[1]霍成立,谢 凡,秦世引.面向室内移动机器人的无迹滤波实时导航方法[J].智能系统学报,2009,4(04):295-302.
 HUO Cheng-li,XIE Fan,QIN Shi-yin.A case study in realtime UKFbased navigation for indoor autonomous travel of mobile robots[J].CAAI Transactions on Intelligent Systems,2009,4(04):295-302.
点击复制

面向室内移动机器人的无迹滤波实时导航方法(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第4卷
期数:
2009年04期
页码:
295-302
栏目:
出版日期:
2009-08-25

文章信息/Info

Title:
A case study in realtime UKFbased navigation  for indoor autonomous travel of mobile robots
文章编号:
1673-4785(2009)04-0295-08
作者:
霍成立谢 凡秦世引
北京航空航天大学 自动化科学与电气工程学院,北京 100191)
Author(s):
HUO Cheng-li XIE Fan QIN Shi-yin
School of Automation Science and Electrical Engineering, BeiHang University, Beijing 100083, China
关键词:
室内自主漫游无迹卡尔曼滤波实时导航移动机器人
Keywords:
indoor autonomous cruise UKF realtime navigation mobile robots
分类号:
TP242.6
文献标志码:
A
摘要:
根据轮式移动机器人室内自主漫游的目标要求和约束条件,提出了一种新的实时导航策略,利用UKF(unscented Kalman filter)算法对置顶相机和里程计获取的移动机器人位姿信息进行在线滤波,为实现移动机器人的室内自主漫游控制进行实时导航定位.实验结果表明,提出的导航策略和实现算法满足实时性要求,具有较高的精度,可为进一步的实际应用提供参考.
Abstract:
A new realtime navigation strategy was proposed that reflects the requirements and constraints for indoor autonomous travel of wheeled mobile robots. The unscented Kalman filter (UKF) algorithm was employed to carry out online filtering of mobile robot positional data collected from ceiling cameras and the odometer. This allows realtime navigation and positioning for control of indoor autonomous travel. A series of experiments demonstrated that this navigational strategy and implementation algorithm can meet realtime high precision requirements. These results make our strategy a good reference for further research that will lead to practical applications.

参考文献/References:

[1]刘方湖.管道形轮腿式月球探测机器人及其运动特性的研究[D].上海:上海交通大学,2002.
 LIU Fanghu. A pipelineshaped wheellegged lunar exploration robot and its locomotion characteristics research[D].Shanghai:Shanghai Jiao Tong University,2002. 
[2]FLOBERGHAGEN R,BOUMAN J. On the information content and regularization of lunnar gravity field solutions[M]. Delf :Delft University Press,1998:1522.
[3]SIMMONS R, APFELBAUM D, BURGARD W, et al. Coordination for multirobot exploration and mapping[C]// Texas: Proceedings of National Conference on Artificial Intelligence.[S.l.]:AAAI,2000:852858.
[4】LAI X C, KONG C Y, GE S S, et al. Online map building for autonomous mobile robots by fusing laser and sonar data[C]// Proceedings of Conference on Mechatronics and Automation. Ontario,2005:993998.
[5】蒋新松.机器人学导论[M].辽宁:辽宁科学技术出版社,1994:57.
[6]KOREN Y, BORENSTEIN J. Potential field methods and their inherent limitations for mobile robot navigation[C]//Proceedings of the 1991 IEEE International Conference on Robotics and Automation. Sacramento. USA,1991:13981404
[7]马兆青,袁曾任.基于栅格方法的移动机器人实时导航和避障[J].机器人,1996(11):344348.
MA Zhaoqing, YUAN Zengren. Real time navigation and obstacle avoidance based on grids method for fast mobile robot[J].Robot, 1996(11):344348.
[8]段 华.室外移动机器人视觉导航关键技术研究[D].南京:南京航空航天大学,2006.
 DUAN Hua.Research on the key technology of visionbased navigation for outdoor mobile robots[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2006.
[9]吴东晖. 智能移动机器人的视觉导航技术[D].杭州:浙江大学, 2001. 
 WU Donghui.Visual navigation technology of intelligent mobile robot[D]. Hangzhou:Zhejiang University, 2001.
[10]JULIER S J. The scaled unscented transformation[C]//Proceedings of the 2002 American Control Conference.[S.l.],2002: 45554559.
 [11]WELCH G, BISHOP G. An introduction to the Kalman filer[R]. University of North Carolina at Chapel Hill: TR 95041, 2004.
[12]SINGER R A. Estimating optimal tracking filter performance for manned maneuvering targets[J].IEEE Trans on Aerospace and Electronic Systems, 1970, 5(4):473483.
[13]CHAN Y T, HU A G C, PLANT J B. A Kalman filter based tracking scheme with input estimation[J].IEEE Trans on Aerospace and Electronic Systems, 1979,15(2):237244.
[14]LERROD, BARSHALOM Y K. Tracking with debiased consistent converted measurement vs EKF[J]. IEEE Trans on Aerospace and Electronics Systems, 1993, 29(3):10151022.
[15]JULIER S J, UHLMANN J K. A new method for the nonlinear transformation of means and covariances in filters and estimators[J].IEEE Trans 1A 1C1, 2000, 45(3):47724821.

备注/Memo

备注/Memo:
收稿日期:2009-04-13.
基金项目:国际科技合作基金资助项目(2007DFA11530);国家高技术研究发展计划(863)基金资助项目(2006AA04Z207);国家自然科学基金资助项目(60875072);教育部博士点基金资助项目(20060006018).
通信作者:霍成立.E-mail:hchlsy@sina.com.
作者简介:
霍成立,男,1979年生,硕士研究生,主要研究方向为基于视觉轮式移动机器人的导航算法与实现技术. 
 谢 凡,男,1982年生,博士研究生,主要研究方向为多机器人系统的协同优化控制.
 秦世引,男,1955年生,博士,教授,博士生导师,现任中国人工智能学会秘书长,智能控制与智能管理专业委员会副主任,中国自动化学会智能自动化专业委员会委员,系统复杂性专业委员会委员,《智能系统学报》编委会副主任.主要研究方向为图像处理与模式识别、大规模多机器人系统智能优化控制、复杂系统与复杂性科学等.1999年入选“北京市跨世纪优秀人才工程”人选.发表学术论文110余篇,合著出版学术专著2部,研究生教材1部,译著2部.
更新日期/Last Update: 2009-11-16