基于网络模型的多机器人系统研究
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摘要
人工智能和机器人技术的迅猛发展给一些复杂、未知或危险性领域的任务完成带来了新的希望。自从上世纪80年末分布式移动机器人的概念被提出以后，多机器人系统的研究就如雨后春笋一般地迅速发展起来。多机器人系统在军事、航空航天、工业制造、自然界探索、灾害预防与处理、以及服务业等领域的远大应用前景吸引着大量的学者对多机器人系统的研究兴趣。但多机器人系统内部的关系极其复杂，系统的能力和潜力与系统中的各个机器人能否恰当处理好相互之间的竞争，并友好协作有着相当密切的关系。而系统中各个机器人的信息交换和共享，即各机器人间的相互通信是多机器人系统协作的充分而又必要的条件。通信的时间、效率和质量极大地影响着整个系统的适应性、灵活性和可靠性。
本文在建立了多机器人系统的物理、通信、控制网络模型的基础上，利用网络图论、矩阵理论、分布式算法等一些有效的数学工具对系统的通信、信息流和分布式控制进行了分析和设计，得到了一个从通信结构、信息流传播方式到分布式控制等方面都优化的系统。本文所做的主要研究工作和成果如下：
(1)建立了多机器人系统的物理、通信和控制网络模型，并分析了多机器人系统内的信息来源，概述了智能机器人的多传感器信息融合方法。
(2)根据小世界网络的特点，提出了依据全局效率和局部效率演化小世界网络的方法，并按照此方法演化了不同规模的多机器人系统的通信网络，得到了一个小世界网络模型的通信结构。
(3)利用网络图论、矩阵理论、分布式算法等数学工具对多机器人系统协作中的信息流和一致性问题进行了研究，多机器人系统的协作可在状态层或协作变量层达到一致性。本文给出了同步网络和部分同步网络的一致性协议，证明了所提出协议的收敛性，并通过对同步网络、部分同步网络及小世界网络的仿真进一步验证了所给出的一致性协议的收敛性。
(4)研究了多机器人编队系统，建立了通信和控制网络模型，并分析了编队系统的稳定性。
(5)提出了一种改进的多目标粒子群(PSO)优化算法，引入了外部知识库和变异算子，在Pareto多目标优化概念的基础上，运用此算法对多机器人编队系统进行了优化，仿真结果显示了多目标PSO算法能快速高效地优化多目标函数。
对上述问题的研究结果显示，本文所建立的多机器人系统的网络模型使分析和设计多机器人系统的协作策略更简单明了；小世界网络的演化构造了一个优化的多机器人系统的通信网络；信息流的一致性收敛问题的研究证明了通信在协作中的重要性；多目标PSO算法在多机器人编队优化控制中的成功应用说明了本文所提出的改进的PSO优化算法的有效性，从而得到了一个通信、控制都优化的多机器人系统。
The development of Artificial Intelligent and robotics technology brings new hopes for task implementation in complex, uncertain and hazardous environment. Since the advent of distributed mobile robotics in the late 1980s, the research field of multi-robot system (MRS) has grown dramatically, with a much wider variety of topics being addressed. The great application prospect of MRS in military, space, industry manufacture, nature exploration, disaster prevention and recovery, and personal service fields has drawn many researchers' interest. However, because of the complexity of inter-robot relationship, the capacity and potential of MRS are associated with the conflict elimination and cooperation of inter-robot greatly. Shared information is a sufficient and necessary condition for cooperation. The efficiency and quality of communication influence the adaptation, flexibility, and reliability of multi-robot system.
On the basis of physical, communication, and control network model building, the communication, information flow and distributed control methods have analyzed and designed in this thesis with some useful mathematic tools, such as algebraic graph theory, matrix theory, and distributed algorithm, and accordingly, a system was gotten with optimal communication architecture, information flow, and distributed control approaches. The main contributions of this thesis are as follows:
(1) Information resource in MRS was analyzed, and multiple sensor information was fused with Support Vector Machine (SVM) method. The physical, communication and control network model in MRS were built also.
(2) Under the characteristic of small-world network, a new method of small-world network evolution in terms of global and local efficiency evaluation index was proposed. Communication networks of MRS with different size were evolved, and optimal small-world network communication architecture was shaped correspondingly.
(3) Information flow and consensus problems in MRS were researched with some mathematic tools, such as algebraic graph theory, matrix theory, and distributed algorithm. Consensus can be formed on the situational state or the coordination variable. In this thesis, consensus protocols of synchronous and partial synchronous networks in MRS were presented, whose convergence were proved and convergence rate were derived. The simulation results of synchronous, partial synchronous and small-world network verified further the convergence of presented consensus protocols.
(4) Multi-robot formation system was studied. Under the communication and control network model, system's stability was analyzed in terms of algebraic graph theory.
(5) An improved multi-object Particle Swarm Optimization (PSO) algorithm with external repository and mutation operator was presented, and used to solve multi-object optimization problem in multi-robot formation system on the basis of Pareto optimal concept.
The aforementioned research results show that the network model of MRS makes it easier to analyze and design cooperation approaches. Consensus convergence study of information flow verifies the importance of communication in multi-robot cooperation. Evolution of small-world network constitutes optimal communication architecture of MRS. Successful application of multi-object PSO algorithm in multi-robot formation optimization problem show the efficiency of improved multi-object PSO algorithm. Therefore, MRS with optimal communication and control methods is gained.
本文在建立了多机器人系统的物理、通信、控制网络模型的基础上，利用网络图论、矩阵理论、分布式算法等一些有效的数学工具对系统的通信、信息流和分布式控制进行了分析和设计，得到了一个从通信结构、信息流传播方式到分布式控制等方面都优化的系统。本文所做的主要研究工作和成果如下：
(1)建立了多机器人系统的物理、通信和控制网络模型，并分析了多机器人系统内的信息来源，概述了智能机器人的多传感器信息融合方法。
(2)根据小世界网络的特点，提出了依据全局效率和局部效率演化小世界网络的方法，并按照此方法演化了不同规模的多机器人系统的通信网络，得到了一个小世界网络模型的通信结构。
(3)利用网络图论、矩阵理论、分布式算法等数学工具对多机器人系统协作中的信息流和一致性问题进行了研究，多机器人系统的协作可在状态层或协作变量层达到一致性。本文给出了同步网络和部分同步网络的一致性协议，证明了所提出协议的收敛性，并通过对同步网络、部分同步网络及小世界网络的仿真进一步验证了所给出的一致性协议的收敛性。
(4)研究了多机器人编队系统，建立了通信和控制网络模型，并分析了编队系统的稳定性。
(5)提出了一种改进的多目标粒子群(PSO)优化算法，引入了外部知识库和变异算子，在Pareto多目标优化概念的基础上，运用此算法对多机器人编队系统进行了优化，仿真结果显示了多目标PSO算法能快速高效地优化多目标函数。
对上述问题的研究结果显示，本文所建立的多机器人系统的网络模型使分析和设计多机器人系统的协作策略更简单明了；小世界网络的演化构造了一个优化的多机器人系统的通信网络；信息流的一致性收敛问题的研究证明了通信在协作中的重要性；多目标PSO算法在多机器人编队优化控制中的成功应用说明了本文所提出的改进的PSO优化算法的有效性，从而得到了一个通信、控制都优化的多机器人系统。
The development of Artificial Intelligent and robotics technology brings new hopes for task implementation in complex, uncertain and hazardous environment. Since the advent of distributed mobile robotics in the late 1980s, the research field of multi-robot system (MRS) has grown dramatically, with a much wider variety of topics being addressed. The great application prospect of MRS in military, space, industry manufacture, nature exploration, disaster prevention and recovery, and personal service fields has drawn many researchers' interest. However, because of the complexity of inter-robot relationship, the capacity and potential of MRS are associated with the conflict elimination and cooperation of inter-robot greatly. Shared information is a sufficient and necessary condition for cooperation. The efficiency and quality of communication influence the adaptation, flexibility, and reliability of multi-robot system.
On the basis of physical, communication, and control network model building, the communication, information flow and distributed control methods have analyzed and designed in this thesis with some useful mathematic tools, such as algebraic graph theory, matrix theory, and distributed algorithm, and accordingly, a system was gotten with optimal communication architecture, information flow, and distributed control approaches. The main contributions of this thesis are as follows:
(1) Information resource in MRS was analyzed, and multiple sensor information was fused with Support Vector Machine (SVM) method. The physical, communication and control network model in MRS were built also.
(2) Under the characteristic of small-world network, a new method of small-world network evolution in terms of global and local efficiency evaluation index was proposed. Communication networks of MRS with different size were evolved, and optimal small-world network communication architecture was shaped correspondingly.
(3) Information flow and consensus problems in MRS were researched with some mathematic tools, such as algebraic graph theory, matrix theory, and distributed algorithm. Consensus can be formed on the situational state or the coordination variable. In this thesis, consensus protocols of synchronous and partial synchronous networks in MRS were presented, whose convergence were proved and convergence rate were derived. The simulation results of synchronous, partial synchronous and small-world network verified further the convergence of presented consensus protocols.
(4) Multi-robot formation system was studied. Under the communication and control network model, system's stability was analyzed in terms of algebraic graph theory.
(5) An improved multi-object Particle Swarm Optimization (PSO) algorithm with external repository and mutation operator was presented, and used to solve multi-object optimization problem in multi-robot formation system on the basis of Pareto optimal concept.
The aforementioned research results show that the network model of MRS makes it easier to analyze and design cooperation approaches. Consensus convergence study of information flow verifies the importance of communication in multi-robot cooperation. Evolution of small-world network constitutes optimal communication architecture of MRS. Successful application of multi-object PSO algorithm in multi-robot formation optimization problem show the efficiency of improved multi-object PSO algorithm. Therefore, MRS with optimal communication and control methods is gained.
引文
[AAF~+1995] L. Aguilar, R. Alami, S. Fleury, M. Herrb, F. Ingrand, and F. Robert. Ten autonomous mobile robots (and even more) in a route network like environment. IEEE/RSJ Int. Conf. on IROS, vol. 2: 260-267, 1995.
[AAF~+1998] H. Asama, T. Arai, T. Fukuda, and T. Hasegawa, editors. Distributed Autonomous Robotic Systems 6. Springer-Verlag. 1998.
[AKN~+1999] M. Asada, H. Kitano, I. Noda, & M. Veloso. RoboCup: Today and tomorrow - What we have learned. Artificial Intelligence. 110(2):193-214, 1999.
[AMI1989] H. Asama, A. Matsumoto, and Y. Ishida. Design of an autonomous and distributed robot system: ACTRESS. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems. pp: 283-290, 1989.
[AOM~+1992] H. Asama, K. Ozaki, A. Matsumoto, Y. Ishida, and I. Endo. Development of task assignment system using communication for multiple autonomous robots. Journal of Robotics and Mechatronics. 4(2): 122-127, 1992.
[Asa1992] H. Asama, editor. Proceedings of the Int. Symposium on Distributed Autonomous Robotic Systems. Wako, Saitama, Japan, Sept. 1992.
[BA1994] T. Balch and R. C. Arkin. Communication in reactive multiagent robotic systems. Autonomous Robots. 1(1): 1-25, 1994.
[BA1999a] T. Balch and R. C. Arkin. Behavior-based formation control for multi-robot teams. IEEE Transaction on Robotics and Automation. XX(Y): 1015, 1999.
[BA1999b] A. L. Barabasi and R. Albert. Emergence of scaling in random networks. Science, vol. 286: 509-512, 1999.
[BE2000] F. Bergh and A. P. Engelbrecbt. Cooperative learning in neural networks using particle swarm optimizers. South African Computer Journal, vol. 26: 84-90, 2000.
[BE2001] F. Bergh and A. P. Engelbrecbt. Effects of swarm size on cooperative particle swarm optimizers. Proc. of the Genetic and Evolutionary Computation Conf. pp:892-899,2001.
[Ben1988] G. Beni. The concept of cellular robot. 3~(rd) IEEE Symposium on Intelligent Control, pp: 57-61,1988.
[BH2000] T. Balch, M. Hybinette. Social potentials for scalable multi-robot formations. Proceedings of the IEEE International Conference on Robotics and Automations, pp:73-80,2000.
[BKB2004] F. Borrelli, T. Keviczky, and G. J. Balas. Collision-free UAV formation flight using decentralized optimization and invariant sets. 43~(rd) IEEE Conf. on Decision and Control, pp: 1099-1104, 2004.
[Bol2003] B. Bollobas. Modern graph theory. Beijing World Publishing Corporation. Beijing. 2003.
[Boy2006] S. Boyd. Convex optimization of graph laplacian eigenvalues. Proceedings Int. Congress of Mathematicians, vol. 3:1311-1319, 2006.
[BP2002] T. Balch and L. E. Parker, editors. Robot Teams: From Polymorphism to Polymorphism. A K Peters, Natick, Massachusetts. 2002.
[Bro1986] R. A. Brooks. Achieving artificial intelligence through building robots. MIT AI Laboratory Report. AIM-899: 1-11, May 1986.
[Bro1991] R. A. Brooks. Intelligence without representation. Artificial Intelligence. vol.47: 139-159,1991.
[BS1987] P. L. Bolger and ShaferDempster. Reasoning with application to multisensor target identification systems. IEEE Transaction on SMC. 17(6): 968-977, 1987.
[BS2003] R. W. Beard and V. Stepanyan. Information consensus in distributed multiple vehicle coordination control. IEEE Conf on Decision and Control, pp: 2029-2034, 2003.
[BT1989] D. P. Bertsekas and J. N. Tsitsiklis. Parallel and distributed computation. Prentice Hall, Englewood Cliffs. New Jersey. 1989.
[CFK1997] Y. U. Cao, A. S. Fukunaga, and A. B. Kahng. Cooperative mobile robotics: antecedents and directions. Autonomous Robots. 4(1): 7-27, 1997.
[CL2002] C. A. C. Coello and M. S. Lecbuga. MOPSO: a proposal for multiple objective particle swarm optimization. Congress on Evolutionary Computation, vol. 2: 1051-1056,2002.
[Cle1999] M. Clerc. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. IEEE Int. Conf. on Evolutionary Computation, pp: 1951-1957, 1999.
[CMB2004] J. Cortes, S. Martinez, and F. Bullo. Robust rendezvous for mobile autonomous agents via proximity graphs in arbitrary dimensions. IEEE Transactions on Automatic Control. pp: 1-11, 2004.
[CP1995] O. Causse, L. H. Pampagnin. Management of a multi-robot system in a public environment. IEEE/RSJ Int. Conf on IROS, vol. 2: 246-252, 1995.
[CSK~+2001] L. Chaimowica, T. Sugar, V. Kumar, and M.F.M. Campos. An architecture for tightly coupled multirobot cooperation. IEEE Int. Conf on Robotics & Automation. pp: 2992-2997, 2001.
[CSM2005] M. Cao, D. A. Spielman, and A. S. Morse. A lower bound on convergence of a distributed network consensus algorithm. 44~(th) IEEE Conf on Decision and Control. pp: 2356-2361, 2005.
[DJM~+1993] G. Dudek, M. Jenkin, E. Milios, and D. Wilkes. A taxonomy for swarm robots. IEEE/RSJ Int. Conf on IROS. pp: 441-447, 1993.
[DJR1994] B. R. Donald, J. Jennings, and D. Rus. Analyzing teams of cooperating mobile robots. IEEE Int. Conf on Robotics & Automation. pp: 1896-1903, 1994.
[DOK1998] J. R Desai, I. Ostrowski, and V. Kumar. Controlling formation of multiple mobile robots. IEEE Int. Conf on Robotics & Automation. pp: 2864-2869, 1998.
[Doo1934] J. L. Doob. Stochastic processes and statistics. PNAS: Mathematics. pp: 376-379, 1934.
[DSK~+2002] A. Das, J. Spletzer, V. Kumar, and C. Taylor. Ad hoe networks for localization and control. 41~(st) IEEE Conf. on Decision and Control, pp: 2978-2983, 2002.
[EH1999] R. C. Eberhart and X. Hu. Human tremor analysis using particle swarm optimization. IEEE Congress on Evolutionary Computation. pp: 1927-1930, 1999.
[EM2002] K.I. Easton and A. Martinoli. Efficiency and optimization of explicit and implicit communication schemes in collaborative robotics experiments. IEEE/RSJ Int. Conf on Intelligent Robots and Systems. pp: 2795-2800, 2002.
[ETM1996] Y. Eiichi, A. Tamio, and Y. Masakazu. Evaluating the efficiency of local and global communication in distributed mobile robotic systems. IROS 96. pp: 1661-1666, 1996.
[Fax2002] J. A. Fax. Optimal and cooperative control of vehicle formations [Dissertation]. California Institute of Technology. 2002.
[FD2003] R. Fierro and A. K. Das. Hybrid control of reconfigurable robot formations. American Control Conference, pp: 4607-4612, 2003.
[FM2001] J. Fredslund and M. J. Mataric. Robot formations using only local sensing and control. IEEE Int. Symposium on Computational Intelligent in Robotics & Automation, pp: 308-314, 2001.
[FM2002] J. Fredslund and M. J. Mataric. A general algorithm for robot formations using local sensing and minimal communication. IEEE Transaction on Robotics and Automation. 18(5): 837-846, 2002.
[FN1987] T. Fukuda, S. Nakagawa. A dynamically reconfigurable robotic system (concept of a system and optimal configurations). Proceedings of IECON. pp: 588-595, 1987.
[FNK~+1988] T. Fukuda, S. Nakagawa, Y. Kawauchi, and M. Buss. Self-organizing robots based on cell structure—CEBOT. IEEE Int. workshop on Intelligent Robots and Systems, pp: 145-150, 1988.
[GM2002] B.P. Gerkey and M.J. Matarie. Sold!: auction methods for multirobot coordination. IEEE Transaction on Robotics and Automation. 18(5): 758-768, 2002.
[GR2004] C. Godsil and G. Royle. Algebraic graph theory. Beijing World Publishing Corporation. Beijing. 2004.
[HES2003] X. Hu, R. C. Eberhart, and Y. Shi. Particle swarm with extended memory for multi-objective optimization. Proc, of IEEE Swarm Intelligence Symposium, pp: 193-197,2003.
[HIV1976] C. C. Huang, D. Isaacson, and B. Vinograde. The rate of convergence of certain nonhomogeneous markov chains. Z. Wahrscheinlichkeitstheorie verw. Gebiete. pp: 141-146,1976.
[HJS~+] K. A. Hawick, H. A. James, J. E. Story, and R. J. Sheperd. An architecture for swarm robots. Technical report DHPC-121, University of Wales, UK.
[HM2004] Y. Hatano and M. Mesbahi. Agreement of random networks. IEEE Conf. on Decision and Control. 2004.
[HS1996] T. Haynes, S. Sen. Evolving behavioral strategies in predators and prey. In Gerhard Weiβand Sandip Sen, editors, Adaptation and Learning in Multiagent Systems, pp: 113-126. Springer Verlag, Berlin, 1996.
[IH1999] S. Ichikawa and F. Hara. Characteristics of object-searching and object-fetching behaviors of multirobot system using local communication. IEEE Int. Conf on Systems, Man and Sybernetics. vol. 4: 775-781, 1999.
[Ios1972] B. M. Iosifescu. On two recent papers on ergodicity in nonhomogeneous markov chains. The Annals of Mathematical Statistics. 43(5): 1732-1736, 1972.
[Jen1993] N.R. Jennings. Commitments, conventions: the foundation of coordination in multi-agent systems. The Knowledge Engineering Review, 8(3):223-250. 1993.
[JLM2003] A. Jadbabaie, J. Lin, and A. S. Morse. Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Transaction on Automatic Control. 48(6): 988-1001, 2003.
[JSS2006] Z. Jin, T. Shima, and C. J. Schumacher. Optimal scheduling for refueling multiple autonomous aerial vehicles. IEEE Transaction on Robotics. 22(4): 682-693, 2006.
[KAG2000] J. M. Keller, S. Auephanwiriyakul, and R D. Gader. New fuzzy set tools to aid in predictive sensor fusion. SPIE 2000. 4038(Ⅱ): 1497-1507, 2000.
[Kaz2002] B. S. AI. Kazemi. Multi-phase particle swarm optimization [Dissertation]. Syracuse University. 2002.
[KE1995] J. Kennedy and R. C. Eberhart. Particle swarm optimization. IEEE Conf on Neural Networks. pp: 1942-1948, 1995.
[KE1997] J. Kennedy and R. C. A discrete binary version of the particle swarm algorithm. IEEE Conf on SMC. pp: 4104-4109, 1997.
[KFB~+2006] T. Keviczky, K. Fregene, F. Borrelli, G. J. Balas, and D. Godbole. Coordinated autonomous vehicle formations: decentralization, control synthesis and optimization. American Control Conference. pp: 2022-2027, 2006.
[KGL2005] E. Klavins, R. Ghrist, and D. Lipsky. A grammatical approach to self-organizing robotic systems. IEEE Transaction on Automatic Control. pp: 1-13, 2005.
[Kle2004] J. Kleinberg. The small-world phenomenon and decentralized search. SIAM News. 37(3): 1-2, 2004.
[Kra1997] S. Kraus. Negotiation And Cooperation In Multi-agent Environments, Artificial Intelligence. vol. 94: 79-97, 1997.
[KR2001] S. Kume and A.A. Rizzi. A high-performance network infrastructure and protocols for distributed automation. IEEE Int. Conf. on Robotics & Automation. pp: 3121-3126, 2001.
[KWY~+2004] D. H. Kim, H. O. Wang, G. Ye, and S. Shin. Decentralized control of autonomous swarm systems using artificial potential functions: analytical design guidelines. 43~(rd) IEEE Conf. on Decision and Control. pp: 159-164, 2004.
[LC2005] Yangmin Li and Xin Chen. Stability on mult-robot formation with dynamic interaction topologies. IEEE Int. Conf. on Intelligent Robots & Systems. pp: 394-399, 2005.
[LJ2004] XiaoLin Long and JiangJing Ping. Towards multirobot communication. IEEE International Conference on Robotics and Biomimetics. pp: 307-312, 2004.
[LL1994] T. C. Lueth, T. Laengle. Task description, decomposition, and allocation in a distributed autonomous multi-agent robot system. IEEE/RSJ Int. Conf. on IROS. vol. 3: 1516-1523, 1994.
[LLX2003] A. Lim, Jing Lin and Fei Xiao. Particle swarm optimization and hill climbing to solve the bandwidth minimization problem. 5~(th) Metaheuristics Int. Conference. pp: 1-6, 2003.
[LSL2002] P. Lucidarme, O. Simonin, and A. Liegeois. Implementation and evaluation of a satisfaction/altruism based architecture for multirobot systems. IEEE Int. Conf on Rrobotics & Automation. pp: 1007-1012, 2002.
[LT2002] S. Liu, Yo Tian. Multi-agent learning methods in an uncertain environment. Proceedings of the 1st Machine Learning and Cybernetics, pp:650-654, 2002
[Lyn2004] N. A. Lynch 著，舒继武，李国东，佘华山 译．分布式算法。机械工业岀版社．北京．2004．
[Mat1994] M. J. Mataric. Interaction and Intelligence Behavior [Dissertation]. MIT. 1994.
[Mat1997] M. J. Mataric. Using communication to reduce locality in distributed multi-agent learning. Autonomous Robots. 1 (4): 1-20, 1997.
[Mat2001] M. J. Mataric. Learning In Behavior-based Multi-robot Systems: policies, models, and other agents. Journal of Cognitive Systems Research. vol. 2: 81-93, 2001.
[MBC~+2006] S. Martinez, F. Bullo, J. Cortes, and E. Frazzoli. On synchronous robotic networks-part I: models, tasks and complexity. IEEE Transaction on Automatic Control. pp: 1-30, June, 2006.
[MH1999a] M. Mesbahi and F. Y. Hadaegh. A robust control approach for the formation flying of multiple spacecraft. European Control Conference. 1999.
[MH1999b] M. Mesbahi and F. Y. Hadaegh. Formation flying control of multiple spacecraft via graphs, matrix inequalities, and switching. IEEE Int. Conf on Control Applications. Aug., 1999.
[Mi11967] S. Milgram. Psychology today. Physical Review Letters. vol. 2, pp: 60-67, 1967.
[ML1988] M. Mathews and S. J. Leszkiewicz. Efficient spacecraft formation keeping with consideration of ballistic coefficient control. AIAA Aerospace Science Meeting, 1988.
[MLT~+2002] Robin R. Murphy, C. L. Lisetti, Russell Tardif, Lian Irish, and Aaron Gage. Emotion-based control of cooperating heterogeneous mobile robots. IEEE Transaction on Robotics and Automation. 18(5): 744-757, Oct. 2002
[MNS1995] M. J. Mataric, M. Nilsson, and K. T. Simsarian. Cooperative multi-robot box-pushing. IEEE/IROS-95. pp: 556-561, 1995.
[MO2004] R. Muraleedharan and L. A. Osadciw. Decision making in a building access system using swarm intelligence & POSets. 38~(th) Annual Conf on Information Sciences and Systems. 2004.
[MR2001] A. Merke, M. Riedmiller. Karlsruhe Brainstormers: A reinforcement learning way to robotic soccer Ⅱ. In A. Birk, S. Coradeschi, & S. Tadokoro, editors, RoboCup-2001: Robot Soccer World Cup V, pp:322-327, 2001.
[Nak1997] T. Nakamura. Development of self-learning vision-based mobile robots for acquiring Soccer robots behaviors. In H. Kitano, editors, RoboCup-97: Robot Soccer World Cup I, pp:257-276, Springer, 1997.
[Nor1992] F. R. Noreils. An architecture for cooperative and autonomous mobile robots. IEEE Int. Conf on Robotics andAutomation, pp: 2703-2710, 1992.
[Nor1993] F. R. Noreils. Towards a robot architecture integrating cooperation between mobile robots: Application to indoor environment. International Journal of Robotics Research. 12(1): 79-98, Feb. 1993.
[NTH~+1999] J. Neira, J. D. Tardos, J. Horn, and G. Schmidt. Fusing range and intensity images for mobile robot localization. IEEE Transaction on Robotics & Automation. 15(1): 76-83, 1999.
[Par1993a] L.E. Parker. Designing control laws for cooperative agent teams. Proc. of the IEEE Robotics and Automation Conference. pp:582-587, 1993.
[Par1994a] L. E. Parker. Heterogeneous multi-robot cooperation [Dissertation]. MIT, 1994.
[Par1994b] Lynne. E. Parker. Alliance: An architecture for fault-tolerant multi-robot cooperation. IEEE Transaction on Robotics and Automation. 14(2): 220-240, 1994.
[Par1994c] L.E. Parker. ALLIANCE: An Architecture for Fault Tolerant, Cooperative Control of Heterogeneous Mobile Robots. IEEE/RSJ/G1 Int. Conf. on Intelligent Robots and Systems. pp: 776-783, Sept. 1994.
[Par1995] J Paredis. Coevolutionary Computation. Artificial Life. pp: 355-375, 1995.
[PAM~+1999] E. Pagello, A.D. Angelo, F. Montesello, and F. Gardli. Cooperative behaviors in multirobot systems through implicit communication. Robotics and Autonomous Systems. vol. 29: 65-77, 1999.
[PPC~+2002] G. A. S. Pereira, B. S. Pimentel, L. Chaimowicz, and M.F.M. Campos. Coordination of multiple mobile robots in an object carrying task using implicit communication. IEEE Int. Conf. on Robotics & Automation. pp: 281-286, 2002.
[PSH2002] A. Pant, P. Seiler, and K. Hedrick. Mesh stability of look-ahead interconnected systems. IEEE Transaction on Automatic Control. 47(2): 403-407, 2002.
[PY1990] S. Premvuti and S. Yuta. Consideration on the cooperation of multiple autonomous mobile robots. IEEE International Workshop of Intelligent Robots and Systems. pp: 59-63, 1990.
[PV2002] K. E. Parsopoulos and M. N. Vrahatis. Recent approaches to global optimization problems through particle swarm optimization. Natural Computing. 1(2-3): 235-306, 2002.
[RBA2005] W. Ren, R. W. Beard, and E. M. Atkins. A survey of consensus problems in multi-agent coordination. American Control Conference. pp: 1859-1864, 2005.
[RBM2004] W. Ren, W. Beard, and T. W. McLain. Coordination variables and consensus building in multiple vehicle systems. Cooperative Control, LNCIS 309. pp: 171-188, 2004.
[RDJ1995] D. Rus, B. Donald, and J. Jennings. Moving furniture with teams of autonomous robots. IEEE/RSJ Conf on Intelligent Robots and Systems. pp: 235-242, 1995.
[Rey1987] C. Reynolds. Flocks, herds and schools: A distributed behavioral model. Computer Graphics. 21 (4):25-34, 1987.
[RVV2003] M. Roth, D. Vail, and M. Veloso. A real-time world model for multi-robot teams with high-latency communication. IEEE/RSJ Int. Conf on Intelligent Robots and Systems. pp: 2494-2499, 2003.
[Sab2005] R. Olfati-Saber. Ultrafast consensus in small-world networks. American Control Conference. pp: 2371-2378, 2005.
[SBF2005] S. L. Smith, M. E. Broucke, and B. A. Francis. A hierarchical cyclic pursuit scheme for vehicle networks. Automatica. 41(6), pp: 1045-1053, 2005.
[SDM2003] R. O. Saber, W. B. Dunbar, and R. M. Murray. Cooperative control of multi-vehicle systems using cost graphs and optimization. American Control Conference. pp: 2217-2222, 2003.
[SE1998] Y. H. Shi and R. C. Eberhart. A modified particle swarm optimizer. IEEE Int. Conf on Evolutionary Computation. pp: 63-79, 1998.
[SF1999] K. Sekiyama, T. Fukuda. Toward Social Robotics. Applied Artificial Intelligence. vol. 13:213-238, 1999.
[SFM2007] R. Olfati-Saber, J. A. Fax, and R. M. Murray. Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE. 95(1): 1-17, 2007.
[SH1996] D. Swaroop and J. K. Hedrick. String stability of interconnected systems. IEEE Transaction on Automatic Control. 41(3): 349-357, 1996.
[SH2002] R. S. Smith and F. Y. Hadaegh. Control topologies for deep space formation flying spacecraft. American Control Conference. pp: 2836-2841, 2002.
[SIT~+2002] D. M. Stipanovic, G. Inalhan, R. Teo, and C. J. Tomlin. Decentralized overlapping control of a formation of unmanned aerial vehicles. 41~(st) IEEE Conf on Decision and Control. pp: 2829-2835, 2002.
[SM2003] R. Olfati-Saber and R. M. Murray. Consensus protocols for networks of dynamic agents. American Control Conference. pp: 951-956, 2003.
[SM2004] R. Olfati-Saber and R. M. Murray. Consensus problems in networks of agents with switching topology and time-delays. IEEE Transaction on Automatic Control. 49(9): 1520-1533, 2004.
[SP2002] A. Schultz and L. E. Parker, editors. Multi-Robot Systems: From Swarms to Intelligent Automata. Kluwer. 2002.
[SPR1977] G. B. Sholomitsky, O. F. Prilutsky, and V. G. Rodin. Infra-red space interferometer. 28~(th) Int. Astronautical Federation Congress. Paper IAF-77-68, 1977.
[SSM2004] C. Sultan, S. Seereeram, and R. K. Mehra. Minimization and equalization of energy for formation flying reconfiguration. IEEE Int. Conf on Robotics & Automation. pp: 4047-4052, 2004.
[SS2001] P. Stone, R.S. Sutton. Scaling reinforcement learning toward RoboCup Soccer. 8~(th) Int. Conf on Machine Learning. pp: 537-544, 2001.
[Ste1990] L. Steel. Cooperation between distributed agents through self-organizing. Decentralized Artificial Intelligence, Y. Demazeau and J.-P Muller, Eds. Amsterdam, The Netherlands: Elsevier, 1990.
[TPK2004] H. G. Tanner, G. J. Pappas, and V. Kumar. Leader-to-formation stability. IEEE Transaction on Robotics & Automation. 20(3): 1-11, 2004.
[UKK2001] C. Unsal, H.Kiliccote, & P. Khosla. A 3-D modular self-reconfigurable bipartite robotic system: Implementation and motion planning. Autonomous Robots. 10(1): 23-40, 2001.
[VCJ~+1995] T. Vicsek, A. Czirok, E. B. Jacob, I. Cohen, and O. Schochet. Novel type of phase transitions in a system of self-driven particles. Physical Review Letters. vol. 75: 1226-1229, 1995.
[VOV2003] K. Veeramachaneni, L. A. Osadciw, and P. K. Varshney. Adaptive multimodal biometric fusion algorithm using particle swarm. Proc. Of SPIE. vol. 5099:211-221, 2003.
[Wan1989] P. K. C. Wang. Navigation strategies for multiple autonomous mobile robots. IEEE/RSJ Int. Conf on Intelligent Robots and Systems (IROS). pp: 486-493, 1989.
[Wer1999] B.B. Werger. Cooperation Without Deliberation: A minimal behavior-based approach to multi-robot teams. Artificial Intelligence. vol. 110: 293-320, 1999.
[Why1986] H. F. Durrant-Whyte. Consistent integration and propagation of disparate sensor observations. IEEE Int. Conf Robotics and Automation. pp: 1464-1469, 1986.
[WKL2004] R. White, K. Kopanski, and H. Lipson. Stochastic self-reconfigurable cellular robotics. IEEE Int. Conf on Robotics & Automation. pp: 2888-2893, 2004.
[WLB1999] I. A. Wagner, M. Lindenbaum, and A. M. Bruchstein. Distributed coveting by ant-robots using evaporating traces. IEEE Transaction on Robotics & Automation. 15(5): 918-933, 1999.
[Wo11963] J. Wolfowitz. Products of indecomposable, aperiodic, stochastic Matrices. American Mathematical Society. 14(5): 733-737, 1963.
[WPT1995] J. Wang, S. Premvuti, and A. Tabbara. A wireless medium access protocol (CSMA/CD-W) for mobile robot based distributed robotic systems. IEEE Int. Conf on Robotics & Automation. pp: 2561-2566, 1995.
[WS1998] D. J. Watts and S. H. Strogatz. Collective dynamics of "small-world" networks. Nature. vol. 393: 440-442, 1998.
[WSM2002] J. Wawerla, G. S. Sukhatme,and M. J. Matarie. Collective construction with multiple robots. IEEE/RSJ Intl. Conf on intelligent robots and systems, pp: 2696-2701,2002.
[WT2000] S. Wang and M. Tan. Multi-robot cooperation and data fusion in map-building. 3~(rd) World Congress on Intelligent Control and Automation. pp: 1261-1265, 2000.
[Wu2005] C. W. Wu. Agreement and consensus problems in groups of autonomous agents with linear dynamics. IEEE Int. Symposium on Circuits and Systems. pp: 292-295, 2005.
[XB2004] L. Xiao and S. Boyd. Fast linear iterations for distributed averaging. Systems and Control Letters. pp: 1-20, 2004.
[YDR2000] M. Yim, D. G. Duff, & K. D. Roufas. Polybot: A modular reconfigurable robot. IEEE International Conference on Robotics and Automation. pp.514-520, 2000.
[曹立明等1995] 曹立明，魏兵，周强．图论及其在计算机科学中的应用．中国矿业大学出版社．徐州．1995．
[程云鹏等2001] 程云鹏，张凯院，徐仲．矩阵论．西北工业大学出版社．西安．2001．
[程磊等2006] 程磊，王永冀，朱全民．基于智能体的多移动机器人群集编队控制系统．系统工程与电子技术．28(5)：731．735，2006．
[丁滢颖2005] 丁滢颖．基于个性的多机器人协作研究[博士论文]．浙江大学．2005．
[董炀斌2007] 董炀斌．多机器人系统的协作研究[博士论文]．浙江大学．2007．
[顾国昌等2003] 顾国昌，仲宇，张汝波．一种新的多智能体强化学习算法及其在多机器人协作任务中的应用，机器人，25(4)：344-348，362，2003。
[龙晓林等2005] 龙晓林，蒋静坪．基于SVM的机器人工件识别．华中科技大学学报(自然科学版)．33(2)：41-43，2005．
[龙晓林等2005] 龙晓林，蒋静坪．基于WEB的多机器人远程控制．武汉大学学报(工学版)．38(3)：122．125，2005．
[罗志增I1998] 罗志增．机器人多感觉传感器系统与多信息融合技术[博士论文]．浙江大学．1998．
[李玉榕2001] 李玉榕．信息融合与智能处理的研究[博士论文]．浙江大学．2001．
[谭民等2002] 谭民，范永，王硕．基于遗传算法的参数优化在多移动机器人系统中的应用，自动化学报，28(4)：642-645，2002。
[谭民等2005] 谭民，王硕，曹志强．多机器人系统．清华大学出版社．北京．2005．
[席裕庚等2003] 席裕庚，陈卫东，王卫华．移动机器人地图创建中的不确定传感信息处理，自动化学报，29(2)：267-274，2003。
[杨文等2006] 杨文，汪小帆，李翔．一致性问题综述．Proc．of the 25~(th) Chinese Control Conference．PP：1491-1495，2006．
[朱晓芸等1997] 朱晓芸，杨建刚，何志钧．神经网络的多传感器神经融合基于新算法在障碍物识别中的应用．机器人．19(3)：166-171，1997．
[张学工等2000] V．Vapnik 著，张学工 译．统计学习理论的本质．清华大学出版社．北京．2000．
[AAF~+1998] H. Asama, T. Arai, T. Fukuda, and T. Hasegawa, editors. Distributed Autonomous Robotic Systems 6. Springer-Verlag. 1998.
[AKN~+1999] M. Asada, H. Kitano, I. Noda, & M. Veloso. RoboCup: Today and tomorrow - What we have learned. Artificial Intelligence. 110(2):193-214, 1999.
[AMI1989] H. Asama, A. Matsumoto, and Y. Ishida. Design of an autonomous and distributed robot system: ACTRESS. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems. pp: 283-290, 1989.
[AOM~+1992] H. Asama, K. Ozaki, A. Matsumoto, Y. Ishida, and I. Endo. Development of task assignment system using communication for multiple autonomous robots. Journal of Robotics and Mechatronics. 4(2): 122-127, 1992.
[Asa1992] H. Asama, editor. Proceedings of the Int. Symposium on Distributed Autonomous Robotic Systems. Wako, Saitama, Japan, Sept. 1992.
[BA1994] T. Balch and R. C. Arkin. Communication in reactive multiagent robotic systems. Autonomous Robots. 1(1): 1-25, 1994.
[BA1999a] T. Balch and R. C. Arkin. Behavior-based formation control for multi-robot teams. IEEE Transaction on Robotics and Automation. XX(Y): 1015, 1999.
[BA1999b] A. L. Barabasi and R. Albert. Emergence of scaling in random networks. Science, vol. 286: 509-512, 1999.
[BE2000] F. Bergh and A. P. Engelbrecbt. Cooperative learning in neural networks using particle swarm optimizers. South African Computer Journal, vol. 26: 84-90, 2000.
[BE2001] F. Bergh and A. P. Engelbrecbt. Effects of swarm size on cooperative particle swarm optimizers. Proc. of the Genetic and Evolutionary Computation Conf. pp:892-899,2001.
[Ben1988] G. Beni. The concept of cellular robot. 3~(rd) IEEE Symposium on Intelligent Control, pp: 57-61,1988.
[BH2000] T. Balch, M. Hybinette. Social potentials for scalable multi-robot formations. Proceedings of the IEEE International Conference on Robotics and Automations, pp:73-80,2000.
[BKB2004] F. Borrelli, T. Keviczky, and G. J. Balas. Collision-free UAV formation flight using decentralized optimization and invariant sets. 43~(rd) IEEE Conf. on Decision and Control, pp: 1099-1104, 2004.
[Bol2003] B. Bollobas. Modern graph theory. Beijing World Publishing Corporation. Beijing. 2003.
[Boy2006] S. Boyd. Convex optimization of graph laplacian eigenvalues. Proceedings Int. Congress of Mathematicians, vol. 3:1311-1319, 2006.
[BP2002] T. Balch and L. E. Parker, editors. Robot Teams: From Polymorphism to Polymorphism. A K Peters, Natick, Massachusetts. 2002.
[Bro1986] R. A. Brooks. Achieving artificial intelligence through building robots. MIT AI Laboratory Report. AIM-899: 1-11, May 1986.
[Bro1991] R. A. Brooks. Intelligence without representation. Artificial Intelligence. vol.47: 139-159,1991.
[BS1987] P. L. Bolger and ShaferDempster. Reasoning with application to multisensor target identification systems. IEEE Transaction on SMC. 17(6): 968-977, 1987.
[BS2003] R. W. Beard and V. Stepanyan. Information consensus in distributed multiple vehicle coordination control. IEEE Conf on Decision and Control, pp: 2029-2034, 2003.
[BT1989] D. P. Bertsekas and J. N. Tsitsiklis. Parallel and distributed computation. Prentice Hall, Englewood Cliffs. New Jersey. 1989.
[CFK1997] Y. U. Cao, A. S. Fukunaga, and A. B. Kahng. Cooperative mobile robotics: antecedents and directions. Autonomous Robots. 4(1): 7-27, 1997.
[CL2002] C. A. C. Coello and M. S. Lecbuga. MOPSO: a proposal for multiple objective particle swarm optimization. Congress on Evolutionary Computation, vol. 2: 1051-1056,2002.
[Cle1999] M. Clerc. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. IEEE Int. Conf. on Evolutionary Computation, pp: 1951-1957, 1999.
[CMB2004] J. Cortes, S. Martinez, and F. Bullo. Robust rendezvous for mobile autonomous agents via proximity graphs in arbitrary dimensions. IEEE Transactions on Automatic Control. pp: 1-11, 2004.
[CP1995] O. Causse, L. H. Pampagnin. Management of a multi-robot system in a public environment. IEEE/RSJ Int. Conf on IROS, vol. 2: 246-252, 1995.
[CSK~+2001] L. Chaimowica, T. Sugar, V. Kumar, and M.F.M. Campos. An architecture for tightly coupled multirobot cooperation. IEEE Int. Conf on Robotics & Automation. pp: 2992-2997, 2001.
[CSM2005] M. Cao, D. A. Spielman, and A. S. Morse. A lower bound on convergence of a distributed network consensus algorithm. 44~(th) IEEE Conf on Decision and Control. pp: 2356-2361, 2005.
[DJM~+1993] G. Dudek, M. Jenkin, E. Milios, and D. Wilkes. A taxonomy for swarm robots. IEEE/RSJ Int. Conf on IROS. pp: 441-447, 1993.
[DJR1994] B. R. Donald, J. Jennings, and D. Rus. Analyzing teams of cooperating mobile robots. IEEE Int. Conf on Robotics & Automation. pp: 1896-1903, 1994.
[DOK1998] J. R Desai, I. Ostrowski, and V. Kumar. Controlling formation of multiple mobile robots. IEEE Int. Conf on Robotics & Automation. pp: 2864-2869, 1998.
[Doo1934] J. L. Doob. Stochastic processes and statistics. PNAS: Mathematics. pp: 376-379, 1934.
[DSK~+2002] A. Das, J. Spletzer, V. Kumar, and C. Taylor. Ad hoe networks for localization and control. 41~(st) IEEE Conf. on Decision and Control, pp: 2978-2983, 2002.
[EH1999] R. C. Eberhart and X. Hu. Human tremor analysis using particle swarm optimization. IEEE Congress on Evolutionary Computation. pp: 1927-1930, 1999.
[EM2002] K.I. Easton and A. Martinoli. Efficiency and optimization of explicit and implicit communication schemes in collaborative robotics experiments. IEEE/RSJ Int. Conf on Intelligent Robots and Systems. pp: 2795-2800, 2002.
[ETM1996] Y. Eiichi, A. Tamio, and Y. Masakazu. Evaluating the efficiency of local and global communication in distributed mobile robotic systems. IROS 96. pp: 1661-1666, 1996.
[Fax2002] J. A. Fax. Optimal and cooperative control of vehicle formations [Dissertation]. California Institute of Technology. 2002.
[FD2003] R. Fierro and A. K. Das. Hybrid control of reconfigurable robot formations. American Control Conference, pp: 4607-4612, 2003.
[FM2001] J. Fredslund and M. J. Mataric. Robot formations using only local sensing and control. IEEE Int. Symposium on Computational Intelligent in Robotics & Automation, pp: 308-314, 2001.
[FM2002] J. Fredslund and M. J. Mataric. A general algorithm for robot formations using local sensing and minimal communication. IEEE Transaction on Robotics and Automation. 18(5): 837-846, 2002.
[FN1987] T. Fukuda, S. Nakagawa. A dynamically reconfigurable robotic system (concept of a system and optimal configurations). Proceedings of IECON. pp: 588-595, 1987.
[FNK~+1988] T. Fukuda, S. Nakagawa, Y. Kawauchi, and M. Buss. Self-organizing robots based on cell structure—CEBOT. IEEE Int. workshop on Intelligent Robots and Systems, pp: 145-150, 1988.
[GM2002] B.P. Gerkey and M.J. Matarie. Sold!: auction methods for multirobot coordination. IEEE Transaction on Robotics and Automation. 18(5): 758-768, 2002.
[GR2004] C. Godsil and G. Royle. Algebraic graph theory. Beijing World Publishing Corporation. Beijing. 2004.
[HES2003] X. Hu, R. C. Eberhart, and Y. Shi. Particle swarm with extended memory for multi-objective optimization. Proc, of IEEE Swarm Intelligence Symposium, pp: 193-197,2003.
[HIV1976] C. C. Huang, D. Isaacson, and B. Vinograde. The rate of convergence of certain nonhomogeneous markov chains. Z. Wahrscheinlichkeitstheorie verw. Gebiete. pp: 141-146,1976.
[HJS~+] K. A. Hawick, H. A. James, J. E. Story, and R. J. Sheperd. An architecture for swarm robots. Technical report DHPC-121, University of Wales, UK.
[HM2004] Y. Hatano and M. Mesbahi. Agreement of random networks. IEEE Conf. on Decision and Control. 2004.
[HS1996] T. Haynes, S. Sen. Evolving behavioral strategies in predators and prey. In Gerhard Weiβand Sandip Sen, editors, Adaptation and Learning in Multiagent Systems, pp: 113-126. Springer Verlag, Berlin, 1996.
[IH1999] S. Ichikawa and F. Hara. Characteristics of object-searching and object-fetching behaviors of multirobot system using local communication. IEEE Int. Conf on Systems, Man and Sybernetics. vol. 4: 775-781, 1999.
[Ios1972] B. M. Iosifescu. On two recent papers on ergodicity in nonhomogeneous markov chains. The Annals of Mathematical Statistics. 43(5): 1732-1736, 1972.
[Jen1993] N.R. Jennings. Commitments, conventions: the foundation of coordination in multi-agent systems. The Knowledge Engineering Review, 8(3):223-250. 1993.
[JLM2003] A. Jadbabaie, J. Lin, and A. S. Morse. Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Transaction on Automatic Control. 48(6): 988-1001, 2003.
[JSS2006] Z. Jin, T. Shima, and C. J. Schumacher. Optimal scheduling for refueling multiple autonomous aerial vehicles. IEEE Transaction on Robotics. 22(4): 682-693, 2006.
[KAG2000] J. M. Keller, S. Auephanwiriyakul, and R D. Gader. New fuzzy set tools to aid in predictive sensor fusion. SPIE 2000. 4038(Ⅱ): 1497-1507, 2000.
[Kaz2002] B. S. AI. Kazemi. Multi-phase particle swarm optimization [Dissertation]. Syracuse University. 2002.
[KE1995] J. Kennedy and R. C. Eberhart. Particle swarm optimization. IEEE Conf on Neural Networks. pp: 1942-1948, 1995.
[KE1997] J. Kennedy and R. C. A discrete binary version of the particle swarm algorithm. IEEE Conf on SMC. pp: 4104-4109, 1997.
[KFB~+2006] T. Keviczky, K. Fregene, F. Borrelli, G. J. Balas, and D. Godbole. Coordinated autonomous vehicle formations: decentralization, control synthesis and optimization. American Control Conference. pp: 2022-2027, 2006.
[KGL2005] E. Klavins, R. Ghrist, and D. Lipsky. A grammatical approach to self-organizing robotic systems. IEEE Transaction on Automatic Control. pp: 1-13, 2005.
[Kle2004] J. Kleinberg. The small-world phenomenon and decentralized search. SIAM News. 37(3): 1-2, 2004.
[Kra1997] S. Kraus. Negotiation And Cooperation In Multi-agent Environments, Artificial Intelligence. vol. 94: 79-97, 1997.
[KR2001] S. Kume and A.A. Rizzi. A high-performance network infrastructure and protocols for distributed automation. IEEE Int. Conf. on Robotics & Automation. pp: 3121-3126, 2001.
[KWY~+2004] D. H. Kim, H. O. Wang, G. Ye, and S. Shin. Decentralized control of autonomous swarm systems using artificial potential functions: analytical design guidelines. 43~(rd) IEEE Conf. on Decision and Control. pp: 159-164, 2004.
[LC2005] Yangmin Li and Xin Chen. Stability on mult-robot formation with dynamic interaction topologies. IEEE Int. Conf. on Intelligent Robots & Systems. pp: 394-399, 2005.
[LJ2004] XiaoLin Long and JiangJing Ping. Towards multirobot communication. IEEE International Conference on Robotics and Biomimetics. pp: 307-312, 2004.
[LL1994] T. C. Lueth, T. Laengle. Task description, decomposition, and allocation in a distributed autonomous multi-agent robot system. IEEE/RSJ Int. Conf. on IROS. vol. 3: 1516-1523, 1994.
[LLX2003] A. Lim, Jing Lin and Fei Xiao. Particle swarm optimization and hill climbing to solve the bandwidth minimization problem. 5~(th) Metaheuristics Int. Conference. pp: 1-6, 2003.
[LSL2002] P. Lucidarme, O. Simonin, and A. Liegeois. Implementation and evaluation of a satisfaction/altruism based architecture for multirobot systems. IEEE Int. Conf on Rrobotics & Automation. pp: 1007-1012, 2002.
[LT2002] S. Liu, Yo Tian. Multi-agent learning methods in an uncertain environment. Proceedings of the 1st Machine Learning and Cybernetics, pp:650-654, 2002
[Lyn2004] N. A. Lynch 著，舒继武，李国东，佘华山 译．分布式算法。机械工业岀版社．北京．2004．
[Mat1994] M. J. Mataric. Interaction and Intelligence Behavior [Dissertation]. MIT. 1994.
[Mat1997] M. J. Mataric. Using communication to reduce locality in distributed multi-agent learning. Autonomous Robots. 1 (4): 1-20, 1997.
[Mat2001] M. J. Mataric. Learning In Behavior-based Multi-robot Systems: policies, models, and other agents. Journal of Cognitive Systems Research. vol. 2: 81-93, 2001.
[MBC~+2006] S. Martinez, F. Bullo, J. Cortes, and E. Frazzoli. On synchronous robotic networks-part I: models, tasks and complexity. IEEE Transaction on Automatic Control. pp: 1-30, June, 2006.
[MH1999a] M. Mesbahi and F. Y. Hadaegh. A robust control approach for the formation flying of multiple spacecraft. European Control Conference. 1999.
[MH1999b] M. Mesbahi and F. Y. Hadaegh. Formation flying control of multiple spacecraft via graphs, matrix inequalities, and switching. IEEE Int. Conf on Control Applications. Aug., 1999.
[Mi11967] S. Milgram. Psychology today. Physical Review Letters. vol. 2, pp: 60-67, 1967.
[ML1988] M. Mathews and S. J. Leszkiewicz. Efficient spacecraft formation keeping with consideration of ballistic coefficient control. AIAA Aerospace Science Meeting, 1988.
[MLT~+2002] Robin R. Murphy, C. L. Lisetti, Russell Tardif, Lian Irish, and Aaron Gage. Emotion-based control of cooperating heterogeneous mobile robots. IEEE Transaction on Robotics and Automation. 18(5): 744-757, Oct. 2002
[MNS1995] M. J. Mataric, M. Nilsson, and K. T. Simsarian. Cooperative multi-robot box-pushing. IEEE/IROS-95. pp: 556-561, 1995.
[MO2004] R. Muraleedharan and L. A. Osadciw. Decision making in a building access system using swarm intelligence & POSets. 38~(th) Annual Conf on Information Sciences and Systems. 2004.
[MR2001] A. Merke, M. Riedmiller. Karlsruhe Brainstormers: A reinforcement learning way to robotic soccer Ⅱ. In A. Birk, S. Coradeschi, & S. Tadokoro, editors, RoboCup-2001: Robot Soccer World Cup V, pp:322-327, 2001.
[Nak1997] T. Nakamura. Development of self-learning vision-based mobile robots for acquiring Soccer robots behaviors. In H. Kitano, editors, RoboCup-97: Robot Soccer World Cup I, pp:257-276, Springer, 1997.
[Nor1992] F. R. Noreils. An architecture for cooperative and autonomous mobile robots. IEEE Int. Conf on Robotics andAutomation, pp: 2703-2710, 1992.
[Nor1993] F. R. Noreils. Towards a robot architecture integrating cooperation between mobile robots: Application to indoor environment. International Journal of Robotics Research. 12(1): 79-98, Feb. 1993.
[NTH~+1999] J. Neira, J. D. Tardos, J. Horn, and G. Schmidt. Fusing range and intensity images for mobile robot localization. IEEE Transaction on Robotics & Automation. 15(1): 76-83, 1999.
[Par1993a] L.E. Parker. Designing control laws for cooperative agent teams. Proc. of the IEEE Robotics and Automation Conference. pp:582-587, 1993.
[Par1994a] L. E. Parker. Heterogeneous multi-robot cooperation [Dissertation]. MIT, 1994.
[Par1994b] Lynne. E. Parker. Alliance: An architecture for fault-tolerant multi-robot cooperation. IEEE Transaction on Robotics and Automation. 14(2): 220-240, 1994.
[Par1994c] L.E. Parker. ALLIANCE: An Architecture for Fault Tolerant, Cooperative Control of Heterogeneous Mobile Robots. IEEE/RSJ/G1 Int. Conf. on Intelligent Robots and Systems. pp: 776-783, Sept. 1994.
[Par1995] J Paredis. Coevolutionary Computation. Artificial Life. pp: 355-375, 1995.
[PAM~+1999] E. Pagello, A.D. Angelo, F. Montesello, and F. Gardli. Cooperative behaviors in multirobot systems through implicit communication. Robotics and Autonomous Systems. vol. 29: 65-77, 1999.
[PPC~+2002] G. A. S. Pereira, B. S. Pimentel, L. Chaimowicz, and M.F.M. Campos. Coordination of multiple mobile robots in an object carrying task using implicit communication. IEEE Int. Conf. on Robotics & Automation. pp: 281-286, 2002.
[PSH2002] A. Pant, P. Seiler, and K. Hedrick. Mesh stability of look-ahead interconnected systems. IEEE Transaction on Automatic Control. 47(2): 403-407, 2002.
[PY1990] S. Premvuti and S. Yuta. Consideration on the cooperation of multiple autonomous mobile robots. IEEE International Workshop of Intelligent Robots and Systems. pp: 59-63, 1990.
[PV2002] K. E. Parsopoulos and M. N. Vrahatis. Recent approaches to global optimization problems through particle swarm optimization. Natural Computing. 1(2-3): 235-306, 2002.
[RBA2005] W. Ren, R. W. Beard, and E. M. Atkins. A survey of consensus problems in multi-agent coordination. American Control Conference. pp: 1859-1864, 2005.
[RBM2004] W. Ren, W. Beard, and T. W. McLain. Coordination variables and consensus building in multiple vehicle systems. Cooperative Control, LNCIS 309. pp: 171-188, 2004.
[RDJ1995] D. Rus, B. Donald, and J. Jennings. Moving furniture with teams of autonomous robots. IEEE/RSJ Conf on Intelligent Robots and Systems. pp: 235-242, 1995.
[Rey1987] C. Reynolds. Flocks, herds and schools: A distributed behavioral model. Computer Graphics. 21 (4):25-34, 1987.
[RVV2003] M. Roth, D. Vail, and M. Veloso. A real-time world model for multi-robot teams with high-latency communication. IEEE/RSJ Int. Conf on Intelligent Robots and Systems. pp: 2494-2499, 2003.
[Sab2005] R. Olfati-Saber. Ultrafast consensus in small-world networks. American Control Conference. pp: 2371-2378, 2005.
[SBF2005] S. L. Smith, M. E. Broucke, and B. A. Francis. A hierarchical cyclic pursuit scheme for vehicle networks. Automatica. 41(6), pp: 1045-1053, 2005.
[SDM2003] R. O. Saber, W. B. Dunbar, and R. M. Murray. Cooperative control of multi-vehicle systems using cost graphs and optimization. American Control Conference. pp: 2217-2222, 2003.
[SE1998] Y. H. Shi and R. C. Eberhart. A modified particle swarm optimizer. IEEE Int. Conf on Evolutionary Computation. pp: 63-79, 1998.
[SF1999] K. Sekiyama, T. Fukuda. Toward Social Robotics. Applied Artificial Intelligence. vol. 13:213-238, 1999.
[SFM2007] R. Olfati-Saber, J. A. Fax, and R. M. Murray. Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE. 95(1): 1-17, 2007.
[SH1996] D. Swaroop and J. K. Hedrick. String stability of interconnected systems. IEEE Transaction on Automatic Control. 41(3): 349-357, 1996.
[SH2002] R. S. Smith and F. Y. Hadaegh. Control topologies for deep space formation flying spacecraft. American Control Conference. pp: 2836-2841, 2002.
[SIT~+2002] D. M. Stipanovic, G. Inalhan, R. Teo, and C. J. Tomlin. Decentralized overlapping control of a formation of unmanned aerial vehicles. 41~(st) IEEE Conf on Decision and Control. pp: 2829-2835, 2002.
[SM2003] R. Olfati-Saber and R. M. Murray. Consensus protocols for networks of dynamic agents. American Control Conference. pp: 951-956, 2003.
[SM2004] R. Olfati-Saber and R. M. Murray. Consensus problems in networks of agents with switching topology and time-delays. IEEE Transaction on Automatic Control. 49(9): 1520-1533, 2004.
[SP2002] A. Schultz and L. E. Parker, editors. Multi-Robot Systems: From Swarms to Intelligent Automata. Kluwer. 2002.
[SPR1977] G. B. Sholomitsky, O. F. Prilutsky, and V. G. Rodin. Infra-red space interferometer. 28~(th) Int. Astronautical Federation Congress. Paper IAF-77-68, 1977.
[SSM2004] C. Sultan, S. Seereeram, and R. K. Mehra. Minimization and equalization of energy for formation flying reconfiguration. IEEE Int. Conf on Robotics & Automation. pp: 4047-4052, 2004.
[SS2001] P. Stone, R.S. Sutton. Scaling reinforcement learning toward RoboCup Soccer. 8~(th) Int. Conf on Machine Learning. pp: 537-544, 2001.
[Ste1990] L. Steel. Cooperation between distributed agents through self-organizing. Decentralized Artificial Intelligence, Y. Demazeau and J.-P Muller, Eds. Amsterdam, The Netherlands: Elsevier, 1990.
[TPK2004] H. G. Tanner, G. J. Pappas, and V. Kumar. Leader-to-formation stability. IEEE Transaction on Robotics & Automation. 20(3): 1-11, 2004.
[UKK2001] C. Unsal, H.Kiliccote, & P. Khosla. A 3-D modular self-reconfigurable bipartite robotic system: Implementation and motion planning. Autonomous Robots. 10(1): 23-40, 2001.
[VCJ~+1995] T. Vicsek, A. Czirok, E. B. Jacob, I. Cohen, and O. Schochet. Novel type of phase transitions in a system of self-driven particles. Physical Review Letters. vol. 75: 1226-1229, 1995.
[VOV2003] K. Veeramachaneni, L. A. Osadciw, and P. K. Varshney. Adaptive multimodal biometric fusion algorithm using particle swarm. Proc. Of SPIE. vol. 5099:211-221, 2003.
[Wan1989] P. K. C. Wang. Navigation strategies for multiple autonomous mobile robots. IEEE/RSJ Int. Conf on Intelligent Robots and Systems (IROS). pp: 486-493, 1989.
[Wer1999] B.B. Werger. Cooperation Without Deliberation: A minimal behavior-based approach to multi-robot teams. Artificial Intelligence. vol. 110: 293-320, 1999.
[Why1986] H. F. Durrant-Whyte. Consistent integration and propagation of disparate sensor observations. IEEE Int. Conf Robotics and Automation. pp: 1464-1469, 1986.
[WKL2004] R. White, K. Kopanski, and H. Lipson. Stochastic self-reconfigurable cellular robotics. IEEE Int. Conf on Robotics & Automation. pp: 2888-2893, 2004.
[WLB1999] I. A. Wagner, M. Lindenbaum, and A. M. Bruchstein. Distributed coveting by ant-robots using evaporating traces. IEEE Transaction on Robotics & Automation. 15(5): 918-933, 1999.
[Wo11963] J. Wolfowitz. Products of indecomposable, aperiodic, stochastic Matrices. American Mathematical Society. 14(5): 733-737, 1963.
[WPT1995] J. Wang, S. Premvuti, and A. Tabbara. A wireless medium access protocol (CSMA/CD-W) for mobile robot based distributed robotic systems. IEEE Int. Conf on Robotics & Automation. pp: 2561-2566, 1995.
[WS1998] D. J. Watts and S. H. Strogatz. Collective dynamics of "small-world" networks. Nature. vol. 393: 440-442, 1998.
[WSM2002] J. Wawerla, G. S. Sukhatme,and M. J. Matarie. Collective construction with multiple robots. IEEE/RSJ Intl. Conf on intelligent robots and systems, pp: 2696-2701,2002.
[WT2000] S. Wang and M. Tan. Multi-robot cooperation and data fusion in map-building. 3~(rd) World Congress on Intelligent Control and Automation. pp: 1261-1265, 2000.
[Wu2005] C. W. Wu. Agreement and consensus problems in groups of autonomous agents with linear dynamics. IEEE Int. Symposium on Circuits and Systems. pp: 292-295, 2005.
[XB2004] L. Xiao and S. Boyd. Fast linear iterations for distributed averaging. Systems and Control Letters. pp: 1-20, 2004.
[YDR2000] M. Yim, D. G. Duff, & K. D. Roufas. Polybot: A modular reconfigurable robot. IEEE International Conference on Robotics and Automation. pp.514-520, 2000.
[曹立明等1995] 曹立明，魏兵，周强．图论及其在计算机科学中的应用．中国矿业大学出版社．徐州．1995．
[程云鹏等2001] 程云鹏，张凯院，徐仲．矩阵论．西北工业大学出版社．西安．2001．
[程磊等2006] 程磊，王永冀，朱全民．基于智能体的多移动机器人群集编队控制系统．系统工程与电子技术．28(5)：731．735，2006．
[丁滢颖2005] 丁滢颖．基于个性的多机器人协作研究[博士论文]．浙江大学．2005．
[董炀斌2007] 董炀斌．多机器人系统的协作研究[博士论文]．浙江大学．2007．
[顾国昌等2003] 顾国昌，仲宇，张汝波．一种新的多智能体强化学习算法及其在多机器人协作任务中的应用，机器人，25(4)：344-348，362，2003。
[龙晓林等2005] 龙晓林，蒋静坪．基于SVM的机器人工件识别．华中科技大学学报(自然科学版)．33(2)：41-43，2005．
[龙晓林等2005] 龙晓林，蒋静坪．基于WEB的多机器人远程控制．武汉大学学报(工学版)．38(3)：122．125，2005．
[罗志增I1998] 罗志增．机器人多感觉传感器系统与多信息融合技术[博士论文]．浙江大学．1998．
[李玉榕2001] 李玉榕．信息融合与智能处理的研究[博士论文]．浙江大学．2001．
[谭民等2002] 谭民，范永，王硕．基于遗传算法的参数优化在多移动机器人系统中的应用，自动化学报，28(4)：642-645，2002。
[谭民等2005] 谭民，王硕，曹志强．多机器人系统．清华大学出版社．北京．2005．
[席裕庚等2003] 席裕庚，陈卫东，王卫华．移动机器人地图创建中的不确定传感信息处理，自动化学报，29(2)：267-274，2003。
[杨文等2006] 杨文，汪小帆，李翔．一致性问题综述．Proc．of the 25~(th) Chinese Control Conference．PP：1491-1495，2006．
[朱晓芸等1997] 朱晓芸，杨建刚，何志钧．神经网络的多传感器神经融合基于新算法在障碍物识别中的应用．机器人．19(3)：166-171，1997．
[张学工等2000] V．Vapnik 著，张学工 译．统计学习理论的本质．清华大学出版社．北京．2000．