[1]周波,樊帅权,戴先中.基于集员滤波的移动机器人动态环境建模[J].东南大学学报(自然科学版),2011,41(1):107-112.[doi:10.3969/j.issn.1001-0505.2011.01.021]
 Zhou Bo,Fan Shuaiquan,Dai Xianzhong.Dynamic environment modeling of mobile robots based on set membership filter[J].Journal of Southeast University (Natural Science Edition),2011,41(1):107-112.[doi:10.3969/j.issn.1001-0505.2011.01.021]
点击复制

基于集员滤波的移动机器人动态环境建模()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
41
期数:
2011年第1期
页码:
107-112
栏目:
自动化
出版日期:
2011-01-20

文章信息/Info

Title:
Dynamic environment modeling of mobile robots based on set membership filter
作者:
周波樊帅权戴先中
(东南大学自动化学院, 南京 210096)
Author(s):
Zhou BoFan ShuaiquanDai Xianzhong
(School of Automation, Southeast University, Nanjing 210096, China)
关键词:
移动机器人动态环境建模目标跟踪集员滤波
Keywords:
mobile robot dynamic environment modeling target tracking set membership filter
分类号:
TP24
DOI:
10.3969/j.issn.1001-0505.2011.01.021
摘要:
针对移动机器人动态环境建模中动态的障碍物/目标跟踪定位问题,提出了一种采用集员滤波框架来解决动态障碍物/目标跟踪的方法.与传统的概率估计方法相比,该方法无需噪声的概率先验假设,仅要求噪声未知但有界,从而保证了方法的普遍性和实用性,且能够获得动态目标的状态或参数的不确定性偏差边界,有利于与后继路径规划和运动控制的结合,以提高机器人自身的稳定性和性能.此外,该方法对于跟踪过程中突发的传感器故障或数据丢失具有一定的容错和处理能力.针对多个动态目标以不同形式运动以及测量信号丢失的情况,对移动机器人的目标跟踪问题进行了相应的仿真,仿真结果验证了该方法的有效性和鲁棒性.
Abstract:
A set membership filter scheme is presented to solve the problem of dynamic obstacles or objects tracking for mobile robots. Compared with traditional probability estimation methods, the method presented in this paper does not need the priori probabilistic assumption of the noise. It only requires that the noise is unknown but bounded which ensures its universality and practicality. And this algorithm can obtain the boundaries of the estimation of state or parameters. So it can be combined with path planning and motion control conveniently which can improve the stability and performance of the robot. In addition, this method can tackle well with the case of sensor failure or sudden loss of measurement data. Simulations of multi-target tracking and situation of sudden loss of measurements have been done to verify the validity and robustness of the algorithm.

参考文献/References:

[1] Stubbs K,Hinds P J,Wettergreen D.Autonomy and common ground in human-robot interaction:a field study [J].Intelligent Systems,IEEE,2007,22(2):42-45.
[2] Kleiner A,Dornhege C.Real-time localization and elevation mapping within urban search and rescue scenarios [J].Journal of Field Robotics,2007,24(8/9):723-745.
[3] Jaulin L.A nonlinear set membership approach for the localization and map building of underwater robots [J].IEEE Transactions on Robotics,2009,25(1):88-98.
[4] Bar-Shalom Y,Li X R,Kirubarajan T.Estimation with applications to tracking and navigation [M].New York:John Wiley &Sons,2002:199-266.
[5] Grewal M S,Andrews A P.Kalman filtering:theory and practice using MATLAB [M].New York:John Wiley &Sons,2001:169-199.
[6] Martinez-Cantin R,Castellanos J A.Bounding uncertainty in EKF-SLAM:the robocentric local approach [C]//The 2006 IEEE International Conference on Robotics and Automation.Orlando,FL,USA,2006:430-435.
[7] Julier S,Uhlmann J,Durrant-Whyte H F.A new method for the nonlinear transformation of means and covariances in filters and estimators [J].IEEE Transactions on Automatic Control,2000,45(3):477-482.
[8] Julier S,Uhlmann J.Unscented filtering and nonlinear estimation [J].Proceedings of the IEEE,2004,93(2):401-422.
[9] Arulampalam S,Maskell S,Gordan N,et al.A tutorial on particle filters for online non-linear/non-Gaussian Bayesian tracking [J].IEEE Transactions on Signal Processing,2002,50(2):174-188.
[10] Kamel H,Badawy W.A smoothing Rao-Blackwellized particle filter for tracking a highly-maneuverable target [C]//2005 IEEE International Radar Conference.Arlington,Virginia,USA,2005,1:967-970.
[11] Schweppe F.Recursive state estimation:unknown but bounded errors and system inputs [J].IEEE Transactions on Automatic Control,1968,13(1):22-38.
[12] Gollamudi S,Nagaraj S,Kapoor S,et al.Set-membership state estimation with optimal bounding ellipsoids [C]//International Symposium on Information Theory and Its Applications.Victoria,BC,Canada,1996:1-4.
[13] He Q,Zhang J.Nonlinear state estimation in mobile robot using fuzzy set membership filter [C]//The 27th Chinese Control Conference.Kunming,China,2008:345-348.
[14] Blackman S S.Multiple hypothesis tracking for multiple target tracking [J].IEEE A&E Systems Magazine,2004,19(1):5-18.
[15] Gorji A,Menhaj M B,Shiry S.Multiple target tracking for mobile robots using the JPDAF algorithm [C]//The 19th IEEE International Conference on Tools with Artificial Intelligence.Patras,Greece,2007,1:137-145.
[16] Schulz D,Burgard W,Fox D,et al.People tracking with a mobile robot using sample-based joint probabilistic data association filters [J].The International Journal of Robotics Research,2003,22(2):99-116.
[17] Scholte E,Campell M.A nonlinear set-membership filter for on-line applications [J].International Journal of Robust and Nonlinear Control,2003,13(15):1337-1358.
[18] 周宏仁,敬忠良,王培德.机动目标跟踪[M].北京:国防工业出版社,1991:10-19.

相似文献/References:

[1]涂刚毅,金世俊,祝雪芬,等.基于粒子滤波的移动机器人SLAM算法[J].东南大学学报(自然科学版),2010,40(1):117.[doi:10.3969/j.issn.1001-0505.2010.01.022]
 Tu Gangyi,Jin Shijun,Zhu Xuefen,et al.Particle filter SLAM method for mobile robot[J].Journal of Southeast University (Natural Science Edition),2010,40(1):117.[doi:10.3969/j.issn.1001-0505.2010.01.022]
[2]房芳,马旭东,戴先中.一种新的移动机器人Monte Carlo自主定位算法[J].东南大学学报(自然科学版),2007,37(1):40.[doi:10.3969/j.issn.1001-0505.2007.01.010]
 Fang Fang,Ma Xudong,Dai Xianzhong.New Monte Carlo algorithm for mobile robot self-localization[J].Journal of Southeast University (Natural Science Edition),2007,37(1):40.[doi:10.3969/j.issn.1001-0505.2007.01.010]
[3]尚文,马旭东,戴先中.融合多传感器信息的移动机器人自定位方法[J].东南大学学报(自然科学版),2004,34(6):784.[doi:10.3969/j.issn.1001-0505.2004.06.015]
 Shang Wen,Ma Xudong,Dai Xianzhong.Mobile robot self-localization based-on multi-sensory information fusion[J].Journal of Southeast University (Natural Science Edition),2004,34(1):784.[doi:10.3969/j.issn.1001-0505.2004.06.015]
[4]周波,戴先中.基于SR-UKF的移动机器人主动故障检测和容错控制[J].东南大学学报(自然科学版),2011,41(5):1002.[doi:10.3969/j.issn.1001-0505.2011.05.021]
 Zhou Bo,Dai Xianzhong.SR-UKF based active fault detection and tolerant control of mobile robots[J].Journal of Southeast University (Natural Science Edition),2011,41(1):1002.[doi:10.3969/j.issn.1001-0505.2011.05.021]
[5]房芳,马旭东,戴先中.基于混合模型的移动机器人同时定位与环境建模[J].东南大学学报(自然科学版),2009,39(5):923.[doi:10.3969/j.issn.1001-0505.2009.05.011]
 Fang Fang,Ma Xudong,Dai Xianzhong.Mixed-model based simultaneous localization and mapping approach for mobile robot[J].Journal of Southeast University (Natural Science Edition),2009,39(1):923.[doi:10.3969/j.issn.1001-0505.2009.05.011]
[6]李新德,金晓彬,张秀龙,等.一种基于BoW物体识别模型的视觉导航方法[J].东南大学学报(自然科学版),2012,42(3):393.[doi:10.3969/j.issn.1001-0505.2012.03.001]
 Li Xinde,Jin Xiaobin,Zhang Xiulong,et al.Visual navigation method based on BoW object recognition model[J].Journal of Southeast University (Natural Science Edition),2012,42(1):393.[doi:10.3969/j.issn.1001-0505.2012.03.001]

备注/Memo

备注/Memo:
作者简介:周波(1981—),男,博士,讲师,zhoubo@seu.edu.cn.
基金项目:国家高技术研究发展计划(863计划)资助项目(2006AA040202).
引文格式: 周波,樊帅权,戴先中.基于集员滤波的移动机器人动态环境建模[J].东南大学学报:自然科学版,2011,41(1):107-112.[doi:10.3969/j.issn.1001-0505.2011.01.021]
更新日期/Last Update: 2011-01-20