for cellular robotie system. JSME InternationalJoural, Series C,36:353-360,1993.
[47]An-hui Cai, ToShio Fulcuda, Fumihito Arai, Tsuyoshi Ueyama, Atsushi Sakai. Hierarehical Control Architecture for Cellular Robotic System:Simulations and Experiments. IEEE International Conf. On Robotics and Automation,1995: 1191-1196P
[48]Ueyama T, Fukuda T. Self-organization of cellular robots using random walk with simple rules. Proceedings of IEEE ICRA,1993:595-600P
[49]Toshio Fukuda, Yoshio Kawauehi, Fumio Hara. Dynamic Distributed Knowledge System in Self-organizing Robotic System:CEBOT. IEEE Proc. Of Conf. On Robotics and Automation, Saeramento, California, April 1991:1908-1913P
[50]Lynne E.Parker.Heterogeneous multi-robot cooperation. PhD thesis, MIT 1994.
[51]王越超.多机器人协作系统研究：学位论文J.哈尔滨工业大学1999
[52]董胜龙,陈卫东,席裕庚.多移动机器人编队的分布式控制系统,机器人.2000,22(6)： 433-438
[53]T.Weigel, J-S.Gutmann, M.Dietl, A.Kleiner, and B.Nebel.CS Freiberg:Coordinating robots for sueeessful soeeer Playing. IEEE Transaetions on Robotics and Automation, vol.18 (5):685-699,2002.
[54]李实,徐旭明,叶棒,孙增忻.国际机器人足球比赛及其相关技术.机器人.2000,22(8)：420-426
[55]SUBHASH CHALLA and DON KOKS, Bayesian and Dempster-Shafer fusion, Sadhana Vol.29, Part 2, April 2004, pp.145-174
[56]蓝金辉,马宝华,蓝天,周兆英,D-S证据理论数据融合方法在目标识别中的应用[J],清华大学学报(自然科学版)2001年第41卷第2期,53-55
[57]刘怀国,吴陈,张冰,D-S证据理论在多传感器融合中的应用[J],华东船舶工业学院学报(自然科学版),Vol.15No：3,2001年6月
[58]S. Le H'egarat-Mascle, D. Richard and C. Ottl'e,Multi-scale data fusion using Dempster Shafer evidence theory, [J],Integrated Computer-Aided Engineering 10 (2003)9-22
[59]Samuel Foucher,Mickael Germain, Jean-Marc Boucher, Goze Bertin Benie,Multisource Classification Using ICM and Dempster-Shafer Theory, [J],IEEE Transacttions on Instrumentation and Measuretation,VOL(51), NO:2,2002
[60]Dezert, J and F. Smarandache.2003. On the generation of hyper-powersets for the DSmT[C]. In:Proceedings of the 6th Int. Conf. on Information Fusion. Cairns, Qld., Australia,10-13 July 2003.
[61]Smarandache F., Dezert. J (Editors)(2004), Advances and Applications of DSmT (I) for Information Fusion[M], American Research Press, Rehoboth
[62]Smarandache F., Dezert. J (Editors)(2006), Advances and Applications of DSmT (II) for Information Fusion[M], American Research Press, Rehoboth
[63]Z. Pawlak.Rough Sets:Theoretical Aspects of Reasoning about Data [J].Dordrecht Kluwer Academic Publishers,1991.
[64]张文修,吴伟志等,粗糙集理论与方法[M],科学出版社,2001年,北京
[65]李新德,多源不完善信息融合方法及其应用研究[D],博士学位论文,武汉：华中科技大学图书馆,2007.
[66]Everett, H.R., Sensors for Mobile Robots, Theory and Applications. New York, Natick,MA,A.K.Peters,Ltd.,1995
[67]高健,DSmT信息融合技术及其在机器人地图创建中的应用[D],博士学位论文,武汉： 华中科技大学图书馆,2009
[68]Salzenstein F, Boudraa A O. Unsupervised multisensor data fusion approach [C].International Symposium on Signal Processing and its Applications, Kuala Lumpur,Malaysia,13-16 August,2001,QUT, Australia,152-154
[69]王奉涛,马孝江,朱泄,王志鹏.基于Dempster-Shafer证据理论的信息融合在设备故障诊断中应用[J],大连理工大学学报,2003,43(4)：470-474
[70]俞志富,罗景青,基于灰关联分析与D-S证据理论的多传感器雷达辐射源识别方法[J],电讯技术,2004,(1)：52-5
[71]Al-Ani A, Deriche M. A Dempster-Shafer theory of evidence approach for combiningtrained neural networks [C]. The 2001 IEEE International Symposium on Circuits and Systems.6-9 May2001,3:703-706
[72]黄鵾,陈森发,周振国等.基于粗集理论和证据理论的多源信息融合方法[J],信息与控制,2004,33(8)：422-426.
[73]Yao Y Y, Pawan L ingras. Interp retations of Belief Functions in The Theory of Rough Sets [J]. Information Sciences,1998,104 (12):81-106.
[74]杨善林,刘业政,李亚飞.基于Rough Sets理论的证据获取与合成方法[J],管理科学学报,2005,8(5)：69-75.
[75]Skowron A., The relationship between the rough set theory and evidence theory. Bulletin of Polish Academy of Science Mathematics,1989,37:87-90
[76]Skowron A.,The rough sets theory and evidence theory. Fundamental Information, 1990,ⅩⅢ:245-262
[77]张文修,吴伟志.粗糙集理论介绍和研究综述[J],模糊系统与数学,2000,14(4)：1-12
[78]张睿,胡孔法,陈崚.不完备信息系统的规则提取研究[J],计算机应用研究,2007.7[7]67-70
[79]李文,孙辉,陈善木.一种建立模糊模型的粗糙集方法[J].控制理论与应用,2001,18(1)：69-75
[80]张建军,张静波.一种新的基于粗糙集理论的决策表离散化算法[J],西安电子科技大学学报(自然科学版),2004,31(3)：469-47
[81]苗夺谦.理论中连续属性的离散化方法[J].自动化学报,2001,27(3)：296-302
[82]陶志,许宝栋,汪定伟.一种基于粗糙集理论的连续属性离散化方法[J],东北大学学报(自然科学版),2003,24(8)：747-750
[83]吴山产,毛锋,王文渊.基于粗糙集的两种离散化算法的研究[J].计算机工程与应用.2004,26：68-70
[84]李新德,黄心汉,王敏.基于经典DSmT的Sonar栅格地图创建[J],计算机应用研究,2007,24(3)：209-212.
[85]DEZERT J. Foundations for a New Theory of Plausible and Paradoxical Reasoning [J]. Information and Security,2002,9:13-57.
[86]Lefevre E, Colot O, VannoorenbergheR Belief function combination and conflict management [J]. Information Fusion,2002,3 (2):149-162
[87]Smets P, Kennes R. The transfer belief model [J]. Artificial Intelligence,1994,66 (3): 191-234
[88]Yager R R. On the Dempster-Shafer framework and new combination rules [J] Information System,1989,41(2):93-137
[89]孙全,叶秀清,顾伟康.一种新的基于证据理论的合成公式[J],电子学报,2000,28(8)：117-119
[90]张山鹰,潘泉,张洪才.一种新的证据推理组合规则[J],控制与决策,2000,15(5)：540-544
[91]潘泉,张山鹰,程咏梅,张洪才.证据推理的鲁棒性研究[J].自动化学报,2001.27(6)：798-805
[92]Haenni R. Are alternatives to Dempster's rule of combination real alternatives Comments on "About the belief function combination and the conflict management problem " [J].Information Fusion,2002,3 (4):237-239
[93]Murphy C K.Combining belief functions when evidence conflicts [J]. Decision Support Systems,2000,29:1-9
[94]邓勇,施文康,朱振福.一种有效处理冲突证据的组合方法[J],红外与毫米波学报,2004,23(1)：27-32
[95]林作锉,牟克典.韩庆.基于未知扰动的冲突证据合成方法[J],软件学报,2004,18(8)：1150-1156
[96]侯俊,苗壮,潘泉.DST与DSmT自适应融合算法[J],微电子学与计算机,2006,23(10)：150-152
[97]Adel El-Fallah, Mike Perloff, Avinash Gandhe. Multisensor-Multitarget Sensor Management Using Geometric Objective Functions. IEEE Octoberl-3,2003:349-354
[98]Lefevre E, Colot O, VannoorenbergheR Belief function combination and conflict management [J]. Information Fusion,2002,3 (2):149-162
[99]Dezert, J. Smarandache, F.,2003. Partial ordering of hyper-power sets and matrix representation of belief functions within DSmT, Information Fusion,2003. Proceedings of the Sixth International Conference 2003, Volume 2:1230-1238
[100]Dezert, J. Smarandache, F.2008. A new probabilistic transformation of belief mass assignment, Information Fusion,2008 11th International Conference on Cologne June 30 2008-July 3 2008:1-8
[101]Dezert, J. Smarandache, F.,2003. Partial ordering of hyper-power sets and matrix representation of belief functions within DSmT, Information Fusion,2003. Proceedings of the Sixth International Conference 2003, Volume 2:1230-1238
[102]李新德,黄心汉,王敏.基于经典DSmT的Sonar栅格地图创建[J].计算机应用研究,2007,24(3)：209--212.
[103]Hong Wen, Xinhan Huang, Xinde Li,2008. A new tool applied to robot perception by selecting evidence sources, Automation and Logistics,2008. ICAL2008. IEEE International Conference on Qindao Septl-3.2008:2364-2369
[104]Jousselme A.L., Dominic G., et al,2001, A new distance between two bodies of evidence, Information Fusion,2001,2:91-101,
[105]Diaz J., Rifqi M., A similarity measure between basic belief assignments, in Proceeding of 9th International Conference on Information Fusion. Italy:Florence, July 10-13, 2006.1-6
[106]林志贵,基于证据理论的信息融合研究及其在水质监测中的应用[D]：[博士论文],南京：河海大学水利电力工程,2005
[107]Thun S, Bucken A. Integrating grid-based and topological maps for mobile robot navigation. In:Proceedings of the Thirteenth National Conference on Artificial Inteligence. Poland:1996:944-950
[108]Kuipers B J. Representing knowledge of large scale space. Technical Report TR-418, MIT Artificial Intelligence Laboratory,1997
[109]Patric Jensfelt. Approaches to mobile robot localization in indoor envirenments. Doctoral Thesis, Royal Institute of Technology,2001
[110]Raschke U, Borenstein J. A comparion of grid-type map-building techniques by index of performance. Proc. of the IEEE International Conference on Robotics and Automation,1990:1228-1832
[111]Borenstein J, Koren Y. Histogramic in-motion mapping for mobile robot obstacle avoidance. IEEE Transactions on Robotics and Automation,7(4):535-539
[112]赵忆文,移动机器人体系结构及多机器人协调规划研究[D]：[博士论文],沈阳：中国科学院沈阳自动化研究所,2000
[113]王硕,多机器人系统协调协作理论与应用的研究[D]：[硕士论文],中科院自动化研究 所,2001
[114]Smith R. The contract net protocol:high-level communication and control in a distributed problem solver. IEEE transactions on Computers,1980,29(12):1104-1113
[115]Brian Yamauchi. Frontier-based exploration using multiple robots. Proceedings of the Second International Conference on Autonomous Agents,1998,47-53
[2]E, C., and Borenstein, J. Characterization of a 2d laser scanner for mobile robot obstacle negotiation. In Proceedings IEEE International Conference on Robotics and Automation (ICRA),2512-2518.2002.
[3]White F E. Data fusion lexicon. Joint directors of laboratories, Technical Panel for C3, Data fusion sub-panel, naval ocean systems center, San Diego, CA, USA,1987
[4]White F E. A model for data fusion. In:Proc.1st National Symposium on Sensor Fusion. Orlando, FL, vol.2, Apr.5-8,1988
[5]M.Bedworth, J.O'Brien.The omnibus model:a new model of data fusion? IEEE Aerospace and Electronic Systems Magazine,2000,15(4):30-36
[6]C.B.Frankel, M.D.Bedworth. Control, estimation and abstraction in fusion architectures:lessons from human information processing. In:Proceeaing of the 3rd International Conference on Information Fusion. Paris, France:2000. MOC5/3-MOC5/1
[7]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
[8]Shafer G A Mathematical Theory of Evidence. Princeton N J:Princeton University Press,1976.133-185
[9]DEZERT J. Foundations for a New Theory of Plausible and Paradoxical Reasoning [J]. Information and Security,2002,9:13-57.
[10]Zadeh L A. Fuzzy sets. Information and Control,1965,8:338-353
[11]Simon S. Haykin. Neural networks and learning machines, Person Education Ltd.USA. 2008.736-780
[12]J.M.Nash.Optimal allocation of tracking resource.Proceedings IEEE Conference on Decision and control, p.1177-1180.
[13]R.Fung, E.Horvitz, P.Rothman.Decision Theoretic Approach to Sensor Management. DTIC#AB B172227, Feb 1993, Wright-Patterson AFB.
[14]Raj Malhotra.Temporal Considerable in Sensor Management. NAECON'95, Proceedings of IEEE 1995 National Aerospace and Electronics Conference, Dayton, OH,May 22-26,1995.Vol.1(A96-13576 02-62),New York,Institute of Electrical and Electonics Engineers,Inc,1995, p.86-93.
[15]David A.Castanon.Optimal search strategies in dynamic hypothesis testing.IEEE Trans.On system, Man and Cybernetics, Vol.25, No.7, July 1995, p.1130-1138.
[16]R.Wasburn, A.Chao, D.Castanon, D.Bertsekas, R.Malhotra.Stochastic Dynamic Programming for Far-Sighted Sensor Management 1977 IRIS National Symposium on Sensor and Data Fusion,1997.
[17]Liu Xianxing,Pan Quan,Zhang Hongcai,Dai guozhong. Study on Algorithm of Sensor Manaement Based on Functions of efficiency and Waste.Chinese Journal of Aeronautics,Vol.13, No.1,2000, p.39-40.
[18]P.L.Rothman,S.G.Bier.Evaluation of Sensor Management Systems.Proceedings of the IEEE 1989 National Aerospace and Electronics Conference,NAECON 1989, Vol.4, Dayton, OH, May 22-26 1989,p.1747-1752.
[19]J.M.Manyika, H.Durrant-Whyte.On Sensor Management in Decentralized Data Fusion. Proceedings of the 31st Conference on Decision and Control, Vol.4, Tucson, AZ, December 16-18 1992, p.3506-3507.
[20]A.Gaskell,P.Probert.Sensor Models and a Framework for Sensor Management.Sensor Fusion VI Proceedings of the SPIE-The International Society for Optical Engineering, Vol.2059, Boston, MA,September 7-8 1993, p.2-13.
[21]B.D.Leon,P.R.Heller.An Expert system and Simulaion Approach for Sensor Management and Control in Distributed Surveillance Network.Applications of Artificial Intelligence V. Proceedings of the SPIE-The International Society for Optical Engineering, Vol.786, Orlando,FL,May 18-20 1987, p.41-51.
[22]Hui, Patrick J.EXNAV:An Intelligence Sensor Processor.Proceeding NAECON 1988, IEEE,p.1219-1219.
[23]Cowan, Rosa A.Improved Tracking and Data fusion through Sensor Management and Control. Proceeding Data Fusion Symp.1987, p.661-665.
[24]J.M.Mplina Lopez, F.J.Jimenez Rodriguze, J.R.Casar Corredera. FuzzyReasoning for Multisensor Management.1995 IEEE International Conference on Systems, Man, and Cybernetics, Vol2, Vancouver, British Corumbia, Canada, October22-25 1995, p.1398-1403.
[25]J.Zhongliang,X.Hong,W.xueqin.Information Fusion and Tracking of Maneuvering Targets with Artificial Neutral Networks.Proceedings of the 1994 IEEE International Conference on Neural Networks,Vol.5,Orlando,FL,June 27-July2 1994, p.3403-3408.
[26]Hintz Kenneth J.,McIntyre,Greg.Goal lattices for sensor management.Proceedings of the 1999 Signal Processing,Sensor Fusion, and Target Recognition Ⅷ.Orlando, FL,USA(SPIE proceedings.Vol.3365),1999, p.249-255.
[27]McIntyre Gregory A., Hintz Kenneth J.Sensor management simulation and comparative study. Signal Processing, Sensor Fusion,and Target Recognition VI,Proceedings of the conference, Orlando, FL,Apr.21-24,1997(A97-39676 10-63),Bellingham, WA,Society of Photo-Optical
[28]Schmaedeke Wayne.Information based sensor management.Signal Processing,Sensor Fusion,and Target Recognition II,Proceedings of the Meeting, Orlando, FL, Apr.12-14, 1993 (A94-2976109-63),Bellingham,WA,Society of Photo-Optical Instrumentation Engineers (SPIE Proceedings.Vol.1955),1993, p.156-164.
[29]Friedman Avner, Kastella Keith, Schmaedeke Wayne. Sensor Management and Nonlinear Filtering Research.NASA no.19990032046.
[30]K.Kastella.Event-Averaged Maximum Likelihood Estimation and Mean-Field Theory in Multitarget Tracking. IEEE Transcation on Automatic Control, Vol,140, No.6, p.1070-1074.
[31]W.Schmaedeke and Kastella..Event-Averaged Maximum Likelihood Estimation and Information Based on Sensor Management.Signal Processing, Sensor Fusion, and Target Recognition III. Proceedings of the SPIE-The International Society for Optical Engineering, Vol.2232,Orlando,FL, April 4-6 1994, p.91-96.
[32]K.Kastella.A Maximum Likelihood Estimator for Report-to-Track Association.Signal and Data Processing of Small Targets 1993.Proceedings of the SPIE-The International Society for Optical Engineering,Vol.2059,Boston,MA,September 7-8 1993, p.2-13.
[33]Pierre Dodin, Julien.Verliac, Vincent Nimier.Analysis of the Multisensor Multitarget Tracking Resource Allocation Problem.The 3rd Intermational Conference of Information Fusion, Paris, France, July 10-13rd,2000, WECE-3.
[34]R.Mahler.Measurement models for ambiguous evidence using conditional random sets. Proceedings of the SPIE-The International Society for Optical Engineering.Vol.3068, 1997,p.40-51.
[35]I.R.Goodman, R.P.S.Mahler, H:T.Nguyen.Mathematics of Data Fusion.Academic Publishers,1997.
[36]Wang Guohong,He you,Yang Zhi,Mao Shi yi,Ludajin.Adaptive Sensor management in Multisensor Data Fusion System.Chinese Journal of Electronics,Vol.8,No.2,1999, p.136-139.
[37]刘先省.传感器管理方法研究[D].西安：西北工业大学博士学位论文,2000年.
[38]田康生,朱光喜.目标跟踪中的传感器管理[J],传感器技术,2003,22(3)：27-29.
[39]T. Ueyama, T. Fukuda, A. Sakal et al. Hierarchical Control Arehitecture with Learning and Adaptation Ability for Cellular Robotic system [J]. Distributed Autonomous Robotic systems,1994:17-28
[40]Alami R, Fleury S, Herrb M, et al. Multi-robot cooperration in the MARTHA project. IEEE Robotics& Automation Magazine,1998(1):36-47
[41]丁澄颖,何衍,蒋静坪.基于蚁群算法的多机器人协作策略.机器人,25(5)：414-418,2003.
[42]http://www.cs.ualberta.ca/-kube/-research.html
[43]http://www.-robotics.usc.edu/-barry/NerdHerd.html
[44]J.Deneubours, S. Goss, G. Sandini, F. Eferrari, and P.Dario.Self-organizing collection and transport of objects in unpredictable environment, In Proceedings of Japan-USA SymPosium on Flexibility Automation,PP:1093-1098,1990.
[45]L.Steel.Cooperation between distributed agents through self-organzing. Decentralized Artificial Intelligence, Y. Demazeau and J-P.Muller, Eds.Amsterdam, The Netherlands: Elsevief,1990
[46]T.Ueyama, T.Fukuda, F.Arai, Ykawauchi, Y. Kauto, S.Matsumura, and T.Uesugi. Communication architecture for cellular robotie system. JSME InternationalJoural, Series C,36:353-360,1993.
[47]An-hui Cai, ToShio Fulcuda, Fumihito Arai, Tsuyoshi Ueyama, Atsushi Sakai. Hierarehical Control Architecture for Cellular Robotic System:Simulations and Experiments. IEEE International Conf. On Robotics and Automation,1995: 1191-1196P
[48]Ueyama T, Fukuda T. Self-organization of cellular robots using random walk with simple rules. Proceedings of IEEE ICRA,1993:595-600P
[49]Toshio Fukuda, Yoshio Kawauehi, Fumio Hara. Dynamic Distributed Knowledge System in Self-organizing Robotic System:CEBOT. IEEE Proc. Of Conf. On Robotics and Automation, Saeramento, California, April 1991:1908-1913P
[50]Lynne E.Parker.Heterogeneous multi-robot cooperation. PhD thesis, MIT 1994.
[51]王越超.多机器人协作系统研究：学位论文J.哈尔滨工业大学1999
[52]董胜龙,陈卫东,席裕庚.多移动机器人编队的分布式控制系统,机器人.2000,22(6)： 433-438
[53]T.Weigel, J-S.Gutmann, M.Dietl, A.Kleiner, and B.Nebel.CS Freiberg:Coordinating robots for sueeessful soeeer Playing. IEEE Transaetions on Robotics and Automation, vol.18 (5):685-699,2002.
[54]李实,徐旭明,叶棒,孙增忻.国际机器人足球比赛及其相关技术.机器人.2000,22(8)：420-426
[55]SUBHASH CHALLA and DON KOKS, Bayesian and Dempster-Shafer fusion, Sadhana Vol.29, Part 2, April 2004, pp.145-174
[56]蓝金辉,马宝华,蓝天,周兆英,D-S证据理论数据融合方法在目标识别中的应用[J],清华大学学报(自然科学版)2001年第41卷第2期,53-55
[57]刘怀国,吴陈,张冰,D-S证据理论在多传感器融合中的应用[J],华东船舶工业学院学报(自然科学版),Vol.15No：3,2001年6月
[58]S. Le H'egarat-Mascle, D. Richard and C. Ottl'e,Multi-scale data fusion using Dempster Shafer evidence theory, [J],Integrated Computer-Aided Engineering 10 (2003)9-22
[59]Samuel Foucher,Mickael Germain, Jean-Marc Boucher, Goze Bertin Benie,Multisource Classification Using ICM and Dempster-Shafer Theory, [J],IEEE Transacttions on Instrumentation and Measuretation,VOL(51), NO:2,2002
[60]Dezert, J and F. Smarandache.2003. On the generation of hyper-powersets for the DSmT[C]. In:Proceedings of the 6th Int. Conf. on Information Fusion. Cairns, Qld., Australia,10-13 July 2003.
[61]Smarandache F., Dezert. J (Editors)(2004), Advances and Applications of DSmT (I) for Information Fusion[M], American Research Press, Rehoboth
[62]Smarandache F., Dezert. J (Editors)(2006), Advances and Applications of DSmT (II) for Information Fusion[M], American Research Press, Rehoboth
[63]Z. Pawlak.Rough Sets:Theoretical Aspects of Reasoning about Data [J].Dordrecht Kluwer Academic Publishers,1991.
[64]张文修,吴伟志等,粗糙集理论与方法[M],科学出版社,2001年,北京
[65]李新德,多源不完善信息融合方法及其应用研究[D],博士学位论文,武汉：华中科技大学图书馆,2007.
[66]Everett, H.R., Sensors for Mobile Robots, Theory and Applications. New York, Natick,MA,A.K.Peters,Ltd.,1995
[67]高健,DSmT信息融合技术及其在机器人地图创建中的应用[D],博士学位论文,武汉： 华中科技大学图书馆,2009
[68]Salzenstein F, Boudraa A O. Unsupervised multisensor data fusion approach [C].International Symposium on Signal Processing and its Applications, Kuala Lumpur,Malaysia,13-16 August,2001,QUT, Australia,152-154
[69]王奉涛,马孝江,朱泄,王志鹏.基于Dempster-Shafer证据理论的信息融合在设备故障诊断中应用[J],大连理工大学学报,2003,43(4)：470-474
[70]俞志富,罗景青,基于灰关联分析与D-S证据理论的多传感器雷达辐射源识别方法[J],电讯技术,2004,(1)：52-5
[71]Al-Ani A, Deriche M. A Dempster-Shafer theory of evidence approach for combiningtrained neural networks [C]. The 2001 IEEE International Symposium on Circuits and Systems.6-9 May2001,3:703-706
[72]黄鵾,陈森发,周振国等.基于粗集理论和证据理论的多源信息融合方法[J],信息与控制,2004,33(8)：422-426.
[73]Yao Y Y, Pawan L ingras. Interp retations of Belief Functions in The Theory of Rough Sets [J]. Information Sciences,1998,104 (12):81-106.
[74]杨善林,刘业政,李亚飞.基于Rough Sets理论的证据获取与合成方法[J],管理科学学报,2005,8(5)：69-75.
[75]Skowron A., The relationship between the rough set theory and evidence theory. Bulletin of Polish Academy of Science Mathematics,1989,37:87-90
[76]Skowron A.,The rough sets theory and evidence theory. Fundamental Information, 1990,ⅩⅢ:245-262
[77]张文修,吴伟志.粗糙集理论介绍和研究综述[J],模糊系统与数学,2000,14(4)：1-12
[78]张睿,胡孔法,陈崚.不完备信息系统的规则提取研究[J],计算机应用研究,2007.7[7]67-70
[79]李文,孙辉,陈善木.一种建立模糊模型的粗糙集方法[J].控制理论与应用,2001,18(1)：69-75
[80]张建军,张静波.一种新的基于粗糙集理论的决策表离散化算法[J],西安电子科技大学学报(自然科学版),2004,31(3)：469-47
[81]苗夺谦.理论中连续属性的离散化方法[J].自动化学报,2001,27(3)：296-302
[82]陶志,许宝栋,汪定伟.一种基于粗糙集理论的连续属性离散化方法[J],东北大学学报(自然科学版),2003,24(8)：747-750
[83]吴山产,毛锋,王文渊.基于粗糙集的两种离散化算法的研究[J].计算机工程与应用.2004,26：68-70
[84]李新德,黄心汉,王敏.基于经典DSmT的Sonar栅格地图创建[J],计算机应用研究,2007,24(3)：209-212.
[85]DEZERT J. Foundations for a New Theory of Plausible and Paradoxical Reasoning [J]. Information and Security,2002,9:13-57.
[86]Lefevre E, Colot O, VannoorenbergheR Belief function combination and conflict management [J]. Information Fusion,2002,3 (2):149-162
[87]Smets P, Kennes R. The transfer belief model [J]. Artificial Intelligence,1994,66 (3): 191-234
[88]Yager R R. On the Dempster-Shafer framework and new combination rules [J] Information System,1989,41(2):93-137
[89]孙全,叶秀清,顾伟康.一种新的基于证据理论的合成公式[J],电子学报,2000,28(8)：117-119
[90]张山鹰,潘泉,张洪才.一种新的证据推理组合规则[J],控制与决策,2000,15(5)：540-544
[91]潘泉,张山鹰,程咏梅,张洪才.证据推理的鲁棒性研究[J].自动化学报,2001.27(6)：798-805
[92]Haenni R. Are alternatives to Dempster's rule of combination real alternatives Comments on "About the belief function combination and the conflict management problem " [J].Information Fusion,2002,3 (4):237-239
[93]Murphy C K.Combining belief functions when evidence conflicts [J]. Decision Support Systems,2000,29:1-9
[94]邓勇,施文康,朱振福.一种有效处理冲突证据的组合方法[J],红外与毫米波学报,2004,23(1)：27-32
[95]林作锉,牟克典.韩庆.基于未知扰动的冲突证据合成方法[J],软件学报,2004,18(8)：1150-1156
[96]侯俊,苗壮,潘泉.DST与DSmT自适应融合算法[J],微电子学与计算机,2006,23(10)：150-152
[97]Adel El-Fallah, Mike Perloff, Avinash Gandhe. Multisensor-Multitarget Sensor Management Using Geometric Objective Functions. IEEE Octoberl-3,2003:349-354
[98]Lefevre E, Colot O, VannoorenbergheR Belief function combination and conflict management [J]. Information Fusion,2002,3 (2):149-162
[99]Dezert, J. Smarandache, F.,2003. Partial ordering of hyper-power sets and matrix representation of belief functions within DSmT, Information Fusion,2003. Proceedings of the Sixth International Conference 2003, Volume 2:1230-1238
[100]Dezert, J. Smarandache, F.2008. A new probabilistic transformation of belief mass assignment, Information Fusion,2008 11th International Conference on Cologne June 30 2008-July 3 2008:1-8
[101]Dezert, J. Smarandache, F.,2003. Partial ordering of hyper-power sets and matrix representation of belief functions within DSmT, Information Fusion,2003. Proceedings of the Sixth International Conference 2003, Volume 2:1230-1238
[102]李新德,黄心汉,王敏.基于经典DSmT的Sonar栅格地图创建[J].计算机应用研究,2007,24(3)：209--212.
[103]Hong Wen, Xinhan Huang, Xinde Li,2008. A new tool applied to robot perception by selecting evidence sources, Automation and Logistics,2008. ICAL2008. IEEE International Conference on Qindao Septl-3.2008:2364-2369
[104]Jousselme A.L., Dominic G., et al,2001, A new distance between two bodies of evidence, Information Fusion,2001,2:91-101,
[105]Diaz J., Rifqi M., A similarity measure between basic belief assignments, in Proceeding of 9th International Conference on Information Fusion. Italy:Florence, July 10-13, 2006.1-6
[106]林志贵,基于证据理论的信息融合研究及其在水质监测中的应用[D]：[博士论文],南京：河海大学水利电力工程,2005
[107]Thun S, Bucken A. Integrating grid-based and topological maps for mobile robot navigation. In:Proceedings of the Thirteenth National Conference on Artificial Inteligence. Poland:1996:944-950
[108]Kuipers B J. Representing knowledge of large scale space. Technical Report TR-418, MIT Artificial Intelligence Laboratory,1997
[109]Patric Jensfelt. Approaches to mobile robot localization in indoor envirenments. Doctoral Thesis, Royal Institute of Technology,2001
[110]Raschke U, Borenstein J. A comparion of grid-type map-building techniques by index of performance. Proc. of the IEEE International Conference on Robotics and Automation,1990:1228-1832
[111]Borenstein J, Koren Y. Histogramic in-motion mapping for mobile robot obstacle avoidance. IEEE Transactions on Robotics and Automation,7(4):535-539
[112]赵忆文,移动机器人体系结构及多机器人协调规划研究[D]：[博士论文],沈阳：中国科学院沈阳自动化研究所,2000
[113]王硕,多机器人系统协调协作理论与应用的研究[D]：[硕士论文],中科院自动化研究 所,2001
[114]Smith R. The contract net protocol:high-level communication and control in a distributed problem solver. IEEE transactions on Computers,1980,29(12):1104-1113
[115]Brian Yamauchi. Frontier-based exploration using multiple robots. Proceedings of the Second International Conference on Autonomous Agents,1998,47-53