无线传感器/执行器网络中目标捕获的控制策略研究
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摘要
随着电子通信技术、计算机技术和自动控制技术的迅猛发展，融合了信息感知、信息处理和信息传输于一体的无线传感器网络(Wireless Sensor Networks, WSN)逐步进入人们的视野。WSN通过传感器节点(Sensor Node，SN)进行无线组网，实现了对物理环境信息的采集和传播工作。但在森林火险检测和地震灾后救援等应用场合，不仅需要通过SN来感知环境的变化，还需要传达紧急事件信息，通过控制执行器设备及时地采取行动来处理事件。根据这种需求，近年来发展迅速的无线传感器执行器网络(Wireless Sensor and Actor Networks，WSAN)有望为监测监控类和控制类应用提供必要支撑。
学术界和工业界对WSAN的巨大前景极为重视，并投入了巨大的人力和物力进行科学研究。目标捕获控制作为WSAN中的基本应用之一，一直是研究者重点研究的对象。现有研究往往针对传感器—传感器协作与传感器—执行器协作展开的，以此来研究数据的可靠性传输。然而，除了数据的传输可靠性，执行器节点间的协作控制对目标捕获过程同样重要。事实上，执行器节点能否完成对目标的捕获控制将最终决定WSAN整个协作过程的成败。基于此，本文结合无线传感器网络和多自主体系统(Multi-agent systems，MAS)现有的研究成果，开展WSAN中目标捕获控制策略研究。本文的研究工作有如下几个方面：
(1)针对事件包传输与多目标捕获控制问题，提出了基于虚拟Agent的WSAN模型，设计了Ballooning协议，保证了事件报告能够在有限的时延之内传输到执行器节点。并在此基础上，提出了多目标分配策略，设计了基于Leader-following的自适应捕获算法，以提高WSAN执行多目标的能力。
(2)针对执行器节点对静态与动态目标的捕获控制问题，设计了基于滑模变结构方法的目标捕获策略。为了减少执行器节点间在协作过程中通信能耗，采用基于雏菊链(Daisy-Chain)的目标捕获算法，使执行器节点在维持较少通信消耗的同时，能够对不同类型目标进行捕获。
(3)针对静态障碍物情形下的目标捕获控制问题，设计了基于预测控制方法的目标捕获策略，将避碰的条件转化为性能指标的一部分，提出了基于滚动优化的预测控制算法，使得执行器节点能够根据在线感知的信息动态调整路径。最后，通过设定终点状态控制器，给出了保证系统稳定的条件。
(4)针对动态障碍物情形下的目标捕获控制问题，根据执行器节点是否处于邻居执行器节点的避障区域，设计了内场与外场两类势能函数，进而设计相对应的控制算法，使执行器节点在捕获目标过程中，能够躲避环境中存在的动态障碍物以及邻居执行器节点。
(5)针对主从遥操作系统下的WSAN目标捕获问题，提出了一种动态量化器，使得主端执行器节点与从端执行器节点信号在输出前得到量化。设计了主/从端目标捕获控制律，并将网络中存在的时变时延考虑在控制协议中，使得主端执行器节点能够在时延与量化器下能够追踪到从端执行器节点的编队捕获位置中心，而从端执行器节点能够根据主端执行器节点位置形成期望编队捕获控制。
With rapid development of electronic communication, computing and automaticcontrol technologies, Wireless Sensor Networks (WSN), which is integrated with thecapacities of information sensing, processing and transmission, begins to emerge. WSNperforms the wireless networking through sensor node (SN), and then realizes theinformation collection and transparency for physical environment. However, in manyapplications, such as forest fire danger detection and earthquake disasters rescue，it notonly needs SN to sense the environment conditions, but also needs SN to transmitemergency information. Then, event can be timely managed by controlling actor nodes.According to this demand, Wireless Sensor and Actor Networks (WSAN), which hasdeveloped quickly in recent years, may provide essential support for monitoring theapplications in surveillance and controlling fields.
Attracted by its tremendous potential, many researchers from the academic andindustry communities have already devoted huge efforts to the research of WSAN. As oneof the fundamental applications in WSAN, target capturing control has been a hot andchallenging research topic. Most of existing works aim at dealing with the coordinationrelationship between sensor-sensor and sensor-actor coordinations, and then investigatethe reliability of data transport. However, apart from the reliability of data transport, thecoordination between actors is also important to the target capturing process. In fact, thesuccess of the whole coordination for WSAN directly hinges on the target capturingcontrol by actors. Based on this, this thesis studies the researches in wireless and actornetwork and multi-agent systems (MAS), with the goal of investigating the targetcapturing control in WSAN. The main research works are summarized as follows:
(1) For the event report transmission and multi-target capturing problems, a virtualagent-based WSAN model is proposed, and then a Ballooning protocol is designed whichis used to guarantee the event report can be transmitted to actor nodes within a boundedtime delay. Based on the above designs, a multi-target choosing strategy is provided, and aLeader-following based adaptive capturing algorithm is proposed such that the ability of implementing multi-target tasks for WSAN can be improved.
(2) For the static and dynamic target capturing problems, a sliding mode variablestructure-based target capturing strategy is proposed. To save the communication energy inactor nodes during the coordination process, a Daisy-Chain formation algorithm isdesigned. Then, all actor nodes can successfully capture the different types of target whilemaintaining less communication cost.
(3) For the target capturing problem in static obstacle environment, a recedinghorizon control-based target capturing strategy is proposed. Information on obstacles isincorporated online as a part of the cost function. Then, a receding control algorithm isproposed, such that actor nodes can adjust their positions according the sensed information.The stability is guaranteed by adding a terminal-state penalty to the cost function.
(4) For the target capturing problem in dynamic obstacle environment, two kinds ofpotential function are presented by according to the state whether an actor is within thearea of its neighbors’ influence. Correspondingly, the control algorithm is provided, whoseaim is to make the actor nodes track a moving target, while avoiding collision with amoving obstacle and other actor nodes.
(5) For the WSAN target capturing task in the master-slave teleoperation system, adynamic quantization strategy is provided, where the output signals of master actor nodeand slave actor nodes are quantized before being transmitted. Then, a novel master-slavetarget capturing protocol is designed, such that the master actor can reach to the formationcenter of slave actor nodes, while slave actor nodes can keep a desired formation controlaccording to the position of master actor node.
学术界和工业界对WSAN的巨大前景极为重视，并投入了巨大的人力和物力进行科学研究。目标捕获控制作为WSAN中的基本应用之一，一直是研究者重点研究的对象。现有研究往往针对传感器—传感器协作与传感器—执行器协作展开的，以此来研究数据的可靠性传输。然而，除了数据的传输可靠性，执行器节点间的协作控制对目标捕获过程同样重要。事实上，执行器节点能否完成对目标的捕获控制将最终决定WSAN整个协作过程的成败。基于此，本文结合无线传感器网络和多自主体系统(Multi-agent systems，MAS)现有的研究成果，开展WSAN中目标捕获控制策略研究。本文的研究工作有如下几个方面：
(1)针对事件包传输与多目标捕获控制问题，提出了基于虚拟Agent的WSAN模型，设计了Ballooning协议，保证了事件报告能够在有限的时延之内传输到执行器节点。并在此基础上，提出了多目标分配策略，设计了基于Leader-following的自适应捕获算法，以提高WSAN执行多目标的能力。
(2)针对执行器节点对静态与动态目标的捕获控制问题，设计了基于滑模变结构方法的目标捕获策略。为了减少执行器节点间在协作过程中通信能耗，采用基于雏菊链(Daisy-Chain)的目标捕获算法，使执行器节点在维持较少通信消耗的同时，能够对不同类型目标进行捕获。
(3)针对静态障碍物情形下的目标捕获控制问题，设计了基于预测控制方法的目标捕获策略，将避碰的条件转化为性能指标的一部分，提出了基于滚动优化的预测控制算法，使得执行器节点能够根据在线感知的信息动态调整路径。最后，通过设定终点状态控制器，给出了保证系统稳定的条件。
(4)针对动态障碍物情形下的目标捕获控制问题，根据执行器节点是否处于邻居执行器节点的避障区域，设计了内场与外场两类势能函数，进而设计相对应的控制算法，使执行器节点在捕获目标过程中，能够躲避环境中存在的动态障碍物以及邻居执行器节点。
(5)针对主从遥操作系统下的WSAN目标捕获问题，提出了一种动态量化器，使得主端执行器节点与从端执行器节点信号在输出前得到量化。设计了主/从端目标捕获控制律，并将网络中存在的时变时延考虑在控制协议中，使得主端执行器节点能够在时延与量化器下能够追踪到从端执行器节点的编队捕获位置中心，而从端执行器节点能够根据主端执行器节点位置形成期望编队捕获控制。
With rapid development of electronic communication, computing and automaticcontrol technologies, Wireless Sensor Networks (WSN), which is integrated with thecapacities of information sensing, processing and transmission, begins to emerge. WSNperforms the wireless networking through sensor node (SN), and then realizes theinformation collection and transparency for physical environment. However, in manyapplications, such as forest fire danger detection and earthquake disasters rescue，it notonly needs SN to sense the environment conditions, but also needs SN to transmitemergency information. Then, event can be timely managed by controlling actor nodes.According to this demand, Wireless Sensor and Actor Networks (WSAN), which hasdeveloped quickly in recent years, may provide essential support for monitoring theapplications in surveillance and controlling fields.
Attracted by its tremendous potential, many researchers from the academic andindustry communities have already devoted huge efforts to the research of WSAN. As oneof the fundamental applications in WSAN, target capturing control has been a hot andchallenging research topic. Most of existing works aim at dealing with the coordinationrelationship between sensor-sensor and sensor-actor coordinations, and then investigatethe reliability of data transport. However, apart from the reliability of data transport, thecoordination between actors is also important to the target capturing process. In fact, thesuccess of the whole coordination for WSAN directly hinges on the target capturingcontrol by actors. Based on this, this thesis studies the researches in wireless and actornetwork and multi-agent systems (MAS), with the goal of investigating the targetcapturing control in WSAN. The main research works are summarized as follows:
(1) For the event report transmission and multi-target capturing problems, a virtualagent-based WSAN model is proposed, and then a Ballooning protocol is designed whichis used to guarantee the event report can be transmitted to actor nodes within a boundedtime delay. Based on the above designs, a multi-target choosing strategy is provided, and aLeader-following based adaptive capturing algorithm is proposed such that the ability of implementing multi-target tasks for WSAN can be improved.
(2) For the static and dynamic target capturing problems, a sliding mode variablestructure-based target capturing strategy is proposed. To save the communication energy inactor nodes during the coordination process, a Daisy-Chain formation algorithm isdesigned. Then, all actor nodes can successfully capture the different types of target whilemaintaining less communication cost.
(3) For the target capturing problem in static obstacle environment, a recedinghorizon control-based target capturing strategy is proposed. Information on obstacles isincorporated online as a part of the cost function. Then, a receding control algorithm isproposed, such that actor nodes can adjust their positions according the sensed information.The stability is guaranteed by adding a terminal-state penalty to the cost function.
(4) For the target capturing problem in dynamic obstacle environment, two kinds ofpotential function are presented by according to the state whether an actor is within thearea of its neighbors’ influence. Correspondingly, the control algorithm is provided, whoseaim is to make the actor nodes track a moving target, while avoiding collision with amoving obstacle and other actor nodes.
(5) For the WSAN target capturing task in the master-slave teleoperation system, adynamic quantization strategy is provided, where the output signals of master actor nodeand slave actor nodes are quantized before being transmitted. Then, a novel master-slavetarget capturing protocol is designed, such that the master actor can reach to the formationcenter of slave actor nodes, while slave actor nodes can keep a desired formation controlaccording to the position of master actor node.
引文
[1]贺诗波.无线传感器网络覆盖理论与资源优化研究[D].杭州：浙江大学博士学位论文，2012.
[2] Shi L, Cheng P, Chen J M. Optimal periodic sensor scheduling with limited resources [J]. IEEETransactions on Automatic Control,2011,56(9):2190-2195.
[3] Younis M, Akkaya K. Strategies and techniques for node placement in wireless sensor networks:A survey[J]. Ad Hoc Networks,2008,6(4):621-655.
[4] He S B, Chen J M, Sun Y X. Coverage and connectivity in duty-cycled wireless sensor networkfor event monitoring[J]. IEEE Transactions on Parallel and Distributed Systems,2012,23(3):475-482.
[5]刘云浩.物联网导论[M].北京：科学出版社，2010.
[6] Haenggi M. Mobile sensor-actuator networks: Opportunities and challenges[C]. in Proceedings ofIEEE International Workshop on Cellular Neural Networks and Their Applications, Frankfurt,Germany,2002,283-291.
[7] Akyildiz I F, Kasimoglu I H. Wireless sensor and actor networks: Research challenges [J]. AdHoc Networks,2004,2(4):351-367.
[8] Akkaya K, Janapala S. Maximizing connected coverage via controlled actor relocation in wirelesssensor and actor networks[J]. Computer Networks,2008,52(14):2779-2796.
[9] Akkaya K, Senel F, McLaughlan B. Clustering of wireless sensor and actor networks based onsensor distribution and connectivity[J]. Journal of Parallel and Distributed Computing,2009,69(6):573-587.
[10] Zheng D W. Modeling and analysis of strategies in the design of WSAN coordination systems[J].Journal of Parallel and Distributed Computing,2011,71(7):1055-1064.
[11] Melodia T, Pompili D, Gungor V C, et al. A distributed coordination framework for wirelesssensor and actor networks[C]. in Proceedings of ACM Mobihoc, Urbana-Champaign, USA,2005,99-110.
[12]徐宏勤.面向室内环境监控的无线传感器/执行器网络路由协议[D].杭州:浙江大学硕士学位论文，2008.
[13] Macwan A, Nejat G, Benhabib B. Target-motion prediction for robotic search and rescue inwilderness environments[J]. IEEE Transactions on Systems, Man and Cybernetics-B,2011,41(5):1287-1298.
[14]徐哲壮.面向应急救援的无线传感器/执行器网络传输协议研究[D].上海：上海交通大学博士学位论文,2012.
[15]国家中长期科学和技术发展规划纲要(2006-2020年)[R].北京:中华人民共和国国务院,2006.
[16] Chesi G. Designing image trajectories in the presence of uncertain data for robust visual servingpath-planning[C]. in Proceedings of IEEE International Conference on Robotics and Automation,Kobe, Japan,2009,1492-1497.
[17]汶川地震救灾救援工作研究报告[R].北京：民政部国家减灾中心,2009.
[18] Melodia T, Pompili D, Gungor V C, et al. Communication and coordination in wireless sensorand actor networks[J]. IEEE Transactions on Mobile Computing,2007,6(10):1116-1129.
[19] Gungor V C, Akan O B, Akyildiz I F. A real-time and reliable transport (RT) protocol forwireless Sensor and Actor Networks[J]. IEEE/ACM Transactions on Networking,2008,16(2):359-370.
[20] Obraczka K, Viswanath K, Tsudik G. Flooding for reliable multicast in multi-hop ad hocnetworks[J]. Wireless Networks,2001,7(6):627-634.
[21] Chang N, Liu M. Controlled flooding search in a large network[J]. IEEE/ACM Transactions onNetworking,2007,15(2):436-449.
[22] Hassan J, Jha S. Optimising expanding ring search for multi-hop wireless networks[C]. inProceedings of IEEE Global Telecommunications Conference, Dallas, USA,2004,1061-1065.
[23] Huang H, Hartman J, Hurst T. Data-centric routing in sensor networks using biased walk[C]. inProceedings of IEEE Communications Society on Sensor and Ad Hoc Communications andNetworks, Reston, USA,2006,1-9.
[24] Lima L, Barros J. Random walks on sensor networks[C]. in Proceedings of the InternationalSymposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks andWorkshops, Limassol, Cyprus,2007,1-5.
[25] Lv Q, Cao P, Cohen E, et al. Search and replication in unstructured peer-topeer networks[C]. inProceedings of the International Conference on Supercomputing, New York, USA,2002,84-95.
[26] Rachuri K, Murthy C. Energy efficient and scalable search in dense wireless sensor networks[J].IEEE Transactions on Computing,2009,58(6):812-826.
[27] Chen X H, Zhong Q, Liu D P. An improved MAODV protocol based on mobility prediction andself-pruning flooding[C]. in Proceedings of the International Conference on Communicationsand Mobile Computing, Kunming, PRC,2009,127-131.
[28] Sabbineni H, Chakrabarty K. Location-aided flooding: an energy-efficient data disseminationprotocol for wireless-sensor networks[J]. IEEE Transactions on Computers,2005,54(1):36-46.
[29] Bellur B, Ogier R G. A reliable, efficient topology broadcast protocol for dynamic networks[C].in Proceedings of IEEE INFOCOM, New York, USA,1999,178-186.
[30] Im J H, Wu H, Lee D J. Optimal cluster-based routing scheme using node mobility in ad hocnetworks[J]. Wireless networks,2011,17(4):921-935.
[31] Ren W, Beard R W. Distributed consensus in multi-vehicle cooperative control[M]. London:Springer-Verlag,2008.
[32] Pettersen K Y, Gravdahl J T, Nijmeijer H. Group coordination and cooperative control[M].Berlin: Springer-Verlag,2006.
[33] Guzzella L. Automobiles of the future and the role of automatic control in those systems[J].Annual Reviews in Control,2009,33(1):1-10.
[34] Minsky M. The society of mind[M]. New York: Simon and Schuster,1986.
[35] Ren W. Formation keeping and attitude alignment for multiple spacecraft through localinteractions[J]. AIAA Journal of Guidance, Control and Dynamics,2007,30(2):633-638.
[36] Zhu S Y, Chen C L, Li W S, et al. Distributed optimal consensus filter for target tracking inheterogeneous sensor networks[J]. IEEE Transactions on Systems, Man and Cybernetics-B,2013,1-14, in press.
[37] Macwan A, Nejat G, Benhabib B. Target-motion prediction for robotic search and rescue inwilderness environments[J]. IEEE Transactions on Systems, Man and Cybernetics-B,2011,41(5):1287-1298.
[38] An S, Cui J X, Li L V. Agent-based approach to model commuter behaviour’s day-to-daydynamics under pre-trip information[J]. IET Intelligent Transport Systemss,2011,5(1):70-79.
[39] Reynolds C W. Flocks, herds, and schools: A distributed behavioral model[J]. Computer Graph,1987,21(4):25-34.
[40] Vicsek T, Czirók A, Jacob E, et al. Novel type of phase transition in a system of self-drivenparticles[J]. Physical Review Letters,1995,75(6):1226-1229.
[41] Jadbabaie A, Lin J, Morse A S. Coordination of groups of mobile autonomous agents usingnearest neighbor rules[J]. IEEE Transactions on Automatic Control,2003,48(6):988-1001
[42] Olfati-Saber R, Murray R. M. Consensu problem in networks of agents with switching topologyand time-delays[J]. IEEE Transactions on Automatic Control,2004,49(9):1520-1533.
[43] Olfati-Saber R, Alex Fax J, Murray R. M. Consensu and cooperation in networked multi-agentsysytems[J]. Proceedings of the IEEE,2007,95(1):215-233.
[44] Tian Y P, Liu C L. Robust consensus of multi-agent systems with diverse input delays andasymmetric interconnection perturbations[J]. Automatica,2009,45(5):1347-1353.
[45] Hou Z G, Cheng L, Tian M. Decentralized robust adaptive control for the multi-agent systemconsensus problem using neural networks[J]. IEEE Transactions on Systems, Man andCybernetics-part B: Cybernetics,2009,39(3):636-647.
[46] Xiao F, Wang L. Asynchronous consensus in continuous-time multi-agent systems withswitching topology and time-varying delays[J]. IEEE Transactions on Automatic Control,2008,53(8):1804-1816.
[47] Zheng Y S, Wang L. Consensus of of the heterogeneous multi-agent systems without velocitymeasurements[J]. International Journal of Control,2012,85(7):906-917.
[48] Zhu S Y, Chen C L, Guan X P. Consensus protocol for heterogeneous multi-agent systems: Amakov chain approach[J]. Chinese Physics B,2013,22(1):018901-1-5.
[49] Hu J P, Hong Y G. Leader-following coordination of multi-agent systems with coupling timedelays[J]. Physica A,2007,374(2):853-863.
[50] Ni W, Cheng D Z. Leader-following consensus of multi-agent systems under fixed and switchingtopologies[J]. System and Control Letters,2010,59(3-4):209-217.
[51] Sun F L, Guan Z H. Finite-time consensus for Leader-following second-order multi-agentsystem[J]. Journal of Systems Science,2013,44(4):727-738.
[52] Ding L, Han Q L, Guo G. Network-based Leader-following consensus for distributedmulti-agent systems[J]. Automatica,2013,49(7):2281-2286.
[53] Khatib O. Real obstacle avoidance for manipulators and mobile robots[J]. International Journalof Robotics Research,1986,5(1):90-98.
[54] Olfati-Saber R. Flocking for multi-agent dynamic systems: Algorithms and theory[J]. IEEETransactions on Automatic Control,2006,51(3):401-420.
[55] Gu D B, Wang Z Y. Leader-Follower flocking: Algorithms and experiments[J]. IEEETransactions on Control Systems Technology,2009,17(5):1211-1219.
[56] Luo X Y, Liu D, Guan X P, et al. Flocking in target pursuit for multi-agent systems with partialinformed agents[J]. IET Control Theory and Applications,2012,6(4):560-569.
[57] Consolini L, Morbidi F, Prattichizzo D, et al. Leader-Follower formation control ofnonholonomic mobile robots with input constraints[J]. Automatica,2008,44(5):1343-1349.
[58] Li Q, Jiang Z P. Formation constrained multi-agent system in unknown environments[C]. inProceedings of IEEE International Conference on Robotics and Automation, Taipei, PRC,2003,735-740.
[59] Lawton J R, Beard R W, Young B J. A decentralized approach to formation maneuvers[J]. IEEETransactions on Robotics and Automation,2003,19(6):933-941.
[60] Semsar-Kazerooni E, Khorasani K. Multi-agent team cooperation: A game theory approach[J].Automatica,45(10):2205-2213,2009.
[61] Keviczky T, Borrelli F, Fregene K. Decentralized receding horizon control and coordination ofautonomous vehicle formations[J]. IEEE Transactions on Intelligent Transportation System,2008,16(1):19-33.
[62] Lin Z Y, Broucke M, Francis B. Local control strategies for groups of mobile autonomousagents[J]. IEEE Transactions on Automatic Control,2004,49(4):622-629.
[63] Leonard N E, Fiorelli E. Virtual Leaders, artificial potentials and coordinated control ofgroups[C]. in Proceedings of IEEE Conference on Decision and Control, Orlando, USA,2001,2968-2973.
[64] Alexander Fax J, Murray R M. Information flow and cooperative control of vehicleformations[J]. IEEE Transactions on Automatic Control,2004,49(9):1465-1476.
[65] Chen F, Chen Z Q, Liu Z X, et al. Decentralized formation control of mobile agents: A unifiedframework[J]. Physica A,2008,387(19-20):4917-4926.
[66] Dierks T, Jagannathan S. Neural network output feedback control of robot formations[J]. IEEETransactions on Systems, Man and Cybernetics-part B: Cybernetics,2010,40(2):383-399.
[67] Smith B, Egerstedt M, Howard A. Automatic generation of persistent formations for multi-agentnetworks under range constraints[J]. Mobile Networks and Applications,2009,14(3):322-335.
[68] Hendrickx J M, Fidan B, Yu C, et al. Formation reorganization by primitive operations ondirected graphs[J]. IEEE Transaction on Automatic Control,2008,4(53):968-979.
[69] Luo X Y, Yan Y L, Li S B, et al. Topology control based on optimally rigid graph in wirelesssensor networks[J]. Computer Networks,2013,57(4):1037-1047.
[70] Lee G, Chong N Y. Decentralized formation control for small-scale robot teams withanonymity[J]. Mechatronics,2009,19(1):85-105.
[71] Nguyen D B, Do K D. Formation control of mobile robots[J]. International Journal ofComputers, Communications and Control,2006,1(3):41-59.
[72] Marshall J A, Fung T, Broueke M E. Experiments in multirobot coordination[J]. Robotics andAutonomous Systems,2006,54(3):265-275.
[73] Marshall J A, Broucke M E, Francis B. Pursuit formations of unicycles[J]. Automatica,2006,42(1):3-12.
[74] Lin Z Y, Ding W, Yan G F, et al. Leader-Follower formation via complex Laplacian[J].Automatica,2013,49(6):1900-1906.
[75] Zheng R H, Lin Z Y, Yan G F. Ring-coupled unicycles: Boundedness, convergence, andcontrol[J]. Automatica,2009,45(11):2699-2706.
[76] Ding W, Yan G F, Lin Z Y. Collective motions and formations under pursuit strategies ondirected acyclic graphs[J]. Automatica,2010,46(1):174-181.
[77] Xiao F, Wang L, Chen J, et al. Finite-time formation control for multi-agent systems[J].Automatica,2009,45(11):2605-2611.
[78] Jiang F C, Wang L. Finite-time weighted average consensus with respect to a monotonicfunction and its application[J]. System and Control Letter,2011,60(9):718-725.
[79] Li S H, Du H B, Lin X Z. Finite-time consensus algorithm for multi-agent systems withdouble-integrator dynamics[J]. Automatica,2011,47(8):1706-1712.
[80] Couzin I D, Krause J, Franks N R, et al. Effective Leadership and decision-making in animalgroups on the move[J]. Nature,2005,433(7025):513-516.
[81] Morbidi F, Mariottini G, Prattichizzo D. Observer design via immersion and invariance forvision-based Leader-Follower formation control[J]. Automatica,2010,46(1):148-154.
[82] Lewis M A, Tan K H. High precision formation control of mobile robots using virtualstructures[J]. Autonomous Robots,1997,4(4):387-403.
[83] Balch T, Arkin R C. Behavior-based formation control for multirobot teams[J]. IEEETransactions on Robotics and Automation,1999,14(6):926-939.
[84] Stipanovic D, Inalhan G, Teo R, et al. Decentralized overlapping control of a formation ofunmanned aerial vehicles[J]. Automatica,2004,40(8):1285-1296.
[85] Middleton R H, Braslavsky J H. String instability in classes of linear time invariant formationcontrol with limited communication range[J]. IEEE Transactions on Automatic Control,2010,55(7):1519-1530.
[86] Bruckstein A M, Cohen N, Efrat A. Ants, crickets and frogs in cyclic pursuit[R]. Haifa: TechnionIsrael Institute of Technology,1991:1-40.
[87] Kim T H, Sugie T. Cooperative control for target-capturing task based on a cyclic pursuitstrategy[J]. Automatica,2007,43(8):1426-1431.
[88] Xu Z Z, Chen C L, Guo Y, et al. Ballooning: An agent-based search strategy in wireless sensorand actor[J]. IEEE Communications Letters,2011,15(9):944-946.
[89] Elson J, Girod L, Estrin D. Fine-grained network time synchronization using referencebroadcasts[C]. in Proceedings of the5th Symposium on Operating Systems Design andImplementation, Berkeley, CA,2002,147-163.
[90] Dimarogonas D V. Sufficient conditions for decentralized potential functions based controllersusing canonical vector fields[J]. IEEE Transactions on Automatic Control,2012,57(10):2621-2626.
[91] Wen G, Duan Z, Su H, et al. A Connectivity-preserving flocking algorithm for multi-agentdynamical systems with bounded potential function[J]. IET Control Theory and Applications,2012,6(6):813-821.
[92] Obraczka K, Viswanath K, Tsudik G. Flooding for reliable multicast in multi-hop ad hocnetworks[J]. Wireless Networks,2001,7(6):627-634.
[93] Sgall J. Solution of david gale’s lion and man problem[J]. Theoretical Computer Science,2001,259(1-2):663-670.
[94] Karnad N, Isler V. Bearing-only pursuit[C]. in Proceedings of the International Conference onRobotics and Automation, Pasadena, USA,2008,2665-2670.
[95] Bopardikar S D, Smith S L, Bullo F, et al. On discrete-time pursuit evasion games with sensinglimitations[J]. IEEE Transactions on Robotics,2008,24(6):1429-1439.
[96] Bopardikar S D, Bullo F, Hespanha J P. A cooperative homicidal Chauffeur game[C]. inProceedings of the IEEE Conference on Decision and Control, New Orleans, USA,2007,4857-4862.
[97] Bopardikar S D, Bullo F, Hespanha J P. A cooperative homicidal Chauffeur game[J]. Automatica,2009,45(7):1771-1777.
[98] Du R, Chen C L, Zhang X B, et al. Path planning and obstacle avoidance for PEGs in WSAN:I-ACO based algorithms and implementation[J]. Ad Hoc&Sensor Wireless Networks,2012,16(4):323-345.
[99] Schenato L, Oh S, Sastry S, et al. Swarm coordination for pursuit evasion games using sensornetworks[C]. in Proceedings of the IEEE International Conference on Robotics and Automation,Karlsruhe, Germany,2005,2493-2498.
[100] Xiang J, Braunl T. String formations of multiple vehicles via pursuit strategy[J]. IET ControlTheory and Applications,2010,4(6):1027-1038.
[101] Marshall J, Broucke M, Francis B. Formations of vehicles in cyclic pursuit[J]. IEEE Transactionson Automatic Control,2004,49(11):1963-1974.
[102] Merlin C J, Heinzelman W B. Duty cycle control for low-power listening MAC protocols[J].IEEE Transactions on Mobile Computing,2010,9(11):1508-1521.
[103] Anderson B D, Yu C, Fidan B, et al. Rigid graph control architectures for autonomousformations[J]. IEEE Control Systems Magazine,2008,28(6):48-63.
[104] Defoort M, Floquet T, K k sy A, et al. Sliding-mode formation control for cooperativeautonomous mobile robots[J]. IEEE Transactions on Industrial Electronics,2008,55(11):3944-3953.
[105] Zhu H, Li M, Chlamtac I, et al. A survey of quality of service in IEEE802.11networks[J]. IEEEWireless Communications,2004,11(4):6-14.
[106] Choi J, Oh S, Horowitz R. Distributed learning and cooperative control for multi-agentsystems[J]. Automatica,2009,45(12):2802-2814.
[107] Wang J, Wu X B, Xu Z L. Potential-based obstacle avoidance in formation control[J]. Journal ofControl Theory and Applications,2008,6(3):311-316.
[108] Yuan M X, Wang S A, Wu C Y, et al. Hybrid ant colony and immune network algorithm based onimproved APF for optimal motion planning[J]. Robotica,2010,28(6):833-846.
[109] Jawhar G, Hasan M, Maarouf S. Leader-Follower formation control of nonholonomic robots withfuzzy logic based approach for obstacle avoidance[C]. in Proceeding of the InternationalConference on Intelligent Robots and Systems, San Francisco, USA,2011,2340-2345.
[110] Korodi A, Corman M. Wheeled mobile robot model and cooperative formation control[J].WSEAS Transactions on Systems,2012,11(11):618-627.
[111]钱积新,赵均,徐祖华.预测控制[M].北京:化学工业出版社,2007.
[112]席裕庚,李德伟,林姝.模型预测控制-现状与挑战[J].自动化学报,2013,39(3):222-236.
[113] Dunbar W B, Murray R M. Model predictive control of coordinated multi-vehicle formations[C].in Proceedings of the IEEE Conference on Decision and Control, Las Vegas, USA,2002,4631-4636.
[114] Olfati-Saber R, Dunbar W B, Murray R M. Cooperative control of multi-vehicle systems usingcost graphs and optimization[C]. in Proceedings of the American Control Conference, Denver,USA,2003,2217-2222.
[115] Dunbar W B, Murray R M. Distributed receding horizon control for multi-vehicle formationstabilization[J]. Automatica,2006,42(4):549-558.
[116] Yang T T, Liu Z Y, Chen H, et al. Formation control and obstacle avoidance for multiple robots[J].Acta Automatica Sinica,2008,34(5):588-593.
[117] Godsil C, Royle G, Gordon F. Algebraic Graph Theory[M]. New York: Springer-Verlag,2001.
[118] Defoort M, Kokosy A, Floquet T, et al. Motion planning for cooperative unicycle-type mobilerobots with limited sensing ranges: A distributed receding horizon approach[J]. Robotics andAutonomous Systems,2009,57(11):1094-1106.
[119] Beard R W, Stepanyan V. Information consensus in distributed multiple vehicle coordinatedcontrol[C]. in Proceedings of the IEEE Conference on Decision and Control, Hawaii, USA,2003,2029-2034.
[120] Raffo G V, Gomes G K, Normey-Rico J E. A predictive controller for autonomous vehicle pathtracking. IEEE Transactions on Intelligent Transportation System[J],2009,10(1):92-102.
[121] Huang W R, Fajen B R, Fink J R, et al. Visual navigation and obstacle avoidance using a steeringpotential function[J]. Robotics and Autonomous Systems,2006,54(4):288-299.
[122] Ge S S, Cui Y J. Dynamic motion planning for mobile robots using potential field method[J].Autonomous Robots,2002,13(1):207-222.
[123]毛宇峰,庞永杰,李哗.速度矢量坐标关系下水下机器人动态避障方法[J].哈尔滨工程大学学报[J].2010,32(2):159-164.
[124] Huang L. Velocity planning for a mobile agent to track a moving target-a potential fieldapproach[J]. Robotics and Autonomous Systems,2009,57(1):55-63.
[125] Luo X Y, Li S B, Guan X P. Flocking algorithm with multi-target tracking for multiagentsystems[J]. Pattern Recognition Letters,2010,31(9):800-805.
[126] Chopra N, Spong M W. Bilateral teleoperation: An historical survey[J]. Automatica,2006,42(12):2035-2057.
[127] Nuňo E, Basaňez L, Ortega R. Passivity-based control for bilateral teleoperation: A tutorial[J].Automatica,2011,47(3):485-495.
[128]景兴建,王越超,谈大龙.遥操作机器人系统时延控制方法综述[J].自动化学报,2004,30(2):214-223.
[129] Ye Y, Liu P X. Improving haptic feedback fidelity in wave variable-based teleoperation orientedto telemedical applications[J]. IEEE Transactions on Instrumentation and Measurement,2009,58(8):2847-2855.
[130] Polushin I G, Liu P X, Lung C H. A control scheme for stable force-reflecting teleoperation overIP networks[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics,2006,36(4):930-939.
[131] Polushin I G, Liu P X, Lung C H. Projection-based force reflection algorithm for stable bilateralteleoperation over networks[J]. IEEE Transactions on Instrumentation and Measurement,2008,57(9):1854-1865.
[132] Nuňo E, Ortega R, Barabanov N, et al. A globally stable PD controller for bilateralteleoperators[J]. IEEE Transactions on Robotics,2008,24(3):753-758.
[133] Hua C C, Liu P X. Teleoperation over the internet with/without velocity signal[J]. IEEETransactions on Instrumentation and Measurement,2011,60(1):4-13.
[134] Nuňo E, Basaňez L, Ortega R, et al. Position tracking for non-linear teleoperators with variabletime delay[J]. International Journal of Robotics Research,2009,28(7):895-910.
[135] Yang X, Hua C C, Yan J, et al. New stability criteria for networked teleoperation system[J].Information Sciences,2013,233(1):244-254.
[136] Franchi A, Masone C, B uthoff H H, et al. Bilateral teleoperation of multiple UAVs withdecentralized bearing-only formation control[C]. in Processing of the International Conferenceon Intelligent Robots and Systems, San Francisco, USA,2011.2215-2222.
[137] Xu Z H, Ma L, Wu Z Y, et al. Teleoperating a formation of car-like rovers under time delays[J]. inProcessing of the Chinese Control Conference, Yantai, China,2011,4095-4101.
[138] Rodríguez-Seda E J, Troy J J, Erignac C A, et al. Bilateral teleoperation of multiple mobileagents: Coordinated motion and collision avoidance[J]. IEEE Transactions on Control SystemsTechnology,2010,18(4):984-992.
[139] Kelly R, Santibáňez V, Loria A. Control of robot manipulators in joint space[M]. London:Springer,2005.
[140] Fu M Y, Xie L H. The sector bound approach to quantized feedback control[J]. IEEETransactions on Automatic Control,2005,50(11):1698-1711.
[141] Yang R N, Shi P, Liu G P, et al. Network-based feedback control for systems with mixed delaysbased on quantization and dropout compensation[J]. Automatica,2011,47(12):2805-2809.
[142] Peng C, Tian Y C. Networked H∞control of linear systems with state quantization[J].Information Sciences,2007,177(24):5763-5774.
[143] Chun H Y, Fei S M, Yue D, et al. H∞quantized control for nonlinear networked controlsystems[J]. Fuzzy Sets and Systems,2011,174(1):99-113.
[144] Tian E, Yue D, Zhao X. Quantised control design for networked control systems[J]. IET ControlTheory and applications,2007,1(6):1693-1699.
[2] Shi L, Cheng P, Chen J M. Optimal periodic sensor scheduling with limited resources [J]. IEEETransactions on Automatic Control,2011,56(9):2190-2195.
[3] Younis M, Akkaya K. Strategies and techniques for node placement in wireless sensor networks:A survey[J]. Ad Hoc Networks,2008,6(4):621-655.
[4] He S B, Chen J M, Sun Y X. Coverage and connectivity in duty-cycled wireless sensor networkfor event monitoring[J]. IEEE Transactions on Parallel and Distributed Systems,2012,23(3):475-482.
[5]刘云浩.物联网导论[M].北京：科学出版社，2010.
[6] Haenggi M. Mobile sensor-actuator networks: Opportunities and challenges[C]. in Proceedings ofIEEE International Workshop on Cellular Neural Networks and Their Applications, Frankfurt,Germany,2002,283-291.
[7] Akyildiz I F, Kasimoglu I H. Wireless sensor and actor networks: Research challenges [J]. AdHoc Networks,2004,2(4):351-367.
[8] Akkaya K, Janapala S. Maximizing connected coverage via controlled actor relocation in wirelesssensor and actor networks[J]. Computer Networks,2008,52(14):2779-2796.
[9] Akkaya K, Senel F, McLaughlan B. Clustering of wireless sensor and actor networks based onsensor distribution and connectivity[J]. Journal of Parallel and Distributed Computing,2009,69(6):573-587.
[10] Zheng D W. Modeling and analysis of strategies in the design of WSAN coordination systems[J].Journal of Parallel and Distributed Computing,2011,71(7):1055-1064.
[11] Melodia T, Pompili D, Gungor V C, et al. A distributed coordination framework for wirelesssensor and actor networks[C]. in Proceedings of ACM Mobihoc, Urbana-Champaign, USA,2005,99-110.
[12]徐宏勤.面向室内环境监控的无线传感器/执行器网络路由协议[D].杭州:浙江大学硕士学位论文，2008.
[13] Macwan A, Nejat G, Benhabib B. Target-motion prediction for robotic search and rescue inwilderness environments[J]. IEEE Transactions on Systems, Man and Cybernetics-B,2011,41(5):1287-1298.
[14]徐哲壮.面向应急救援的无线传感器/执行器网络传输协议研究[D].上海：上海交通大学博士学位论文,2012.
[15]国家中长期科学和技术发展规划纲要(2006-2020年)[R].北京:中华人民共和国国务院,2006.
[16] Chesi G. Designing image trajectories in the presence of uncertain data for robust visual servingpath-planning[C]. in Proceedings of IEEE International Conference on Robotics and Automation,Kobe, Japan,2009,1492-1497.
[17]汶川地震救灾救援工作研究报告[R].北京：民政部国家减灾中心,2009.
[18] Melodia T, Pompili D, Gungor V C, et al. Communication and coordination in wireless sensorand actor networks[J]. IEEE Transactions on Mobile Computing,2007,6(10):1116-1129.
[19] Gungor V C, Akan O B, Akyildiz I F. A real-time and reliable transport (RT) protocol forwireless Sensor and Actor Networks[J]. IEEE/ACM Transactions on Networking,2008,16(2):359-370.
[20] Obraczka K, Viswanath K, Tsudik G. Flooding for reliable multicast in multi-hop ad hocnetworks[J]. Wireless Networks,2001,7(6):627-634.
[21] Chang N, Liu M. Controlled flooding search in a large network[J]. IEEE/ACM Transactions onNetworking,2007,15(2):436-449.
[22] Hassan J, Jha S. Optimising expanding ring search for multi-hop wireless networks[C]. inProceedings of IEEE Global Telecommunications Conference, Dallas, USA,2004,1061-1065.
[23] Huang H, Hartman J, Hurst T. Data-centric routing in sensor networks using biased walk[C]. inProceedings of IEEE Communications Society on Sensor and Ad Hoc Communications andNetworks, Reston, USA,2006,1-9.
[24] Lima L, Barros J. Random walks on sensor networks[C]. in Proceedings of the InternationalSymposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks andWorkshops, Limassol, Cyprus,2007,1-5.
[25] Lv Q, Cao P, Cohen E, et al. Search and replication in unstructured peer-topeer networks[C]. inProceedings of the International Conference on Supercomputing, New York, USA,2002,84-95.
[26] Rachuri K, Murthy C. Energy efficient and scalable search in dense wireless sensor networks[J].IEEE Transactions on Computing,2009,58(6):812-826.
[27] Chen X H, Zhong Q, Liu D P. An improved MAODV protocol based on mobility prediction andself-pruning flooding[C]. in Proceedings of the International Conference on Communicationsand Mobile Computing, Kunming, PRC,2009,127-131.
[28] Sabbineni H, Chakrabarty K. Location-aided flooding: an energy-efficient data disseminationprotocol for wireless-sensor networks[J]. IEEE Transactions on Computers,2005,54(1):36-46.
[29] Bellur B, Ogier R G. A reliable, efficient topology broadcast protocol for dynamic networks[C].in Proceedings of IEEE INFOCOM, New York, USA,1999,178-186.
[30] Im J H, Wu H, Lee D J. Optimal cluster-based routing scheme using node mobility in ad hocnetworks[J]. Wireless networks,2011,17(4):921-935.
[31] Ren W, Beard R W. Distributed consensus in multi-vehicle cooperative control[M]. London:Springer-Verlag,2008.
[32] Pettersen K Y, Gravdahl J T, Nijmeijer H. Group coordination and cooperative control[M].Berlin: Springer-Verlag,2006.
[33] Guzzella L. Automobiles of the future and the role of automatic control in those systems[J].Annual Reviews in Control,2009,33(1):1-10.
[34] Minsky M. The society of mind[M]. New York: Simon and Schuster,1986.
[35] Ren W. Formation keeping and attitude alignment for multiple spacecraft through localinteractions[J]. AIAA Journal of Guidance, Control and Dynamics,2007,30(2):633-638.
[36] Zhu S Y, Chen C L, Li W S, et al. Distributed optimal consensus filter for target tracking inheterogeneous sensor networks[J]. IEEE Transactions on Systems, Man and Cybernetics-B,2013,1-14, in press.
[37] Macwan A, Nejat G, Benhabib B. Target-motion prediction for robotic search and rescue inwilderness environments[J]. IEEE Transactions on Systems, Man and Cybernetics-B,2011,41(5):1287-1298.
[38] An S, Cui J X, Li L V. Agent-based approach to model commuter behaviour’s day-to-daydynamics under pre-trip information[J]. IET Intelligent Transport Systemss,2011,5(1):70-79.
[39] Reynolds C W. Flocks, herds, and schools: A distributed behavioral model[J]. Computer Graph,1987,21(4):25-34.
[40] Vicsek T, Czirók A, Jacob E, et al. Novel type of phase transition in a system of self-drivenparticles[J]. Physical Review Letters,1995,75(6):1226-1229.
[41] Jadbabaie A, Lin J, Morse A S. Coordination of groups of mobile autonomous agents usingnearest neighbor rules[J]. IEEE Transactions on Automatic Control,2003,48(6):988-1001
[42] Olfati-Saber R, Murray R. M. Consensu problem in networks of agents with switching topologyand time-delays[J]. IEEE Transactions on Automatic Control,2004,49(9):1520-1533.
[43] Olfati-Saber R, Alex Fax J, Murray R. M. Consensu and cooperation in networked multi-agentsysytems[J]. Proceedings of the IEEE,2007,95(1):215-233.
[44] Tian Y P, Liu C L. Robust consensus of multi-agent systems with diverse input delays andasymmetric interconnection perturbations[J]. Automatica,2009,45(5):1347-1353.
[45] Hou Z G, Cheng L, Tian M. Decentralized robust adaptive control for the multi-agent systemconsensus problem using neural networks[J]. IEEE Transactions on Systems, Man andCybernetics-part B: Cybernetics,2009,39(3):636-647.
[46] Xiao F, Wang L. Asynchronous consensus in continuous-time multi-agent systems withswitching topology and time-varying delays[J]. IEEE Transactions on Automatic Control,2008,53(8):1804-1816.
[47] Zheng Y S, Wang L. Consensus of of the heterogeneous multi-agent systems without velocitymeasurements[J]. International Journal of Control,2012,85(7):906-917.
[48] Zhu S Y, Chen C L, Guan X P. Consensus protocol for heterogeneous multi-agent systems: Amakov chain approach[J]. Chinese Physics B,2013,22(1):018901-1-5.
[49] Hu J P, Hong Y G. Leader-following coordination of multi-agent systems with coupling timedelays[J]. Physica A,2007,374(2):853-863.
[50] Ni W, Cheng D Z. Leader-following consensus of multi-agent systems under fixed and switchingtopologies[J]. System and Control Letters,2010,59(3-4):209-217.
[51] Sun F L, Guan Z H. Finite-time consensus for Leader-following second-order multi-agentsystem[J]. Journal of Systems Science,2013,44(4):727-738.
[52] Ding L, Han Q L, Guo G. Network-based Leader-following consensus for distributedmulti-agent systems[J]. Automatica,2013,49(7):2281-2286.
[53] Khatib O. Real obstacle avoidance for manipulators and mobile robots[J]. International Journalof Robotics Research,1986,5(1):90-98.
[54] Olfati-Saber R. Flocking for multi-agent dynamic systems: Algorithms and theory[J]. IEEETransactions on Automatic Control,2006,51(3):401-420.
[55] Gu D B, Wang Z Y. Leader-Follower flocking: Algorithms and experiments[J]. IEEETransactions on Control Systems Technology,2009,17(5):1211-1219.
[56] Luo X Y, Liu D, Guan X P, et al. Flocking in target pursuit for multi-agent systems with partialinformed agents[J]. IET Control Theory and Applications,2012,6(4):560-569.
[57] Consolini L, Morbidi F, Prattichizzo D, et al. Leader-Follower formation control ofnonholonomic mobile robots with input constraints[J]. Automatica,2008,44(5):1343-1349.
[58] Li Q, Jiang Z P. Formation constrained multi-agent system in unknown environments[C]. inProceedings of IEEE International Conference on Robotics and Automation, Taipei, PRC,2003,735-740.
[59] Lawton J R, Beard R W, Young B J. A decentralized approach to formation maneuvers[J]. IEEETransactions on Robotics and Automation,2003,19(6):933-941.
[60] Semsar-Kazerooni E, Khorasani K. Multi-agent team cooperation: A game theory approach[J].Automatica,45(10):2205-2213,2009.
[61] Keviczky T, Borrelli F, Fregene K. Decentralized receding horizon control and coordination ofautonomous vehicle formations[J]. IEEE Transactions on Intelligent Transportation System,2008,16(1):19-33.
[62] Lin Z Y, Broucke M, Francis B. Local control strategies for groups of mobile autonomousagents[J]. IEEE Transactions on Automatic Control,2004,49(4):622-629.
[63] Leonard N E, Fiorelli E. Virtual Leaders, artificial potentials and coordinated control ofgroups[C]. in Proceedings of IEEE Conference on Decision and Control, Orlando, USA,2001,2968-2973.
[64] Alexander Fax J, Murray R M. Information flow and cooperative control of vehicleformations[J]. IEEE Transactions on Automatic Control,2004,49(9):1465-1476.
[65] Chen F, Chen Z Q, Liu Z X, et al. Decentralized formation control of mobile agents: A unifiedframework[J]. Physica A,2008,387(19-20):4917-4926.
[66] Dierks T, Jagannathan S. Neural network output feedback control of robot formations[J]. IEEETransactions on Systems, Man and Cybernetics-part B: Cybernetics,2010,40(2):383-399.
[67] Smith B, Egerstedt M, Howard A. Automatic generation of persistent formations for multi-agentnetworks under range constraints[J]. Mobile Networks and Applications,2009,14(3):322-335.
[68] Hendrickx J M, Fidan B, Yu C, et al. Formation reorganization by primitive operations ondirected graphs[J]. IEEE Transaction on Automatic Control,2008,4(53):968-979.
[69] Luo X Y, Yan Y L, Li S B, et al. Topology control based on optimally rigid graph in wirelesssensor networks[J]. Computer Networks,2013,57(4):1037-1047.
[70] Lee G, Chong N Y. Decentralized formation control for small-scale robot teams withanonymity[J]. Mechatronics,2009,19(1):85-105.
[71] Nguyen D B, Do K D. Formation control of mobile robots[J]. International Journal ofComputers, Communications and Control,2006,1(3):41-59.
[72] Marshall J A, Fung T, Broueke M E. Experiments in multirobot coordination[J]. Robotics andAutonomous Systems,2006,54(3):265-275.
[73] Marshall J A, Broucke M E, Francis B. Pursuit formations of unicycles[J]. Automatica,2006,42(1):3-12.
[74] Lin Z Y, Ding W, Yan G F, et al. Leader-Follower formation via complex Laplacian[J].Automatica,2013,49(6):1900-1906.
[75] Zheng R H, Lin Z Y, Yan G F. Ring-coupled unicycles: Boundedness, convergence, andcontrol[J]. Automatica,2009,45(11):2699-2706.
[76] Ding W, Yan G F, Lin Z Y. Collective motions and formations under pursuit strategies ondirected acyclic graphs[J]. Automatica,2010,46(1):174-181.
[77] Xiao F, Wang L, Chen J, et al. Finite-time formation control for multi-agent systems[J].Automatica,2009,45(11):2605-2611.
[78] Jiang F C, Wang L. Finite-time weighted average consensus with respect to a monotonicfunction and its application[J]. System and Control Letter,2011,60(9):718-725.
[79] Li S H, Du H B, Lin X Z. Finite-time consensus algorithm for multi-agent systems withdouble-integrator dynamics[J]. Automatica,2011,47(8):1706-1712.
[80] Couzin I D, Krause J, Franks N R, et al. Effective Leadership and decision-making in animalgroups on the move[J]. Nature,2005,433(7025):513-516.
[81] Morbidi F, Mariottini G, Prattichizzo D. Observer design via immersion and invariance forvision-based Leader-Follower formation control[J]. Automatica,2010,46(1):148-154.
[82] Lewis M A, Tan K H. High precision formation control of mobile robots using virtualstructures[J]. Autonomous Robots,1997,4(4):387-403.
[83] Balch T, Arkin R C. Behavior-based formation control for multirobot teams[J]. IEEETransactions on Robotics and Automation,1999,14(6):926-939.
[84] Stipanovic D, Inalhan G, Teo R, et al. Decentralized overlapping control of a formation ofunmanned aerial vehicles[J]. Automatica,2004,40(8):1285-1296.
[85] Middleton R H, Braslavsky J H. String instability in classes of linear time invariant formationcontrol with limited communication range[J]. IEEE Transactions on Automatic Control,2010,55(7):1519-1530.
[86] Bruckstein A M, Cohen N, Efrat A. Ants, crickets and frogs in cyclic pursuit[R]. Haifa: TechnionIsrael Institute of Technology,1991:1-40.
[87] Kim T H, Sugie T. Cooperative control for target-capturing task based on a cyclic pursuitstrategy[J]. Automatica,2007,43(8):1426-1431.
[88] Xu Z Z, Chen C L, Guo Y, et al. Ballooning: An agent-based search strategy in wireless sensorand actor[J]. IEEE Communications Letters,2011,15(9):944-946.
[89] Elson J, Girod L, Estrin D. Fine-grained network time synchronization using referencebroadcasts[C]. in Proceedings of the5th Symposium on Operating Systems Design andImplementation, Berkeley, CA,2002,147-163.
[90] Dimarogonas D V. Sufficient conditions for decentralized potential functions based controllersusing canonical vector fields[J]. IEEE Transactions on Automatic Control,2012,57(10):2621-2626.
[91] Wen G, Duan Z, Su H, et al. A Connectivity-preserving flocking algorithm for multi-agentdynamical systems with bounded potential function[J]. IET Control Theory and Applications,2012,6(6):813-821.
[92] Obraczka K, Viswanath K, Tsudik G. Flooding for reliable multicast in multi-hop ad hocnetworks[J]. Wireless Networks,2001,7(6):627-634.
[93] Sgall J. Solution of david gale’s lion and man problem[J]. Theoretical Computer Science,2001,259(1-2):663-670.
[94] Karnad N, Isler V. Bearing-only pursuit[C]. in Proceedings of the International Conference onRobotics and Automation, Pasadena, USA,2008,2665-2670.
[95] Bopardikar S D, Smith S L, Bullo F, et al. On discrete-time pursuit evasion games with sensinglimitations[J]. IEEE Transactions on Robotics,2008,24(6):1429-1439.
[96] Bopardikar S D, Bullo F, Hespanha J P. A cooperative homicidal Chauffeur game[C]. inProceedings of the IEEE Conference on Decision and Control, New Orleans, USA,2007,4857-4862.
[97] Bopardikar S D, Bullo F, Hespanha J P. A cooperative homicidal Chauffeur game[J]. Automatica,2009,45(7):1771-1777.
[98] Du R, Chen C L, Zhang X B, et al. Path planning and obstacle avoidance for PEGs in WSAN:I-ACO based algorithms and implementation[J]. Ad Hoc&Sensor Wireless Networks,2012,16(4):323-345.
[99] Schenato L, Oh S, Sastry S, et al. Swarm coordination for pursuit evasion games using sensornetworks[C]. in Proceedings of the IEEE International Conference on Robotics and Automation,Karlsruhe, Germany,2005,2493-2498.
[100] Xiang J, Braunl T. String formations of multiple vehicles via pursuit strategy[J]. IET ControlTheory and Applications,2010,4(6):1027-1038.
[101] Marshall J, Broucke M, Francis B. Formations of vehicles in cyclic pursuit[J]. IEEE Transactionson Automatic Control,2004,49(11):1963-1974.
[102] Merlin C J, Heinzelman W B. Duty cycle control for low-power listening MAC protocols[J].IEEE Transactions on Mobile Computing,2010,9(11):1508-1521.
[103] Anderson B D, Yu C, Fidan B, et al. Rigid graph control architectures for autonomousformations[J]. IEEE Control Systems Magazine,2008,28(6):48-63.
[104] Defoort M, Floquet T, K k sy A, et al. Sliding-mode formation control for cooperativeautonomous mobile robots[J]. IEEE Transactions on Industrial Electronics,2008,55(11):3944-3953.
[105] Zhu H, Li M, Chlamtac I, et al. A survey of quality of service in IEEE802.11networks[J]. IEEEWireless Communications,2004,11(4):6-14.
[106] Choi J, Oh S, Horowitz R. Distributed learning and cooperative control for multi-agentsystems[J]. Automatica,2009,45(12):2802-2814.
[107] Wang J, Wu X B, Xu Z L. Potential-based obstacle avoidance in formation control[J]. Journal ofControl Theory and Applications,2008,6(3):311-316.
[108] Yuan M X, Wang S A, Wu C Y, et al. Hybrid ant colony and immune network algorithm based onimproved APF for optimal motion planning[J]. Robotica,2010,28(6):833-846.
[109] Jawhar G, Hasan M, Maarouf S. Leader-Follower formation control of nonholonomic robots withfuzzy logic based approach for obstacle avoidance[C]. in Proceeding of the InternationalConference on Intelligent Robots and Systems, San Francisco, USA,2011,2340-2345.
[110] Korodi A, Corman M. Wheeled mobile robot model and cooperative formation control[J].WSEAS Transactions on Systems,2012,11(11):618-627.
[111]钱积新,赵均,徐祖华.预测控制[M].北京:化学工业出版社,2007.
[112]席裕庚,李德伟,林姝.模型预测控制-现状与挑战[J].自动化学报,2013,39(3):222-236.
[113] Dunbar W B, Murray R M. Model predictive control of coordinated multi-vehicle formations[C].in Proceedings of the IEEE Conference on Decision and Control, Las Vegas, USA,2002,4631-4636.
[114] Olfati-Saber R, Dunbar W B, Murray R M. Cooperative control of multi-vehicle systems usingcost graphs and optimization[C]. in Proceedings of the American Control Conference, Denver,USA,2003,2217-2222.
[115] Dunbar W B, Murray R M. Distributed receding horizon control for multi-vehicle formationstabilization[J]. Automatica,2006,42(4):549-558.
[116] Yang T T, Liu Z Y, Chen H, et al. Formation control and obstacle avoidance for multiple robots[J].Acta Automatica Sinica,2008,34(5):588-593.
[117] Godsil C, Royle G, Gordon F. Algebraic Graph Theory[M]. New York: Springer-Verlag,2001.
[118] Defoort M, Kokosy A, Floquet T, et al. Motion planning for cooperative unicycle-type mobilerobots with limited sensing ranges: A distributed receding horizon approach[J]. Robotics andAutonomous Systems,2009,57(11):1094-1106.
[119] Beard R W, Stepanyan V. Information consensus in distributed multiple vehicle coordinatedcontrol[C]. in Proceedings of the IEEE Conference on Decision and Control, Hawaii, USA,2003,2029-2034.
[120] Raffo G V, Gomes G K, Normey-Rico J E. A predictive controller for autonomous vehicle pathtracking. IEEE Transactions on Intelligent Transportation System[J],2009,10(1):92-102.
[121] Huang W R, Fajen B R, Fink J R, et al. Visual navigation and obstacle avoidance using a steeringpotential function[J]. Robotics and Autonomous Systems,2006,54(4):288-299.
[122] Ge S S, Cui Y J. Dynamic motion planning for mobile robots using potential field method[J].Autonomous Robots,2002,13(1):207-222.
[123]毛宇峰,庞永杰,李哗.速度矢量坐标关系下水下机器人动态避障方法[J].哈尔滨工程大学学报[J].2010,32(2):159-164.
[124] Huang L. Velocity planning for a mobile agent to track a moving target-a potential fieldapproach[J]. Robotics and Autonomous Systems,2009,57(1):55-63.
[125] Luo X Y, Li S B, Guan X P. Flocking algorithm with multi-target tracking for multiagentsystems[J]. Pattern Recognition Letters,2010,31(9):800-805.
[126] Chopra N, Spong M W. Bilateral teleoperation: An historical survey[J]. Automatica,2006,42(12):2035-2057.
[127] Nuňo E, Basaňez L, Ortega R. Passivity-based control for bilateral teleoperation: A tutorial[J].Automatica,2011,47(3):485-495.
[128]景兴建,王越超,谈大龙.遥操作机器人系统时延控制方法综述[J].自动化学报,2004,30(2):214-223.
[129] Ye Y, Liu P X. Improving haptic feedback fidelity in wave variable-based teleoperation orientedto telemedical applications[J]. IEEE Transactions on Instrumentation and Measurement,2009,58(8):2847-2855.
[130] Polushin I G, Liu P X, Lung C H. A control scheme for stable force-reflecting teleoperation overIP networks[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics,2006,36(4):930-939.
[131] Polushin I G, Liu P X, Lung C H. Projection-based force reflection algorithm for stable bilateralteleoperation over networks[J]. IEEE Transactions on Instrumentation and Measurement,2008,57(9):1854-1865.
[132] Nuňo E, Ortega R, Barabanov N, et al. A globally stable PD controller for bilateralteleoperators[J]. IEEE Transactions on Robotics,2008,24(3):753-758.
[133] Hua C C, Liu P X. Teleoperation over the internet with/without velocity signal[J]. IEEETransactions on Instrumentation and Measurement,2011,60(1):4-13.
[134] Nuňo E, Basaňez L, Ortega R, et al. Position tracking for non-linear teleoperators with variabletime delay[J]. International Journal of Robotics Research,2009,28(7):895-910.
[135] Yang X, Hua C C, Yan J, et al. New stability criteria for networked teleoperation system[J].Information Sciences,2013,233(1):244-254.
[136] Franchi A, Masone C, B uthoff H H, et al. Bilateral teleoperation of multiple UAVs withdecentralized bearing-only formation control[C]. in Processing of the International Conferenceon Intelligent Robots and Systems, San Francisco, USA,2011.2215-2222.
[137] Xu Z H, Ma L, Wu Z Y, et al. Teleoperating a formation of car-like rovers under time delays[J]. inProcessing of the Chinese Control Conference, Yantai, China,2011,4095-4101.
[138] Rodríguez-Seda E J, Troy J J, Erignac C A, et al. Bilateral teleoperation of multiple mobileagents: Coordinated motion and collision avoidance[J]. IEEE Transactions on Control SystemsTechnology,2010,18(4):984-992.
[139] Kelly R, Santibáňez V, Loria A. Control of robot manipulators in joint space[M]. London:Springer,2005.
[140] Fu M Y, Xie L H. The sector bound approach to quantized feedback control[J]. IEEETransactions on Automatic Control,2005,50(11):1698-1711.
[141] Yang R N, Shi P, Liu G P, et al. Network-based feedback control for systems with mixed delaysbased on quantization and dropout compensation[J]. Automatica,2011,47(12):2805-2809.
[142] Peng C, Tian Y C. Networked H∞control of linear systems with state quantization[J].Information Sciences,2007,177(24):5763-5774.
[143] Chun H Y, Fei S M, Yue D, et al. H∞quantized control for nonlinear networked controlsystems[J]. Fuzzy Sets and Systems,2011,174(1):99-113.
[144] Tian E, Yue D, Zhao X. Quantised control design for networked control systems[J]. IET ControlTheory and applications,2007,1(6):1693-1699.