室内未知环境下移动机器人导航关键问题研究
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摘要
移动机器人是一种在复杂的环境下工作的具有自规划、自组织、自适应能力的机器人。移动机器人在未知环境下的导航问题是制约当前移动机器人大规模应用的瓶颈。未知环境导航的其中两个关键问题是环境地图的实时建立和机器人的自定位。本文将重点放在了室内环境下机器人自身位姿和环境模型均未知情况下的移动机器人导航问题,即所谓的并发环境建模与定位问题(SLAM:Simultaneous Localization And Mapping)。
论文首先介绍了SLAM问题涉及的两个关键技术,即环境地图的典型表达及机器人位姿的不同估计方法。其次结合超声测距特性,给出了基于旋转超声的贝叶斯网格地图的构建过程,并通过霍夫(Hough)变换改进了地图构建方式,增加了建模精度。本课题选用粒子滤波作为定位方法,文章介绍了粒子滤波定位的算法,并将直方图匹配引入到定位算法的传感器更新阶段,提高了粒子滤波算法的效率和鲁棒性。
论文从降低数据关联的复杂度、提高实时性的角度出发,在贝叶斯栅格地图和粒子滤波定位这两种技术的基础上,提出了改进的基于粒子滤波的SLAM算法。改进的方法降低了位姿精度和地图精度的关联程度。此法在利用粒子滤波定位时,只需要在机器人附近分布粒子就能满足算法需要,减少了粒子数目,增强了实时性,仿真和实验结果均表明它是一种可行的方法。
Mobile robot is a kind of robot which works in complicated environments and has an ability of self-programming, self-organizing and self-adapting. Mobile robot navigation in unknown environment is the bottleneck of using mobile robot broadly. The two key questions of navigation are building map in real time and obtaining robot’s pose precisely. This paper mainly focuses on mobile robot navigation in indoor environment where neither the pose of the robot nor the environment information is known, i.e. the so called Simultaneous Localization and Mapping (SLAM) problem.
Firstly this paper introduces the two key techniques of SLAM, one of which is representative expression means of environmental map, and another is typical localization methods of mobile robots. On the basis of introducing the characteristic of sonar, the process of building grid map using Bayesian rule is presented. In order to increase the precision of the built map, Hough transform is applied. Particle filter is used to localize robot and its algorithm is explained. A histogram matching is utilized in the sensor-data-update phase of particle filter localization. Experiments show it makes the algorithm more efficient and robust.
To reduce the complexity of data association and enhance the real-time performance of the algorithm, an improved SLAM algorithm is put forward using Bayesian gird map and partial filter. This algorithm reduces the association between the precision of map and that of robot pose. Particles are distributed only around the robot, so it promotes the real-time performance. Simulation and experiments results validate the feasibility of algorithm.
论文首先介绍了SLAM问题涉及的两个关键技术,即环境地图的典型表达及机器人位姿的不同估计方法。其次结合超声测距特性,给出了基于旋转超声的贝叶斯网格地图的构建过程,并通过霍夫(Hough)变换改进了地图构建方式,增加了建模精度。本课题选用粒子滤波作为定位方法,文章介绍了粒子滤波定位的算法,并将直方图匹配引入到定位算法的传感器更新阶段,提高了粒子滤波算法的效率和鲁棒性。
论文从降低数据关联的复杂度、提高实时性的角度出发,在贝叶斯栅格地图和粒子滤波定位这两种技术的基础上,提出了改进的基于粒子滤波的SLAM算法。改进的方法降低了位姿精度和地图精度的关联程度。此法在利用粒子滤波定位时,只需要在机器人附近分布粒子就能满足算法需要,减少了粒子数目,增强了实时性,仿真和实验结果均表明它是一种可行的方法。
Mobile robot is a kind of robot which works in complicated environments and has an ability of self-programming, self-organizing and self-adapting. Mobile robot navigation in unknown environment is the bottleneck of using mobile robot broadly. The two key questions of navigation are building map in real time and obtaining robot’s pose precisely. This paper mainly focuses on mobile robot navigation in indoor environment where neither the pose of the robot nor the environment information is known, i.e. the so called Simultaneous Localization and Mapping (SLAM) problem.
Firstly this paper introduces the two key techniques of SLAM, one of which is representative expression means of environmental map, and another is typical localization methods of mobile robots. On the basis of introducing the characteristic of sonar, the process of building grid map using Bayesian rule is presented. In order to increase the precision of the built map, Hough transform is applied. Particle filter is used to localize robot and its algorithm is explained. A histogram matching is utilized in the sensor-data-update phase of particle filter localization. Experiments show it makes the algorithm more efficient and robust.
To reduce the complexity of data association and enhance the real-time performance of the algorithm, an improved SLAM algorithm is put forward using Bayesian gird map and partial filter. This algorithm reduces the association between the precision of map and that of robot pose. Particles are distributed only around the robot, so it promotes the real-time performance. Simulation and experiments results validate the feasibility of algorithm.
引文
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[2] 王鹏飞,孙立宁,黄博,地面移动机器人系统的研究现状与关键技术,机械设计,2006 年 7 月,第 3 卷第 7 期
[3] 李群明, 熊蓉, 褚健. 室内自主移动机器人定位方法研究综述,机器人,2003.11, Vol.25(6): 560-567+573.
[4] 厉茂海 洪炳熔. 移动机器人的概率定位方法研究进展,机器人,2005, Vol.27 (4): 380-384.
[5] 王卫华, 陈卫东, 等. 基于不确定信息的移动机器人地图创建研究进展. 机器人,2001, Vol.23 (6): 563-568.
[6] S. Thrun. Robotic mapping: A survey. In G. Lakemeyer and B. Nebel, editors, Exploring Artificial Intelligence in the New Millenium. Morgan Kaufmann, 2002.
[7] 王璐 蔡自兴. 未知环境中移动机器人并发建图与定位(CML)的研究进展, 机器人,2004, Vol.26 (4): 380-384.
[8] 罗荣华 洪炳镕. 移动机器人同时定位与地图创建研究进展,机器人,2004, Vol.26 (2): 182-186.
[9] 陈卫东, 张飞,移动机器人的同步自定位与地图创建研究进展,控制理论与应用,2005 年,第 22 卷第 3 期, pp.455-460
[10] 迟健男, 徐心和. 移动机器人即时定位与地图创建问题研究. 机器人,2004, Vol.26 (1): 92-96.
[11] 贺伟,梁昔明,未知环境中移动机器人 SLAM 问题的研究进展,微计算机信息,2005 年第 21 卷第 3 期,179-180
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[35] 能力风暴智能机器人开发系统使用手册,上海广茂达伙伴机器人有限公司
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[42] S.Williams, P.Newman, M.Dissanayake, J.Rosenblatt,and H.Durrant-Whyte, A decoupled, distributed AUV control architecture, in proc.31st Int.Symp. Robot.,vol.1, Montreal,PQ, Canada, May 2000,pp.246-251
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[2] 王鹏飞,孙立宁,黄博,地面移动机器人系统的研究现状与关键技术,机械设计,2006 年 7 月,第 3 卷第 7 期
[3] 李群明, 熊蓉, 褚健. 室内自主移动机器人定位方法研究综述,机器人,2003.11, Vol.25(6): 560-567+573.
[4] 厉茂海 洪炳熔. 移动机器人的概率定位方法研究进展,机器人,2005, Vol.27 (4): 380-384.
[5] 王卫华, 陈卫东, 等. 基于不确定信息的移动机器人地图创建研究进展. 机器人,2001, Vol.23 (6): 563-568.
[6] S. Thrun. Robotic mapping: A survey. In G. Lakemeyer and B. Nebel, editors, Exploring Artificial Intelligence in the New Millenium. Morgan Kaufmann, 2002.
[7] 王璐 蔡自兴. 未知环境中移动机器人并发建图与定位(CML)的研究进展, 机器人,2004, Vol.26 (4): 380-384.
[8] 罗荣华 洪炳镕. 移动机器人同时定位与地图创建研究进展,机器人,2004, Vol.26 (2): 182-186.
[9] 陈卫东, 张飞,移动机器人的同步自定位与地图创建研究进展,控制理论与应用,2005 年,第 22 卷第 3 期, pp.455-460
[10] 迟健男, 徐心和. 移动机器人即时定位与地图创建问题研究. 机器人,2004, Vol.26 (1): 92-96.
[11] 贺伟,梁昔明,未知环境中移动机器人 SLAM 问题的研究进展,微计算机信息,2005 年第 21 卷第 3 期,179-180
[12] 张 恒, 樊晓平, 刘艳丽,移动机器人同步定位与地图构建研究进展,数据采集与处理,2005 年 12 月第 20 卷第 4 期, 458-465
[13] Hugh Durrant-Whyte, Tim Bailey, Simultaneous Localization and Mapping: Part I. IEEE Robotics & Automation Magazine, June, 2006, pp.99-108.
[14] Tim Bailey, Hugh Durrant-Whyte, Simultaneous Localization and Mapping: Part II. IEEE Robotics & Automation Magazine, September, 2006, pp.108-117.
[15] Chatila M., Mobile Robot Navigation: Space modeling and decisional processes, Proc. Of 30 International Symposium on Robotics Research, France, 1985, 373-379
[16] Drumhellor M., Mobile Robot Localization using sonar, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, PAMI-9(2), 325-332
[17] Castellanos J.A., Tardos J.D., Mobile localization and map building: A Multisensor Fusion Approach, Kluwer Academic Publisher, 2000
[18] Gorman N.F., Vestli S.J., Schweitzer G., The service robot MOPS: first operating experiences, Robots and Autonomous Systems, 34 , 2001, 165-173
[19] Cassandra A.R., Kaelbling L.P., Kuiren J.A., Acting under uncertainty: Discrete Bayesian models for mobile robot navigation, Proc. IEEE/RSJ International Conference on Robotics and Systems, 1996, V.2, 963-979
[20] Smith R., Self M., Cheeseman P., A stochastic map for uncertain spatial relationships, The Fourth International Symposium on Robotics Research, 1988, 467-471
[21] Moravec H.P., Elfes, A High resolution maps from wide angle sonar, Computers in Engineering, Proceedings of the International Computers in Engineering Conference and 1, 1985, pp. 375-380
[22] Burgard W., et al., Estimating the absolute position of a mobile robot using position probability grids, Proceedings of the national conference on artificial intelligence, Portland, Oregon, USA, 1996, 896-902
[23] Crowley J. L., Wallner F., Schiele B., Position estimation using principal components of range data, Proceedings of the IEEE International Conference on Robotics and Automation, 1998, 3121-3128
[24] Cox I., Blanche, An experiment in guidance and navigation of an autonomous robot vehicle, IEEE Transactions on Robotics and Automation 7(2), 1991, Borenstein J., Everett B., Feng L., Navigating Mobile Robots, Systems and Techniques. A. K. Peters, Ltd., 1996.
[25] 潘学军,自主移动机器人地图构建、探索及定位研究,[硕士学位论文],大连:大连理工大学,2004
[26] Gutmann J. S., Weigel T., Nebel B., A fast accurate and robust method for self-localization in polygonal environments using laser range finders, Advanced Robotics Journal, 2001, 14(8), 651-668.
[27] Fox D, Thrun S, Dellaert F,et al, Markov localization for mobile robots in dynamic environments, Journal of Artificial Intelligence Research, 1999,11
[28] R. Kalman, A new approach to linear filtering and prediction problems, Trans. ASME J. Basic Eng., vol. 82, pp. 35–45, 1960.
[29] Dellaert F,Burgard W,Fox D., et al, Monte Carlo localization for mobile robots, Artificial Intelligence. Proceedings of the IEEE International Conference on Robotics and Automation(ICRA'98) Detroit M1, 1999, 1322-1328
[30] Ioannis Rekleitis, A Particle Filter Tutorial for Mobile Robot Localization, Technical Report TR-CIM-04-02, Centre for Intelligent Machines, McGill University, Montreal, Quebec, Canada, 2004.
[31] 张明路,丁承君,段萍,移动机器人的研究现状与趋势,河北工业大学学报,2004 年 4 月,第 33 卷第 2 期,110-115
[32] Enzo Mumolo, Kristijan Lenacyy and Massimiliano Nolichy, Spatial Map Building using Fast Texture Analysis of Rotating Sonar Sensor Data for Mobile Robots, International Journal of Pattern Recognition and Arti_cial Intelligence Vol. 19, No. 1 (2005) 1-20
[33] Robin R.Murphy[美],杜军平,胡金春.人工智能机器人学导论,北京:电子工业出版社,2004
[34] Moravec, H.P., Senor Fusion in Certainty Grids for Mobile Robots, AI Magazine, vol.9, no.2, 1988, pp.61-74
[35] 能力风暴智能机器人开发系统使用手册,上海广茂达伙伴机器人有限公司
[36] C.S. Tong, Y.W. Yeung, Boundary location from range data using Hough transform, Pattern Recognition 34 (2001), 1975-1982
[37] Xiaoping Yun, Khine Latt and J. Scott Glennon, Mobile robot Localization Using the Hough Transform and Neural Networks, Proceedings of the 1998 IEEE ISIC/ CIRNISAS Join1 Conference Gaithersburg, MD September 14-17,1998, 393-400
[38] 吴奇,复杂结构文档图像的倾斜角度检测,北京大学[本科生毕业论文],2004.5
[39] SMITH R , SELF M, CHESSEMAN P. Estimating uncertain spatial relationships in robotics, Uncertainty in Artificial Intelligence,1988 , 2 : 435 – 461
[40] J.Leonard and H.Durrant-Whyte. Simultaneous map building and Localization for an autonomous mobile robots.In Proc.IEEE Int. Wkshp. Intell. Robots Syst., Osaka, Japan, pp.1442-1447
[41] J.Castellanos,J.Martinez,J.Neira,and J.Tardos,Simultaneous map building and localization for mobile robots:a multisensor fusion approach, in Proc.IEEE Conf.Robot. Automat., Belgium, May 1998,pp.1244-1249
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