多机器人协作定位及系统架构研究
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摘要
近年来,地面自主移动机器人技术得到飞速发展,逐渐渗透到工业、农业和军事等各个应用领域。由于个体机器人能力的局限性,多机器人协作技术应运而生,其可以利用群体协作的优势弥补个体能力的不足,完成个体无法完成的任务,是机器人模拟人类社会发展的必然趋势,是当前人工智能和移动机器人研究领域的重要课题之一,其中协作定位又是多移动机器人技术的基础问题,如何提高定位的精度、效率和稳定性是研究的关键。
多机器人协作定位是指机器人依靠自身携带的传感器和无线通信设备,通过分布式感知、信息共享、相对观测等协作方式实现群体定位。与个体机器人定位相比,多机器人协作可以使得传感器探测的范围更广,信息来源更多样化,信息融合精度更高,并依据相互间的位置参照,可以更好地提高机器人定位和地图创建的精度,并使协作及协同控制更加有效,例如较好地保持编队队形,编队控制过程更加平滑稳定等。本文从系统架构角度和优化控制角度,系统研究了提高多机器人协作定位精度、稳定性和鲁棒性的方法,具体内容及成果如下：
1.为了提高多机器人编队定位精度和控制稳定性,提出一种多机器人复合EKF(Multiple Extended Kalman Filter, Multi-EKF)定位控制方法,方法采用领导-跟随控制模式,首先跟随机器人把其他成员作为相对固定路标,使用EKF进行自身定位；然后领导机器人将整个编队作为一个整体,通过计算编队的联合概率分布,得到编队的整体定位,并将结果反馈给各个跟随机器人进行各自校正。采用这种整体预测-个体校正的反馈控制方法,提高了编队的定位精度和导航的稳定性。
2.在对机器人协作目标围捕的研究中,提出了一种多机器人Cross-EKF围捕算法,围捕机器人依靠算法,各自利用EKF对目标进行定位,把各机器人对目标定位得到的后验估计协方差进行交集计算,得到最小协方差区间,以该协方差区间边缘点到均值中心最大距离为半径,构建收敛圆,多机器人以该收敛圆面为目标,从各个方向进行逼近围捕。实验结果表明将对动态点的收敛扩展到对动态面的收敛,有助于提高系统的收敛速度和控制稳定性。
3.针对多机器人编队定位导航的鲁棒控制问题,提出了一种柔性控制结构,结构利用柔性结构冗余大、易调节的特点增强编队对外部动态环境的适应能力；利用Rao-Blackwellized粒子滤波算法得到较为理想的编队形状和预应力的搭配,增强系统内部承载能力；利用主-从-备份的角色分配方式增强系统的执行能力。实验结果表明柔性结构的多机器人系统能够较好地适应外部和内部环境的变化,具有较强的灵活性和抗毁能力,鲁棒性也得到了提高。
4.在对多机器人协作导航过程中的实时定位与地图创建问题的研究中,提出了多机器人协作实时FastSLAM (Multi-robots Cooperative Online FastSLAM, MRCO-FastSLAM)算法及其改进算法IMRCO-FastSLAM(Improved MRCO-FastSLAM),算法解决了FastSLAM 1.0中缺乏自身定位测量修正引起的累积误差的问题和FastSLAM2.0引入测量修正引起算法复杂度增加的问题。MRCO-FastSLAM在FastSLAM 1.0和领导-跟随编队控制模式的基础上,让跟随机器人在执行SLAM的同时负责为主机器人提供辅助测量的服务,帮助领导机器人在SLAM的过程中,修正其定位估计误差。为了进一步提高领导机器人SLAM的定位精度,降低跟随机器人动态观测噪声和控制噪声的干扰,改进算法IMRCO-FastSLAM让跟随机器人放弃执行SLAM,采用静止观测,阶跃式前进的协作方法,该方法在消耗额外等待时间的基础上,以时间的等待换取空间的精度提高,在对时间要求不是很苛刻的情况下,具有较好的适用性。
5.仍然是针对多机器人编队定位导航的鲁棒控制问题,提出了一种新的多机器人定位导航控制方法,该方法在MRCO-FastSLAM研究的基础上,使得跟随机器人为领导机器人提供观测修正的同时,将领导机器人位姿的后验估计作为编队的标准参考来修正自身位姿的先验估计,在提高主机器人定位精度的基础上,利用相对定位和反馈校正的控制方法,提高跟随机器人自身在队内的定位和控制精度。
6.为了研究高效、通用和松耦合的多机器人协作定位体系结构,提出将面向服务的架构(Service Oriented Architecture, SOA)引入到多机器人体系结构设计中,该结构在分层结构的基础上,以服务为组成元素,设计了多机器人SOA协议与接口,在协作中实现对机器人下层功能组件的透明封装和上层服务的灵活调用,可以有效避免异构对协作的影响,有利于系统的构建、扩展、重组和维护。
In recent years, ground mobile robot technique has been rapidly developed. It has been gradually infiltrating into industry, agriculture, military and some other application domains. As the limitations of the individual robot capabilities, multi-robots collaboration technique is commanded. It can use the advantages of group collaboration to deal with the disadvantages of individual robot and complete the tasks that individual robot can not complete. It is an inevitable trend for robot to imitate human society. It is also an important research topic of artificial intelligence areas and mobile robot technique areas. Cooperative localization is a fundamental issue for multi-robots. The key of study is how to improve their accuracy, efficiency and stability.
The method of multi-robots cooperative localization is that robots rely on their own sensors and wireless communication equipments to achieve group localization goals through follow cooperative approaches:such as distributed sensing, information sharing and relative observation. Compared with individual robot localization, multi-robots'cooperation can achieve that sensors detection is wider, information sources are more diverse, and the acuuracy of information fusion is more improved. Addition to the relative position reference, the accuracy of localization and mapping can be greatly improved. It also can be more efficient for cooperation and collbarative control, such as that, the formation can be well kept, the implementation of formation control can be much smoothert. This dissertation is focused on the cooperative system architectures and optimal control methods to improve the cooperation localization accuracy, stability and robustness. Details and results are as follows:
1. In order to improve the multi-robots localization accuracy and control stability, Multi-EKF localization and control algorithm is proposed. In the algorithm, the formation model of leader-follower is used. Firstly, each robot is treated as relatively static landmark to the other members. EKF is used to localize itself. Then the formation is considered as a whole unit by the leader robot. The leader robot calculates the joint probability distribution of the whole formation and gets whole formation localization results which are then fed back to individual robot for its localization correction. With the control method of overall forecast-individual correction, the localization accuracy and navigation stability are improved.
2. A new hunting strategy based on Cross-EKF localization is proposed in the study of solving the problem of multi-robots cooperative hunting. By this strategy, each hunting robot locates the target with EKF, and the posterior estimate covariance for target estimated by multi-robots is crossly calculated, and a minimum covariance is achieved. The maximum distance from the edge points to the mean center is used as radius to construct a convergence circle. Multi-robots treat the convergence circle as target and round up from all directions. The results show that, if the convergence of dynamic point is extended to the convergence of dynamic surface, it is helpful to improve the convergence rate and control stability of the system.
3. A flexible control structure is proposed to solve the problem of the robust control of formation localization and navigation. This structure utilizes the advantages of large redundancy and easy adjustment to enhance the ability of adapting to external dynamic environments. It makes use of the Rao-Blackwellized particle filter to obtain the better association between formation shape and prestress to enhance the carrying ability of the system. And it also uses the role allocation of master-attendant-backup to enhance the system executive performance. The results show that the flexible structure of the multi-robots system can better adapt to the changes of external and internal environments and it has better flexibility and survivability capabilities. The robustness of the system is also improved.
4. In the study of online localization and mapping in the process of multi-robots cooperative navigation, multi-robots cooperative online FastSLAM (MRCO-FastSLAM) and the improved MRCO-FastSLAM (IMRCO-FastSLAM) are proposed to solve the problems of accumulative errors in FastSLAM 1.0 for its lack of self-localization-measurements and amendments of algorithm complexity of FastSLAM 2.0 for its self-localization-measurements. On the basis of FastSLAM 1.0 and the leader-follower control model, MRCO-FastSLAM requires the follower robot to provide the ancillary measurement service to the leader robot to help it enhance its own localization estimation accuracy in the process of SLAM. To further improve the accuracy of leader robot localization and reduce the affection of dynamic measurement noise and control noise of follower robot, the improved algorithm named IMRCO-FastSLAM requires the follower robot to give up carrying up SLAM. It uses the method of static observation correction and step-style forward to minimize its self-errors impacts. On the basis of additional waiting time consuming, we use time payment to exchange with space accuracy. In the case of relaxed time requirements, the method has good applicability.
5. A new control strategy is proposed to solve the same problem of the robust control of formation localization and navigation based on the study of MRCO-FastSLAM. In this strategy, when the follower robots supply their relative measurements to the leader robot, the posterior estimations of leader robot localization are then as the formation standard location reference for the follower robots to correct their pose prior estimations. On the basis of improving the leader robot's accuracy, we use the method of relative localization and feedback correction to improve the follower robots'internal localization and control accuracy.
6. A layered architecture based on SOA (Service Oriented Architecture) is proposed to construct an efficient, generic and loosely coupled multi-robots architecture for localization and navigation. Service is considered as the constituent elements. SOA packages and interfaces are defined on the normal layered architectures. The lower functional components are encapsulated transparently and the upper service is invoked flexibly during cooperating. The impact of the difference among the heterogeneous robots in cooperation is effectively avoided, which is beneficial to the system construction, expansion, restructuring and maintenance.
多机器人协作定位是指机器人依靠自身携带的传感器和无线通信设备,通过分布式感知、信息共享、相对观测等协作方式实现群体定位。与个体机器人定位相比,多机器人协作可以使得传感器探测的范围更广,信息来源更多样化,信息融合精度更高,并依据相互间的位置参照,可以更好地提高机器人定位和地图创建的精度,并使协作及协同控制更加有效,例如较好地保持编队队形,编队控制过程更加平滑稳定等。本文从系统架构角度和优化控制角度,系统研究了提高多机器人协作定位精度、稳定性和鲁棒性的方法,具体内容及成果如下：
1.为了提高多机器人编队定位精度和控制稳定性,提出一种多机器人复合EKF(Multiple Extended Kalman Filter, Multi-EKF)定位控制方法,方法采用领导-跟随控制模式,首先跟随机器人把其他成员作为相对固定路标,使用EKF进行自身定位；然后领导机器人将整个编队作为一个整体,通过计算编队的联合概率分布,得到编队的整体定位,并将结果反馈给各个跟随机器人进行各自校正。采用这种整体预测-个体校正的反馈控制方法,提高了编队的定位精度和导航的稳定性。
2.在对机器人协作目标围捕的研究中,提出了一种多机器人Cross-EKF围捕算法,围捕机器人依靠算法,各自利用EKF对目标进行定位,把各机器人对目标定位得到的后验估计协方差进行交集计算,得到最小协方差区间,以该协方差区间边缘点到均值中心最大距离为半径,构建收敛圆,多机器人以该收敛圆面为目标,从各个方向进行逼近围捕。实验结果表明将对动态点的收敛扩展到对动态面的收敛,有助于提高系统的收敛速度和控制稳定性。
3.针对多机器人编队定位导航的鲁棒控制问题,提出了一种柔性控制结构,结构利用柔性结构冗余大、易调节的特点增强编队对外部动态环境的适应能力；利用Rao-Blackwellized粒子滤波算法得到较为理想的编队形状和预应力的搭配,增强系统内部承载能力；利用主-从-备份的角色分配方式增强系统的执行能力。实验结果表明柔性结构的多机器人系统能够较好地适应外部和内部环境的变化,具有较强的灵活性和抗毁能力,鲁棒性也得到了提高。
4.在对多机器人协作导航过程中的实时定位与地图创建问题的研究中,提出了多机器人协作实时FastSLAM (Multi-robots Cooperative Online FastSLAM, MRCO-FastSLAM)算法及其改进算法IMRCO-FastSLAM(Improved MRCO-FastSLAM),算法解决了FastSLAM 1.0中缺乏自身定位测量修正引起的累积误差的问题和FastSLAM2.0引入测量修正引起算法复杂度增加的问题。MRCO-FastSLAM在FastSLAM 1.0和领导-跟随编队控制模式的基础上,让跟随机器人在执行SLAM的同时负责为主机器人提供辅助测量的服务,帮助领导机器人在SLAM的过程中,修正其定位估计误差。为了进一步提高领导机器人SLAM的定位精度,降低跟随机器人动态观测噪声和控制噪声的干扰,改进算法IMRCO-FastSLAM让跟随机器人放弃执行SLAM,采用静止观测,阶跃式前进的协作方法,该方法在消耗额外等待时间的基础上,以时间的等待换取空间的精度提高,在对时间要求不是很苛刻的情况下,具有较好的适用性。
5.仍然是针对多机器人编队定位导航的鲁棒控制问题,提出了一种新的多机器人定位导航控制方法,该方法在MRCO-FastSLAM研究的基础上,使得跟随机器人为领导机器人提供观测修正的同时,将领导机器人位姿的后验估计作为编队的标准参考来修正自身位姿的先验估计,在提高主机器人定位精度的基础上,利用相对定位和反馈校正的控制方法,提高跟随机器人自身在队内的定位和控制精度。
6.为了研究高效、通用和松耦合的多机器人协作定位体系结构,提出将面向服务的架构(Service Oriented Architecture, SOA)引入到多机器人体系结构设计中,该结构在分层结构的基础上,以服务为组成元素,设计了多机器人SOA协议与接口,在协作中实现对机器人下层功能组件的透明封装和上层服务的灵活调用,可以有效避免异构对协作的影响,有利于系统的构建、扩展、重组和维护。
In recent years, ground mobile robot technique has been rapidly developed. It has been gradually infiltrating into industry, agriculture, military and some other application domains. As the limitations of the individual robot capabilities, multi-robots collaboration technique is commanded. It can use the advantages of group collaboration to deal with the disadvantages of individual robot and complete the tasks that individual robot can not complete. It is an inevitable trend for robot to imitate human society. It is also an important research topic of artificial intelligence areas and mobile robot technique areas. Cooperative localization is a fundamental issue for multi-robots. The key of study is how to improve their accuracy, efficiency and stability.
The method of multi-robots cooperative localization is that robots rely on their own sensors and wireless communication equipments to achieve group localization goals through follow cooperative approaches:such as distributed sensing, information sharing and relative observation. Compared with individual robot localization, multi-robots'cooperation can achieve that sensors detection is wider, information sources are more diverse, and the acuuracy of information fusion is more improved. Addition to the relative position reference, the accuracy of localization and mapping can be greatly improved. It also can be more efficient for cooperation and collbarative control, such as that, the formation can be well kept, the implementation of formation control can be much smoothert. This dissertation is focused on the cooperative system architectures and optimal control methods to improve the cooperation localization accuracy, stability and robustness. Details and results are as follows:
1. In order to improve the multi-robots localization accuracy and control stability, Multi-EKF localization and control algorithm is proposed. In the algorithm, the formation model of leader-follower is used. Firstly, each robot is treated as relatively static landmark to the other members. EKF is used to localize itself. Then the formation is considered as a whole unit by the leader robot. The leader robot calculates the joint probability distribution of the whole formation and gets whole formation localization results which are then fed back to individual robot for its localization correction. With the control method of overall forecast-individual correction, the localization accuracy and navigation stability are improved.
2. A new hunting strategy based on Cross-EKF localization is proposed in the study of solving the problem of multi-robots cooperative hunting. By this strategy, each hunting robot locates the target with EKF, and the posterior estimate covariance for target estimated by multi-robots is crossly calculated, and a minimum covariance is achieved. The maximum distance from the edge points to the mean center is used as radius to construct a convergence circle. Multi-robots treat the convergence circle as target and round up from all directions. The results show that, if the convergence of dynamic point is extended to the convergence of dynamic surface, it is helpful to improve the convergence rate and control stability of the system.
3. A flexible control structure is proposed to solve the problem of the robust control of formation localization and navigation. This structure utilizes the advantages of large redundancy and easy adjustment to enhance the ability of adapting to external dynamic environments. It makes use of the Rao-Blackwellized particle filter to obtain the better association between formation shape and prestress to enhance the carrying ability of the system. And it also uses the role allocation of master-attendant-backup to enhance the system executive performance. The results show that the flexible structure of the multi-robots system can better adapt to the changes of external and internal environments and it has better flexibility and survivability capabilities. The robustness of the system is also improved.
4. In the study of online localization and mapping in the process of multi-robots cooperative navigation, multi-robots cooperative online FastSLAM (MRCO-FastSLAM) and the improved MRCO-FastSLAM (IMRCO-FastSLAM) are proposed to solve the problems of accumulative errors in FastSLAM 1.0 for its lack of self-localization-measurements and amendments of algorithm complexity of FastSLAM 2.0 for its self-localization-measurements. On the basis of FastSLAM 1.0 and the leader-follower control model, MRCO-FastSLAM requires the follower robot to provide the ancillary measurement service to the leader robot to help it enhance its own localization estimation accuracy in the process of SLAM. To further improve the accuracy of leader robot localization and reduce the affection of dynamic measurement noise and control noise of follower robot, the improved algorithm named IMRCO-FastSLAM requires the follower robot to give up carrying up SLAM. It uses the method of static observation correction and step-style forward to minimize its self-errors impacts. On the basis of additional waiting time consuming, we use time payment to exchange with space accuracy. In the case of relaxed time requirements, the method has good applicability.
5. A new control strategy is proposed to solve the same problem of the robust control of formation localization and navigation based on the study of MRCO-FastSLAM. In this strategy, when the follower robots supply their relative measurements to the leader robot, the posterior estimations of leader robot localization are then as the formation standard location reference for the follower robots to correct their pose prior estimations. On the basis of improving the leader robot's accuracy, we use the method of relative localization and feedback correction to improve the follower robots'internal localization and control accuracy.
6. A layered architecture based on SOA (Service Oriented Architecture) is proposed to construct an efficient, generic and loosely coupled multi-robots architecture for localization and navigation. Service is considered as the constituent elements. SOA packages and interfaces are defined on the normal layered architectures. The lower functional components are encapsulated transparently and the upper service is invoked flexibly during cooperating. The impact of the difference among the heterogeneous robots in cooperation is effectively avoided, which is beneficial to the system construction, expansion, restructuring and maintenance.
引文
[1]Durrant-Whyte H F. Where am I? A tutorial on mobile vehicle localization. Industrial Robot,1994,21(2):11-16
[2]Bo H. Precise navigation for a 4WS mobile robot. Journal of Zhejiang Universit, Science A,2006,7(2):185-193
[3]Urcola P, Riazuelo L, et al. Cooperative navigation using environment compliant robot formations. Conference on Intelligent Robots and Systems, Nice, France,2008:2789-2794
[4]Thrun S. Simultaneous localization and mapping. Advanced Robotics,2008,38:13-41
[5]Tsay T I J, Hsu M S, Lin R X. Development of a mobile robot for visually guided handling of material. International Conference on Robotics and Automation, Taipei, Taiwan, 2003:3397-3402
[6]http://www. sri.com/about/timeline/shakey.html
[7]http://homepages.laas.fr/matthieu/robots/h2bis.shtml
[8]Lowire J M, Thomas M, et al. The Autonomous Land Vehicle (ALV) preliminary road-following demonstration. Proceedings of SPIE Intelligent Robots and Computer Vision, Vol.579,1985
[9]http://www.ri.cmu.edu/research_lab_group_detail.html?lab_id=28&menu_id=263
[10]Matthies L, Xiong Y, Hogg R, et al. A portable, autonomous, urban reconnaissance robot. Robotics and Autonomous System,2002,40(2-3):163-172
[11]http://www.irobot.com/gi/ground/510_PackBot
[12]http://foster-miller.qinetiq-na.com/lemming.htm
[13]Yoo Y H, Ahmed M, et al. A simulation-based design of extraterrestrial six-legged robot system. IECON, Porto, Portugal,2009:2181-2186
[14]Sibley G, Mei C, et al. Planes, trains and automobiles-autonomy for the modern robot. IEEE Conference on Robotics and Automation, Anchorage, Alaska,2010:285-292
[15]张鹏飞,何克忠等.多功能室外智能移动机器人实验平台—THMR-Ⅴ.机器人2002,24(2)：97-101
[16]孙振平,安向京,贺汉根.CITAVT-Ⅳ—视觉导航的自主车.机器人,2002,24(2)：115-121
[17]Li Q, Zheng N, Cheng H. Springrobot:a prototype autonomous vehicle and its algorithms for lane detection. IEEE Transactions on Intelligent Transportation Systems,2004, 5(4):300-308
[18]Asama H, Matsumoto A, Ishida Y. Design of an autonomous and distributed robot system:ACTRESS. Proc. IEEE/RSJ International Workshop on Intelligent Robots and Systems,1989:283-290
[19]Fukuda T, et al. Structure decision method for self organizing robots based on cell structures-CEROBOT. IEEE International Conference on Robotics and Automation, Scottsdale, AZ, USA,1989:659-700
[20]Jin K, Liang P, Beni G. Stability of synchronized distributed control of discrete swarm structures. In IEEE ICRA, San Diego, CA, USA,1994:1033-1038
[21]Larird R T, Everett H R, et al. MDARS multiple robot host architecture. Association of Unmanned Vehicle Systems, Washington,1995
[22]http://www.robocup.org/robocup-home/
[23]Alami R, Fleury S, Herrb M, et al. Multi-robots cooperation in the MARTHA project. IEEE Robotics and Automation,1998,5(1):36-47
[24]Kube C R, Bonabeau E. Cooperative Transport by Ants and Robots. Robotics and Autonomous Systems,2000,30(1-2):85-101
[25]James M, Daniel Y. Dynamic task assignment in robot swarms. Proceedings of Robotics:Science and Systems. Cambridge, USA,2005:1-8
[26]http://robotics.usc.edu/～agents/?l=Miscellaneous:Fun:herd
[27]http://robotics.usc.edu/～gerkey/research/murdoch.html
[28]Lynne E Parker. ALLIANCE:an architecture for fault tolerant multirobot cooperation. IEEE Transactions of Robotics and Automation,1998,14(2):220-240
[29]Caloud P, et al. Indoor automation with many mobile robots. Proc. IEEE Internatioanl Workshop on Intelligent Robots and Systems, Ibaraki, Japan,1990:67-72
[30]Wang Y C, Tan D L, Huang S. MRCAS:a multi-agent based implementation of multi-robots cooperative assembly system. In Proceedings of 3rd Asian Conference on Robotics and Its Application, Tokyo, Japan,1997:97-102
[31]Luo R C, Li Jian-Xian, Chen C T. Indoor localization using line based map for autonomous mobile robot. IEEE Workshop on advanced robotics and its social impacts, Taipei, Taiwan,2008:1-6
[32]http://baike.baidu.com/view/68567.htm
[33]Parkinson B W, et al.. Global positioning system:theory and applications. Vol. Ⅰ and Ⅱ, American Institute of Aeronautics and Astronautics, Inc., Washington, DC, Vol.136,1996
[34]Jensfelt P. Approaches to mobile robot localization in indoor environments. Sweden Royal Institute of Technology,2001
[35]Gutmann J S. Markov-Kalman Localization for Mobile Robots. Proceedings of 16th International Conference on Pattern Recognition, Quebec, Canada,2002:601-604
[36]Hong W, Zhou C J, Tian Y T. Robust Monte Carlo Localization for humanoid soccer robot. IEEE/ASME International conference on Advanced Intelligent Mechatronics, Singapore,2009:934-939
[37]Rudy N. Robot Localization and Kalman Filters. Master dissertation of Utrecht University,2003
[38]Jiang Y, Liu G R, Zhou L. A review on localization and mapping algorithm based on Extended Kalman Filtering. International Forum on Information Technology and Applications, Chengdu, China,2009:435-440
[39]Martinelli F. Robot localization:comparable performance of EKF and UKF in some interesting indoor settings,16th Mediterranean Conference on Control and Automation, Ajaccio, France,2008:499-504
[40]Xia Y M, Yang Y M. A new particle filter and its application in mobile robot localization. International Conference on Fuzzy Systems and Knowledge Discovery. Jinan, China,2008:522-525
[41]Amarasinghe D, Mann G K I, Gosine R G. Integrated Laser-camera Sensor for the Detection and Localization of landmarks for Robotic applications, IEEE International Conference on Robotics and Automation, Pasadena, USA,2008:4012-4017
[42]Kuratli C, Huang Q T. A CMOS ultrasound Range finder microsystem. IEEE International Solid-State Circuits Conference on Digest of Technical Papers, San Francisco, USA,2000:180-181
[43]Alcantarilla P F, Sang M O, et al. Learning visibility of landmarks for vision-based localization. IEEE International Conference on Robotics and Automation. Alaska, USA,2010: 4881-4888
[44]Yoon K J, Kweon I S. Artificial landmark tracking based on the color histogram. IEEE/RSJ International Conference on Intelligent Robots and Systems, Hawaii, USA,2001: 1918-1923
[45]Wolf J, Burgard W, Burkhardt H. Robust vision-based localization by combining an image-retrieval system with Monte Carlo localization. IEEE Transactions on Robotics,2005, 21(2):208-216
[46]Kleeman L. Optimal estimation of position and heading for mobile robots using ultrasonic beacons and dead-reckoning. IEEE International Conference on Robotics and Automation, Nice, France,1992:2582-2587
[47]Leonard J J, Durrant-Whyte H F. Mobile robot localization by tracking geometric beacons. IEEE Transactions on Robotics and Automation,1991,7(3):376-382
[48]Gross H M, Boehme H J, Wilhelm T. Contribution to vision-based localization, tracking and navigation methods for an interactive mobile service-robot. IEEE International Conference on Systems, Man and Cybernetics. Tucson, USA,2001:672-677
[49]Se S, Lowe D G, Little J J. Vision-based global localization and mapping for mobile robots, IEEE Transactions on Robotics,2005,21(3):364-375
[50]Pink O. Visual map matching and localization using a global feature map. CVPRW 08, Alaska, USA,2008:1-7
[51]Moravec H P. Sensor fusion in certainty grids for mobile robot. Journal of Artificial Intelligence,1988,9(2):61-74
[52]Lu F, Milios E. Globally consistent range scan alignment for environment mapping. Journal of Autonomous Robots,1997,4(4):333-349
[53]Nicola T, Illah N, Roland S. Simultaneous localization and map building:a global topological model with local metric maps. Internatioanl Conference on Intelligent Robots and Systems, Hawaii, USA,2001:421-426
[54]Moravec H P, Elfes A. High resolution maps from wide-angle sonar. Proceedings of the 1985 IEEE International Conference on Robotics and Automation. St.Louis, USA,1985: 116-121
[55]Grisetti G, Stachniss C, Burgard W. Improved Techniques for Grid Mapping with Rao-Blackwellized Particle Filters, IEEE Transactions on Robotics,2007,23(1):34-46
[56]Broten G, Collier J. Continuous motion, outdoor,2.5D grid map generation using inexpensive nodding 2-D laser rangefinder. IEEE International Conference on Robotics and Automation, Orlando, Florida,2006:4240-4245
[57]Kuipers B, Byun Y T. A robot exploration and mapping strategy based on semantic hierarchy of spatial representations. Journal of Robotics and Autonomous Systems,1991,8(1): 47-63
[58]Choset H, Konuksven I, Burdick J W. Sensor based planning for a planar Rod Robot. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. Albuquerque, USA,1996:3584-3591
[59]Choset H, Nagatani K. Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization. IEEE Transactions on Robotics and Automation,2001,17(2):125-137
[60]Smith R, Cheesman P. On the representation and estimation of spatial uncertainty. International Journal of Robotics Research,1987,5(4):56-68
[61]Bailey T, Nebot E M, et al. Data association for mobile robot navigation:a graph theoretic approach. In Proc. IEEE New Orleans on Robotics and Automation, San Francisco, USA,2000:2512-2517
[62]Durrant-Whyte H, Rye D, Nebot E. Localization of automatic guided vehicles. The 7th International Symposium (ISRR'95),1995:613-625
[63]Yap T N, Shelton C R. SLAM in large indoor environments with low-cost noisy, and sparse sonars, IEEE International Conference on Robotics and Automation, Kobe, Japan, 2009:1395-1401
[64]Wan T T, Zhang C W, et al. Mobile robots simultaneous localization and mapping in unstructured environments.2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics, Wuhan, China,2010:116-120
[65]Ian C, Stefan W. SLAM using natural features in an underwater environment.2004 8th International Conference on Control, Automation, Robotics and Vision, Kunming, China, 2004:2076-2081
[66]Jonghyuk K, Salah S. Robust multi-loop airborne SLAM in unknown wind environments. IEEE International Conference on Robotics and Automation, Orlando, USA, 2006:1536-1541
[67]Thrun S, Burgard W, Fox D. Probabilistic Robotics. The MIT Press Cambridge, Massachusetts London, England,2005
[68]Chang H J, Lee C.S.G, et al. P-SLAM:simultaneous localization and mapping with environmental-structure prediction. IEEE transactions on Robotics,2007,23(2):281-293
[69]Kalman R E. A new approach to linear filtering and prediction problems. Transactions of the ASME-Journal of Basic Engineering,1960,82(Series D):35-45
[70]Smith R, Cheeseman P. On the representation and estimation of spatial uncertainty. The International Journal of Robotics Research,1986,5(4):56-68
[71]Leonard J J, Durrant-Whyte H. Simultaneous map building and localization for an autonomous mobile robot. IEEE/RSJ International Workshop on Intelligence Robots and Systems. Osaka, Japan,1991:1442-1447
[72]Durrant-Whyte H, Bailey T. Simultaneous localization and mapping (SLAM):part I the essential algorithms. IEEE Robotics and Automation Magazine,2006,13(2):88-110
[73]Bailey T, Durrant-Whyte H. Simultaneous localization and mapping (SLAM):part II. IEEE Robotics and Automation Magazine,2006,13(3):108-117
[74]Bailey T, Nieto J, et al. Consistency of the EKF-SLAM algorithm. International Conference on Intelligence Robots and Systems, Beijing, China,2006:3562-3568
[75]Guivant J, Nebot E. Optimization of the simultaneous localization and map building algorithm for real time implementation. IEEE Transactions on Robotics and Automation, 2001,17(3):242-257
[76]Christopher R, Gregory D H. Probabilistic data association methods for tracking complex visual objects. IEEE Transactions on pattern analysis and machine intelligence,2001, 23(6):560-576
[77]Bar-Shalom Y, Fortmann T E. Tracking and data association. Academic Press, Boston, USA,1988
[78]Neira J, Tardos J D. Data association in stochastic mapping using the joint compatibility test. IEEE Transactions on Robotics and Automation,2001,17(6):890-897
[79]Julier S, Uhlmann J, Durrant-Whyte H F. A new method for the nonlinear transformation of means and covariances in filters and and estimators. IEEE Transactions on Automatic Control,2000,45(3):477-482
[80]Van der Merwe R. Sigma-point kalman filters for probabilistic inference in dynamic state-space model. Ph.D Thesis, OGI School of Science & Engineering, Oregon Health & Science University,2004
[81]Huang G Q, Mourikis A I, Roumeliotis S I. On the complexity and consitency of UKF-based SLAM. International Conference on Robotics and Automation, Kobe, Japan,2009: 4401-4408
[82]Holmes S A, Klein G, Murray D W. An O(N2) square root unscented kalman filter for visual simultaneous localization and mapping. IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(7):1251-1263
[83]张文玲,朱明清,陈宗海.基于强跟踪UKF的自适应SLAM算法.机器人,2010,32(2)：190-195
[84]Lu F, Milios E. Globally consistent range scan alignment for environment mapping. Journal of Autonomous Robots,1997,4(4):333-349
[85]Thrun S, Montemerlo M. The GraphSLAM algorithm with applications to large-scale mapping of urban structures. International Journal of Robotics Research,2006,25(5-6): 403-429
[86]Kretzschmar H, Grisetti G, Stachniss C. Lifelong map learning for graph-based SLAM in static environments. Kl. Kunstliche Intelligenz,2010,24(3):199-206
[87]Gutmann J S, Konolige K. Incremental mapping of large cyclic environments. Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation. Monterey, CA, USA,1999:318-325
[88]Thrun S, Liu Y, et al. Simultaneous localization and mapping with sparse extended information filters. International Journal of Robotics Research,2004,23(7-8):693-716
[89]Liu Y F, Thrun S. Results for outdoor-SLAM using sparse extended information filters. Proceedings of the 2003 IEEE International Conference on Robotics and Automation, Taipei, Taiwan,2003:1227-1233
[90]Frese U. A proof for the approximate sparsity of SLAM information matrices. Proceedings of the IEEE International Conference on Robotics and Automation, Barcelona, Spain,2005:329-335
[91]Eustice R, Walter M, Leonard J. Sparse Extended information filters:insights into sparsification. IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Canada,2005:3281-3288
[92]Walter M, Eustice R, Leonard J. A provably consistent method for imposing sparsity in feature-based SLAM information filters. International Symposium on Robotics Research. San Francisco, USA,2005:214-236
[93]Thrun S, Fox D, Burgard W, Dellaert F. Robust Monte Carlo localization for mobile robots. Journal of Artificial Intelligence,2001,128(1-2):99-141
[94]Montemerlo M, Thrun S, Koller D, Wegbreit B. FastSLAM:a factored solution to the simultaneous localization and mapping problem. Proceedings of the National Conference on Artificial Intelligence, Edmonton, Canada,2002:593-598
[95]Bolic M, Petar M. Djuric and Hong S J. Resampling algorithms and architectures for distributed particle filters. IEEE Transactions on signal processing,2005,53(7):2442-2450
[96]Hahnel D, Burgard W, Fox D and Thrun S. An efficient FastSLAM algorithm for generating maps of Large-Scale cyclic environment from raw laser range measurements. International Conference on Intelligent Robots and Systems, Sendai, Japan,2003:206-211
[97]Montemerlo M, Thrun S, Koller D, Wegbreit B. FastSLAM 2.0:An improved particle filtering algorithm for simultaneous localization and mapping that provably converges. In Proceedings of the International Conference on Artificial Intelligence, Hyderabad, India,2003: 1151-1156
[98]Kim C, Sakthivel R, Chung W K. Unscented FastSLAM:A robust and efficient solution to the SLAM Problem, IEEE Transactions on Robotics,2008,24(4):808-820
[99]Xiang W, Hong Z. A UPF-UKF framework for SLAM. IEEE International Conference on Robotics and Automation, Roma, Italy,2007:1664-1669
[100]Jennings C, Murray D, Little J J. Cooperative robot localization with vision-based mapping. IEEE International Conference on Robotics and Automation, Detroit, Michigan, 1999:2659-2665
[101]Fox D, Burgard W, Kruppa H, Thrun S. A probabilistic approach to clooaborative multi-robots localization, Journal of Autonomous Robots,2000,8(3):325-344
[102]Wu D, Su H C. An improved probabilistic approach for collaborative multi-robots localization. IEEE International Conference on Robotcis and Biomimetics. Bangkok, Thailand,2009:1868-1875
[103]Roumeliotis S I, Berkey G A. Collective localization:a distributed kalman filter approach to localization of groups of mobile robots. IEEE International Conference on Robotics and Automation. Sn Francisco, USA,2000:2958-2965
[104]Hanek R, Schmitt T. Vision-based localization and data fusion in a system of cooperating mobile robots. IEEE/RSJ International Conference on Intelligent Robots and Systems. Kyushu, Japan,2000:1199-1204
[105]Spletzer J, Das A K, et al. Cooperative localization and control for multi-robots manipulation. International Conference on Intelligent Robots and Systems, Maui, USA,2001: 631-636
[106]Stroupe A W, Balch T. Collaborative probabilistic constraint-based landmark localization. International Conference on Itelligent Robots and Systems, Lausanne, Switzerland,2002:447-453
[107]Martinelli A. Improving the precision on multi robot localization by using a series of filters hierarchically distributed. IEEE/RSJ International Conference on Intelligent Robots and Systems. San Diego, USA,2007:1053-1058
[108]Kondaxakis P, Ruiz V F, Harwin W S. Compensation of observability problem in a multi-robots localization scenario using CEKF. International Conference on Intelligent Robots and Systems. Sendai, Japan,2004:1762-1767
[109]Panzieri S, Pascucci F, Setola R. Multirobot localization using interlaced extended kalman filter. International Conference on Intelligent Robots and Systems. Beijing, China, 2006:2816-2821
[110]Taylor C J, Spletzer J. A bounded uncertainty approach to cooperative localization using relative bearing constraints. International Conference on Intelligent Robots and Systems, San Diego, USA,2007:2500-2506
[111]Jo K H, Lee J H, Kim J B. Cooperative multi-robots localization using differential position data. IEEE/ASME International Conference on Advanced intelligent mechatronics. Zurich,2007:1-6
[112]Tranwny N, Roumeliotis S I, Giannakis G B. Cooperative multi-robots localization under communication constraints. IEEE International Conference on Robotics and Automation, Kobe, Japan,2009:4394-4400
[113]Nerurkar E D, Roumeliotis S I, Martinelli A. Distributed maximum a posteriori estimation for multi-robots cooperative localization. IEEE International Conference on Robotics and Automation, Kobe, Japan,2009:1402-1409
[114]王玲.未知环境中基于相对观测量的多机器人合作定位研究.国防科技大学博士论文,2006
[115]Carlone L, Ng M K, et al. Rao-Blackwellized Particle Filters multi robot SLAM with unknown initial correspondence and limited communication, IEEE International Conference on Robotics and Automation, Alaska, USA,2010:243-249
[116]Gutmann J S, Konolige K. Incremental mapping of large cyclic environments. In Proceedings of CIRA 99, Monterey, California,1999:318-325
[117]Williams S B, Dissanayake G, Durrant-Whyte H. Towards multi-vehicle simultaneous localisation and mapping. IEEE International Conference on Robotics and Automation, Washington, USA,2002:2743-2748
[118]Fenwick J W, Newman P M, Leonard J J. Cooperative concurrent mapping and localization. IEEE International Conference on Robotics and Automation, Washington, USA, 2002:1810-1817
[119]Fox D, Ko J, et al. Distributed multirobot exploration and mapping. Proceedings of the IEEE.2006,94(7):1325-1339
[120]Howard A, Sukhatme G S, Mataric M J. Multirobot simultaneous localization and mapping using manifold representations. Proceedings of the IEEE,2006,94(7):1360-1369
[121]Howard A. Multi-robots simultaneous localization and mapping using particle filters. The international journal of Robotics Research,2006,25(12):1243-1256
[122]Leung K K, Gallagher G. Multi-robots localization and mapping strategy:utilizing behavior based dynamic tree structure and observer-explorer routine. IEEE International Conference on Automation Science and Engineering, Scottsdale, USA,2007:881-886
[123]Ren W, Beard R W, A decentralized scheme for spacecraft formation flying via the virtual structure approach. Proceedings of the 2003 American Control conference, Colorado, 2003:1746-1751
[124]Desai J P, Ostrowski J, Kumar V. Controlling formations of multiple mobile robots, IEEE International Conference on Robotics and Automation, Leuven, Belgium,1998: 2864-2869
[125]Scokaert P O M, Mayne D Q, Rawlings J B. Suboptimal model predictive control: feasibility implies stability. IEEE Transactions on Automatic Control,1999,44(3):648-654
[126]Schneider F E, Wildermuth D. Using an Extended Kalman Filter for Relative Localisation in a Moving Robot Formation. Fourth International Workshop on Robot Motion and Control, Puszczykowo, Poland,2004:85-90
[127]Yamaguchi H. A cooperative hunting behavior by mobile-robot troops. International Journal of Robotics Research,1999,18(8):931-940
[128]Cao Z Q, Wang S, Tan M. Cooperative Hunting of Multiple Mobile Robots in an Unknown Environment. Acta Automatic Sinica,2003,29(4):536-543
[129]Dias M B. Dynamic Heterogeneous Robot Teams Engaged in Adversarial Tasks. CMU-RI-TR-05-14,2005
[130]Van der Merwe R, Doucet A, de Freitas N, Wan E. The Unscented Particle Filter. Technical Report CUED/F-INFENG/TR 380, Cambridge University, Department of Engineering,2000
[131]Liu S C, TAN D L, Liu G J. Robust Leader-follower Formation Control of Mobile Robots Based on a Second Order Kinematics Model. Acta Automatica Sinica,2007,33(9): 947-955
[132]Kanjanawanishkul K. Formation Control of Omnidirectional Mobile Robots using Distributed Model Predictive Control. Proceedings of Second International Conference on Robot Communication and Coordination, Odense, Denmark,2009:1-7
[133]Suh I H, Yeo H J, et al. Design of a supervisory control system for multiple robotics systems. Proceedings of the 1996 IEEE/RSJ International Conference on Intelligent Robots and Systems, Osaka, Japan,1996:332-339
[134]Parker L E. Alliance:An architecture for fault tolerant multirobot cooperation. IEEE Transactions on Robotics and Automation,1998,14(2):220-240
[135]Alur R, Das A J, et al. A framework and architecture for multirobot coordination. International Journal of Robotics Research,2002,21(10/11):977-995
[136]Chan Y J, Yow K C. A strategy-driven framework for multi-robots cooperation system. International Conference on Control, Automation, Robotics and Vision, Singapore,2006:1-6
[137]Gonzalez E, Perez A, et al. MRCC:A multi-resolution cooperative control agent architecture. IEEE International Conference on Intelligent Agent Technology. Silicon Valley, California, USA,2007:391-394
[138]Huntsberger T, Pirjanian P, et al. CAMPOUT:a control architecture for tightly coupled coordination of multirobot Systems for planetary surface exploration. IEEE transactions on systems,2003,33(5),550-559
[139]Endrei M, Ang J. Service-oriented architecture and web services. IBM, USA,2004.
[140]Camilo C, Carlos C. Service oriented architecture for mobile robot localization. IEEE Conference on Emerging Technologies and Factory Automation, Patras, Greece.2007: 888-891
[141]Chen Y N, Bai X Y. On robotics applications in service-oriented architecture. The 28th International Conference on Distributed Computing Systems Workshops, Beijing, China, 2008:551-556
[142]Jose V B M, Francisco M P, et al. Distributed robotic architecture based on smart services. IEEE International Conference on Industrial Informatics, Singapore,2006:480-485
[143]Michele A, Francesco Z, Gianni C. A service-oriented approach for building autonomic peer-to-peer robot systems. Proceedings of the 16th IEEE International Workshops on Enabling Technologies:Infrastructure for Collaborative Enterprises, Paris, France,2007: 137-142
[144]Xie D, Ying S, Zhang T, et al. An approach for describing SOA.2nd International Conference on Wireless Communications, Networking and Mobile Computing, Wuhan, China, 2006,1-4.
[145]MacKenzie C M, Laskey K, et al. Service-oriented architecture expands the vision of web services. http://www.ibm.com/developerworks/webservices/library/ws-soaintro.html, IBM, USA,2004.
[2]Bo H. Precise navigation for a 4WS mobile robot. Journal of Zhejiang Universit, Science A,2006,7(2):185-193
[3]Urcola P, Riazuelo L, et al. Cooperative navigation using environment compliant robot formations. Conference on Intelligent Robots and Systems, Nice, France,2008:2789-2794
[4]Thrun S. Simultaneous localization and mapping. Advanced Robotics,2008,38:13-41
[5]Tsay T I J, Hsu M S, Lin R X. Development of a mobile robot for visually guided handling of material. International Conference on Robotics and Automation, Taipei, Taiwan, 2003:3397-3402
[6]http://www. sri.com/about/timeline/shakey.html
[7]http://homepages.laas.fr/matthieu/robots/h2bis.shtml
[8]Lowire J M, Thomas M, et al. The Autonomous Land Vehicle (ALV) preliminary road-following demonstration. Proceedings of SPIE Intelligent Robots and Computer Vision, Vol.579,1985
[9]http://www.ri.cmu.edu/research_lab_group_detail.html?lab_id=28&menu_id=263
[10]Matthies L, Xiong Y, Hogg R, et al. A portable, autonomous, urban reconnaissance robot. Robotics and Autonomous System,2002,40(2-3):163-172
[11]http://www.irobot.com/gi/ground/510_PackBot
[12]http://foster-miller.qinetiq-na.com/lemming.htm
[13]Yoo Y H, Ahmed M, et al. A simulation-based design of extraterrestrial six-legged robot system. IECON, Porto, Portugal,2009:2181-2186
[14]Sibley G, Mei C, et al. Planes, trains and automobiles-autonomy for the modern robot. IEEE Conference on Robotics and Automation, Anchorage, Alaska,2010:285-292
[15]张鹏飞,何克忠等.多功能室外智能移动机器人实验平台—THMR-Ⅴ.机器人2002,24(2)：97-101
[16]孙振平,安向京,贺汉根.CITAVT-Ⅳ—视觉导航的自主车.机器人,2002,24(2)：115-121
[17]Li Q, Zheng N, Cheng H. Springrobot:a prototype autonomous vehicle and its algorithms for lane detection. IEEE Transactions on Intelligent Transportation Systems,2004, 5(4):300-308
[18]Asama H, Matsumoto A, Ishida Y. Design of an autonomous and distributed robot system:ACTRESS. Proc. IEEE/RSJ International Workshop on Intelligent Robots and Systems,1989:283-290
[19]Fukuda T, et al. Structure decision method for self organizing robots based on cell structures-CEROBOT. IEEE International Conference on Robotics and Automation, Scottsdale, AZ, USA,1989:659-700
[20]Jin K, Liang P, Beni G. Stability of synchronized distributed control of discrete swarm structures. In IEEE ICRA, San Diego, CA, USA,1994:1033-1038
[21]Larird R T, Everett H R, et al. MDARS multiple robot host architecture. Association of Unmanned Vehicle Systems, Washington,1995
[22]http://www.robocup.org/robocup-home/
[23]Alami R, Fleury S, Herrb M, et al. Multi-robots cooperation in the MARTHA project. IEEE Robotics and Automation,1998,5(1):36-47
[24]Kube C R, Bonabeau E. Cooperative Transport by Ants and Robots. Robotics and Autonomous Systems,2000,30(1-2):85-101
[25]James M, Daniel Y. Dynamic task assignment in robot swarms. Proceedings of Robotics:Science and Systems. Cambridge, USA,2005:1-8
[26]http://robotics.usc.edu/～agents/?l=Miscellaneous:Fun:herd
[27]http://robotics.usc.edu/～gerkey/research/murdoch.html
[28]Lynne E Parker. ALLIANCE:an architecture for fault tolerant multirobot cooperation. IEEE Transactions of Robotics and Automation,1998,14(2):220-240
[29]Caloud P, et al. Indoor automation with many mobile robots. Proc. IEEE Internatioanl Workshop on Intelligent Robots and Systems, Ibaraki, Japan,1990:67-72
[30]Wang Y C, Tan D L, Huang S. MRCAS:a multi-agent based implementation of multi-robots cooperative assembly system. In Proceedings of 3rd Asian Conference on Robotics and Its Application, Tokyo, Japan,1997:97-102
[31]Luo R C, Li Jian-Xian, Chen C T. Indoor localization using line based map for autonomous mobile robot. IEEE Workshop on advanced robotics and its social impacts, Taipei, Taiwan,2008:1-6
[32]http://baike.baidu.com/view/68567.htm
[33]Parkinson B W, et al.. Global positioning system:theory and applications. Vol. Ⅰ and Ⅱ, American Institute of Aeronautics and Astronautics, Inc., Washington, DC, Vol.136,1996
[34]Jensfelt P. Approaches to mobile robot localization in indoor environments. Sweden Royal Institute of Technology,2001
[35]Gutmann J S. Markov-Kalman Localization for Mobile Robots. Proceedings of 16th International Conference on Pattern Recognition, Quebec, Canada,2002:601-604
[36]Hong W, Zhou C J, Tian Y T. Robust Monte Carlo Localization for humanoid soccer robot. IEEE/ASME International conference on Advanced Intelligent Mechatronics, Singapore,2009:934-939
[37]Rudy N. Robot Localization and Kalman Filters. Master dissertation of Utrecht University,2003
[38]Jiang Y, Liu G R, Zhou L. A review on localization and mapping algorithm based on Extended Kalman Filtering. International Forum on Information Technology and Applications, Chengdu, China,2009:435-440
[39]Martinelli F. Robot localization:comparable performance of EKF and UKF in some interesting indoor settings,16th Mediterranean Conference on Control and Automation, Ajaccio, France,2008:499-504
[40]Xia Y M, Yang Y M. A new particle filter and its application in mobile robot localization. International Conference on Fuzzy Systems and Knowledge Discovery. Jinan, China,2008:522-525
[41]Amarasinghe D, Mann G K I, Gosine R G. Integrated Laser-camera Sensor for the Detection and Localization of landmarks for Robotic applications, IEEE International Conference on Robotics and Automation, Pasadena, USA,2008:4012-4017
[42]Kuratli C, Huang Q T. A CMOS ultrasound Range finder microsystem. IEEE International Solid-State Circuits Conference on Digest of Technical Papers, San Francisco, USA,2000:180-181
[43]Alcantarilla P F, Sang M O, et al. Learning visibility of landmarks for vision-based localization. IEEE International Conference on Robotics and Automation. Alaska, USA,2010: 4881-4888
[44]Yoon K J, Kweon I S. Artificial landmark tracking based on the color histogram. IEEE/RSJ International Conference on Intelligent Robots and Systems, Hawaii, USA,2001: 1918-1923
[45]Wolf J, Burgard W, Burkhardt H. Robust vision-based localization by combining an image-retrieval system with Monte Carlo localization. IEEE Transactions on Robotics,2005, 21(2):208-216
[46]Kleeman L. Optimal estimation of position and heading for mobile robots using ultrasonic beacons and dead-reckoning. IEEE International Conference on Robotics and Automation, Nice, France,1992:2582-2587
[47]Leonard J J, Durrant-Whyte H F. Mobile robot localization by tracking geometric beacons. IEEE Transactions on Robotics and Automation,1991,7(3):376-382
[48]Gross H M, Boehme H J, Wilhelm T. Contribution to vision-based localization, tracking and navigation methods for an interactive mobile service-robot. IEEE International Conference on Systems, Man and Cybernetics. Tucson, USA,2001:672-677
[49]Se S, Lowe D G, Little J J. Vision-based global localization and mapping for mobile robots, IEEE Transactions on Robotics,2005,21(3):364-375
[50]Pink O. Visual map matching and localization using a global feature map. CVPRW 08, Alaska, USA,2008:1-7
[51]Moravec H P. Sensor fusion in certainty grids for mobile robot. Journal of Artificial Intelligence,1988,9(2):61-74
[52]Lu F, Milios E. Globally consistent range scan alignment for environment mapping. Journal of Autonomous Robots,1997,4(4):333-349
[53]Nicola T, Illah N, Roland S. Simultaneous localization and map building:a global topological model with local metric maps. Internatioanl Conference on Intelligent Robots and Systems, Hawaii, USA,2001:421-426
[54]Moravec H P, Elfes A. High resolution maps from wide-angle sonar. Proceedings of the 1985 IEEE International Conference on Robotics and Automation. St.Louis, USA,1985: 116-121
[55]Grisetti G, Stachniss C, Burgard W. Improved Techniques for Grid Mapping with Rao-Blackwellized Particle Filters, IEEE Transactions on Robotics,2007,23(1):34-46
[56]Broten G, Collier J. Continuous motion, outdoor,2.5D grid map generation using inexpensive nodding 2-D laser rangefinder. IEEE International Conference on Robotics and Automation, Orlando, Florida,2006:4240-4245
[57]Kuipers B, Byun Y T. A robot exploration and mapping strategy based on semantic hierarchy of spatial representations. Journal of Robotics and Autonomous Systems,1991,8(1): 47-63
[58]Choset H, Konuksven I, Burdick J W. Sensor based planning for a planar Rod Robot. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. Albuquerque, USA,1996:3584-3591
[59]Choset H, Nagatani K. Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization. IEEE Transactions on Robotics and Automation,2001,17(2):125-137
[60]Smith R, Cheesman P. On the representation and estimation of spatial uncertainty. International Journal of Robotics Research,1987,5(4):56-68
[61]Bailey T, Nebot E M, et al. Data association for mobile robot navigation:a graph theoretic approach. In Proc. IEEE New Orleans on Robotics and Automation, San Francisco, USA,2000:2512-2517
[62]Durrant-Whyte H, Rye D, Nebot E. Localization of automatic guided vehicles. The 7th International Symposium (ISRR'95),1995:613-625
[63]Yap T N, Shelton C R. SLAM in large indoor environments with low-cost noisy, and sparse sonars, IEEE International Conference on Robotics and Automation, Kobe, Japan, 2009:1395-1401
[64]Wan T T, Zhang C W, et al. Mobile robots simultaneous localization and mapping in unstructured environments.2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics, Wuhan, China,2010:116-120
[65]Ian C, Stefan W. SLAM using natural features in an underwater environment.2004 8th International Conference on Control, Automation, Robotics and Vision, Kunming, China, 2004:2076-2081
[66]Jonghyuk K, Salah S. Robust multi-loop airborne SLAM in unknown wind environments. IEEE International Conference on Robotics and Automation, Orlando, USA, 2006:1536-1541
[67]Thrun S, Burgard W, Fox D. Probabilistic Robotics. The MIT Press Cambridge, Massachusetts London, England,2005
[68]Chang H J, Lee C.S.G, et al. P-SLAM:simultaneous localization and mapping with environmental-structure prediction. IEEE transactions on Robotics,2007,23(2):281-293
[69]Kalman R E. A new approach to linear filtering and prediction problems. Transactions of the ASME-Journal of Basic Engineering,1960,82(Series D):35-45
[70]Smith R, Cheeseman P. On the representation and estimation of spatial uncertainty. The International Journal of Robotics Research,1986,5(4):56-68
[71]Leonard J J, Durrant-Whyte H. Simultaneous map building and localization for an autonomous mobile robot. IEEE/RSJ International Workshop on Intelligence Robots and Systems. Osaka, Japan,1991:1442-1447
[72]Durrant-Whyte H, Bailey T. Simultaneous localization and mapping (SLAM):part I the essential algorithms. IEEE Robotics and Automation Magazine,2006,13(2):88-110
[73]Bailey T, Durrant-Whyte H. Simultaneous localization and mapping (SLAM):part II. IEEE Robotics and Automation Magazine,2006,13(3):108-117
[74]Bailey T, Nieto J, et al. Consistency of the EKF-SLAM algorithm. International Conference on Intelligence Robots and Systems, Beijing, China,2006:3562-3568
[75]Guivant J, Nebot E. Optimization of the simultaneous localization and map building algorithm for real time implementation. IEEE Transactions on Robotics and Automation, 2001,17(3):242-257
[76]Christopher R, Gregory D H. Probabilistic data association methods for tracking complex visual objects. IEEE Transactions on pattern analysis and machine intelligence,2001, 23(6):560-576
[77]Bar-Shalom Y, Fortmann T E. Tracking and data association. Academic Press, Boston, USA,1988
[78]Neira J, Tardos J D. Data association in stochastic mapping using the joint compatibility test. IEEE Transactions on Robotics and Automation,2001,17(6):890-897
[79]Julier S, Uhlmann J, Durrant-Whyte H F. A new method for the nonlinear transformation of means and covariances in filters and and estimators. IEEE Transactions on Automatic Control,2000,45(3):477-482
[80]Van der Merwe R. Sigma-point kalman filters for probabilistic inference in dynamic state-space model. Ph.D Thesis, OGI School of Science & Engineering, Oregon Health & Science University,2004
[81]Huang G Q, Mourikis A I, Roumeliotis S I. On the complexity and consitency of UKF-based SLAM. International Conference on Robotics and Automation, Kobe, Japan,2009: 4401-4408
[82]Holmes S A, Klein G, Murray D W. An O(N2) square root unscented kalman filter for visual simultaneous localization and mapping. IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(7):1251-1263
[83]张文玲,朱明清,陈宗海.基于强跟踪UKF的自适应SLAM算法.机器人,2010,32(2)：190-195
[84]Lu F, Milios E. Globally consistent range scan alignment for environment mapping. Journal of Autonomous Robots,1997,4(4):333-349
[85]Thrun S, Montemerlo M. The GraphSLAM algorithm with applications to large-scale mapping of urban structures. International Journal of Robotics Research,2006,25(5-6): 403-429
[86]Kretzschmar H, Grisetti G, Stachniss C. Lifelong map learning for graph-based SLAM in static environments. Kl. Kunstliche Intelligenz,2010,24(3):199-206
[87]Gutmann J S, Konolige K. Incremental mapping of large cyclic environments. Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation. Monterey, CA, USA,1999:318-325
[88]Thrun S, Liu Y, et al. Simultaneous localization and mapping with sparse extended information filters. International Journal of Robotics Research,2004,23(7-8):693-716
[89]Liu Y F, Thrun S. Results for outdoor-SLAM using sparse extended information filters. Proceedings of the 2003 IEEE International Conference on Robotics and Automation, Taipei, Taiwan,2003:1227-1233
[90]Frese U. A proof for the approximate sparsity of SLAM information matrices. Proceedings of the IEEE International Conference on Robotics and Automation, Barcelona, Spain,2005:329-335
[91]Eustice R, Walter M, Leonard J. Sparse Extended information filters:insights into sparsification. IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Canada,2005:3281-3288
[92]Walter M, Eustice R, Leonard J. A provably consistent method for imposing sparsity in feature-based SLAM information filters. International Symposium on Robotics Research. San Francisco, USA,2005:214-236
[93]Thrun S, Fox D, Burgard W, Dellaert F. Robust Monte Carlo localization for mobile robots. Journal of Artificial Intelligence,2001,128(1-2):99-141
[94]Montemerlo M, Thrun S, Koller D, Wegbreit B. FastSLAM:a factored solution to the simultaneous localization and mapping problem. Proceedings of the National Conference on Artificial Intelligence, Edmonton, Canada,2002:593-598
[95]Bolic M, Petar M. Djuric and Hong S J. Resampling algorithms and architectures for distributed particle filters. IEEE Transactions on signal processing,2005,53(7):2442-2450
[96]Hahnel D, Burgard W, Fox D and Thrun S. An efficient FastSLAM algorithm for generating maps of Large-Scale cyclic environment from raw laser range measurements. International Conference on Intelligent Robots and Systems, Sendai, Japan,2003:206-211
[97]Montemerlo M, Thrun S, Koller D, Wegbreit B. FastSLAM 2.0:An improved particle filtering algorithm for simultaneous localization and mapping that provably converges. In Proceedings of the International Conference on Artificial Intelligence, Hyderabad, India,2003: 1151-1156
[98]Kim C, Sakthivel R, Chung W K. Unscented FastSLAM:A robust and efficient solution to the SLAM Problem, IEEE Transactions on Robotics,2008,24(4):808-820
[99]Xiang W, Hong Z. A UPF-UKF framework for SLAM. IEEE International Conference on Robotics and Automation, Roma, Italy,2007:1664-1669
[100]Jennings C, Murray D, Little J J. Cooperative robot localization with vision-based mapping. IEEE International Conference on Robotics and Automation, Detroit, Michigan, 1999:2659-2665
[101]Fox D, Burgard W, Kruppa H, Thrun S. A probabilistic approach to clooaborative multi-robots localization, Journal of Autonomous Robots,2000,8(3):325-344
[102]Wu D, Su H C. An improved probabilistic approach for collaborative multi-robots localization. IEEE International Conference on Robotcis and Biomimetics. Bangkok, Thailand,2009:1868-1875
[103]Roumeliotis S I, Berkey G A. Collective localization:a distributed kalman filter approach to localization of groups of mobile robots. IEEE International Conference on Robotics and Automation. Sn Francisco, USA,2000:2958-2965
[104]Hanek R, Schmitt T. Vision-based localization and data fusion in a system of cooperating mobile robots. IEEE/RSJ International Conference on Intelligent Robots and Systems. Kyushu, Japan,2000:1199-1204
[105]Spletzer J, Das A K, et al. Cooperative localization and control for multi-robots manipulation. International Conference on Intelligent Robots and Systems, Maui, USA,2001: 631-636
[106]Stroupe A W, Balch T. Collaborative probabilistic constraint-based landmark localization. International Conference on Itelligent Robots and Systems, Lausanne, Switzerland,2002:447-453
[107]Martinelli A. Improving the precision on multi robot localization by using a series of filters hierarchically distributed. IEEE/RSJ International Conference on Intelligent Robots and Systems. San Diego, USA,2007:1053-1058
[108]Kondaxakis P, Ruiz V F, Harwin W S. Compensation of observability problem in a multi-robots localization scenario using CEKF. International Conference on Intelligent Robots and Systems. Sendai, Japan,2004:1762-1767
[109]Panzieri S, Pascucci F, Setola R. Multirobot localization using interlaced extended kalman filter. International Conference on Intelligent Robots and Systems. Beijing, China, 2006:2816-2821
[110]Taylor C J, Spletzer J. A bounded uncertainty approach to cooperative localization using relative bearing constraints. International Conference on Intelligent Robots and Systems, San Diego, USA,2007:2500-2506
[111]Jo K H, Lee J H, Kim J B. Cooperative multi-robots localization using differential position data. IEEE/ASME International Conference on Advanced intelligent mechatronics. Zurich,2007:1-6
[112]Tranwny N, Roumeliotis S I, Giannakis G B. Cooperative multi-robots localization under communication constraints. IEEE International Conference on Robotics and Automation, Kobe, Japan,2009:4394-4400
[113]Nerurkar E D, Roumeliotis S I, Martinelli A. Distributed maximum a posteriori estimation for multi-robots cooperative localization. IEEE International Conference on Robotics and Automation, Kobe, Japan,2009:1402-1409
[114]王玲.未知环境中基于相对观测量的多机器人合作定位研究.国防科技大学博士论文,2006
[115]Carlone L, Ng M K, et al. Rao-Blackwellized Particle Filters multi robot SLAM with unknown initial correspondence and limited communication, IEEE International Conference on Robotics and Automation, Alaska, USA,2010:243-249
[116]Gutmann J S, Konolige K. Incremental mapping of large cyclic environments. In Proceedings of CIRA 99, Monterey, California,1999:318-325
[117]Williams S B, Dissanayake G, Durrant-Whyte H. Towards multi-vehicle simultaneous localisation and mapping. IEEE International Conference on Robotics and Automation, Washington, USA,2002:2743-2748
[118]Fenwick J W, Newman P M, Leonard J J. Cooperative concurrent mapping and localization. IEEE International Conference on Robotics and Automation, Washington, USA, 2002:1810-1817
[119]Fox D, Ko J, et al. Distributed multirobot exploration and mapping. Proceedings of the IEEE.2006,94(7):1325-1339
[120]Howard A, Sukhatme G S, Mataric M J. Multirobot simultaneous localization and mapping using manifold representations. Proceedings of the IEEE,2006,94(7):1360-1369
[121]Howard A. Multi-robots simultaneous localization and mapping using particle filters. The international journal of Robotics Research,2006,25(12):1243-1256
[122]Leung K K, Gallagher G. Multi-robots localization and mapping strategy:utilizing behavior based dynamic tree structure and observer-explorer routine. IEEE International Conference on Automation Science and Engineering, Scottsdale, USA,2007:881-886
[123]Ren W, Beard R W, A decentralized scheme for spacecraft formation flying via the virtual structure approach. Proceedings of the 2003 American Control conference, Colorado, 2003:1746-1751
[124]Desai J P, Ostrowski J, Kumar V. Controlling formations of multiple mobile robots, IEEE International Conference on Robotics and Automation, Leuven, Belgium,1998: 2864-2869
[125]Scokaert P O M, Mayne D Q, Rawlings J B. Suboptimal model predictive control: feasibility implies stability. IEEE Transactions on Automatic Control,1999,44(3):648-654
[126]Schneider F E, Wildermuth D. Using an Extended Kalman Filter for Relative Localisation in a Moving Robot Formation. Fourth International Workshop on Robot Motion and Control, Puszczykowo, Poland,2004:85-90
[127]Yamaguchi H. A cooperative hunting behavior by mobile-robot troops. International Journal of Robotics Research,1999,18(8):931-940
[128]Cao Z Q, Wang S, Tan M. Cooperative Hunting of Multiple Mobile Robots in an Unknown Environment. Acta Automatic Sinica,2003,29(4):536-543
[129]Dias M B. Dynamic Heterogeneous Robot Teams Engaged in Adversarial Tasks. CMU-RI-TR-05-14,2005
[130]Van der Merwe R, Doucet A, de Freitas N, Wan E. The Unscented Particle Filter. Technical Report CUED/F-INFENG/TR 380, Cambridge University, Department of Engineering,2000
[131]Liu S C, TAN D L, Liu G J. Robust Leader-follower Formation Control of Mobile Robots Based on a Second Order Kinematics Model. Acta Automatica Sinica,2007,33(9): 947-955
[132]Kanjanawanishkul K. Formation Control of Omnidirectional Mobile Robots using Distributed Model Predictive Control. Proceedings of Second International Conference on Robot Communication and Coordination, Odense, Denmark,2009:1-7
[133]Suh I H, Yeo H J, et al. Design of a supervisory control system for multiple robotics systems. Proceedings of the 1996 IEEE/RSJ International Conference on Intelligent Robots and Systems, Osaka, Japan,1996:332-339
[134]Parker L E. Alliance:An architecture for fault tolerant multirobot cooperation. IEEE Transactions on Robotics and Automation,1998,14(2):220-240
[135]Alur R, Das A J, et al. A framework and architecture for multirobot coordination. International Journal of Robotics Research,2002,21(10/11):977-995
[136]Chan Y J, Yow K C. A strategy-driven framework for multi-robots cooperation system. International Conference on Control, Automation, Robotics and Vision, Singapore,2006:1-6
[137]Gonzalez E, Perez A, et al. MRCC:A multi-resolution cooperative control agent architecture. IEEE International Conference on Intelligent Agent Technology. Silicon Valley, California, USA,2007:391-394
[138]Huntsberger T, Pirjanian P, et al. CAMPOUT:a control architecture for tightly coupled coordination of multirobot Systems for planetary surface exploration. IEEE transactions on systems,2003,33(5),550-559
[139]Endrei M, Ang J. Service-oriented architecture and web services. IBM, USA,2004.
[140]Camilo C, Carlos C. Service oriented architecture for mobile robot localization. IEEE Conference on Emerging Technologies and Factory Automation, Patras, Greece.2007: 888-891
[141]Chen Y N, Bai X Y. On robotics applications in service-oriented architecture. The 28th International Conference on Distributed Computing Systems Workshops, Beijing, China, 2008:551-556
[142]Jose V B M, Francisco M P, et al. Distributed robotic architecture based on smart services. IEEE International Conference on Industrial Informatics, Singapore,2006:480-485
[143]Michele A, Francesco Z, Gianni C. A service-oriented approach for building autonomic peer-to-peer robot systems. Proceedings of the 16th IEEE International Workshops on Enabling Technologies:Infrastructure for Collaborative Enterprises, Paris, France,2007: 137-142
[144]Xie D, Ying S, Zhang T, et al. An approach for describing SOA.2nd International Conference on Wireless Communications, Networking and Mobile Computing, Wuhan, China, 2006,1-4.
[145]MacKenzie C M, Laskey K, et al. Service-oriented architecture expands the vision of web services. http://www.ibm.com/developerworks/webservices/library/ws-soaintro.html, IBM, USA,2004.