多移动机器人系统地图创建技术研究
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摘要
移动机器人在未知环境中自主完成任务在工业、民用以及军事等领域具有广泛的应用前景。自主式移动机器人在未知环境下工作,必须具有对未知环境进行探测和环境地图创建的能力。本文主要研究移动机器人在未知环境中自主进行地图创建问题,地图创建在移动机器人研究中有着非常重要的意义,是实现自主导航,在未知环境中完成复杂智能任务的关键,也集中体现了机器人的感知能力和智能水平。本文以实现未知环境下的多机器人地图创建为背景,主要工作如下:
首先,本文回顾了移动机器人环境地图创建的研究现状,地图创建中存在的相关问题,并对本文的选题背景和主要内容作了介绍。
然后,针对基于声纳的室内结构化环境地图创建,运用D-S证据理论对多传感器数据进行融合,使用自适应RCF因子消除大量不确定信息,建立了基于栅格的地图表示。通过使用Pioneer3机器人在实际环境中采集数据,初步建立起环境的栅格地图,验证了数据融合算法的有效性。实验表明,基于D-S证据理论的数据融合算法在机器人环境建模中取得了良好效果。
其次,在已建立的环境地图基础上,为进一步消除不确定信息的影响,使用图像处理问题中经常用到的hough变换对原始声纳数据进行直线特征的提取。经过对声纳数据进行预处理,既改善了建立环境地图的质量,又提高了建立地图的效率,为实现移动机器人在未知环境中进行快速、有效的地图创建提供了良好的基础,从而提高了机器人自主运行、导航、路径规划的能力。
第四,为提高机器人实现预定任务的效率,研究了多机器人协作地图创建的问题,分析了多机器人地图融合算法,实现了机器人在不知道相互位置情况下的地图融合。基于地图相似度的概念,利用遗传算法在全局空间搜索,其对数据的并行处理能力改善了全局搜索效率和地图融合的效果。
最后,对本文所做的工作和取得的成果加以总结,并分析了可以进一步进行改进的地方并对未来发展进行了展望。
Mobile robots which can autonomously accomplish tasks in unknown environment have wide potentials for application in industry, civilian life and military. Autonomous mobile robots should be able to explore and map unknown environment for accomplishing various tasks. Map building embodies the perception ability and intelligence of robots. The dissertation focuses on the related problems of map building in unknown environment. The main work of this dissertation is as follows:
Firstly, the dissertation reviews the state-of-the-art research of map building of mobile robot and introduces background and main contents of this thesis.
Secondly, D-S evidence theory is applied to fuse the sonar data for occupancy grid map building in indoor structural environment. Self adaptive RCF factor is used to eliminate uncertainty. We have done experiments with the Pioneer3 mobile robot. Experiment results prove that data fusion algorithm based on D-S evidence theory gain satisfactory effect in modeling environment for mobile robot.
Thirdly, hough transform which is usually used in image process problem is applied to extract the line characteristic of original sonar data for eliminating the impact of uncertain information further. After sonar data being preprocessed, not only the quality of the built map but also mapping efficiency is greatly improved. This will provide good basis for fast and efficient map building of mobile robot in unknown environment and improve the ability of autonomous operation, navigation and path planning further.
Fourthly, for improving the efficiency of implementing expected tasks, we have done some research work on map building for multi-robot. After reviewing and analyzing the algorithm of map merging for multi-robot, the paper implements the map merging process for mobile robots without knowing the relative positions of robots. Based on the map similarity concept, this dissertation applies the genetic algorithm search strategy to search in the global world. The parallel search strategy improves the efficiency of globally search and effect of map merging.
Finally, conclusions are provided. Outlook of future research is ananlyzed.
首先,本文回顾了移动机器人环境地图创建的研究现状,地图创建中存在的相关问题,并对本文的选题背景和主要内容作了介绍。
然后,针对基于声纳的室内结构化环境地图创建,运用D-S证据理论对多传感器数据进行融合,使用自适应RCF因子消除大量不确定信息,建立了基于栅格的地图表示。通过使用Pioneer3机器人在实际环境中采集数据,初步建立起环境的栅格地图,验证了数据融合算法的有效性。实验表明,基于D-S证据理论的数据融合算法在机器人环境建模中取得了良好效果。
其次,在已建立的环境地图基础上,为进一步消除不确定信息的影响,使用图像处理问题中经常用到的hough变换对原始声纳数据进行直线特征的提取。经过对声纳数据进行预处理,既改善了建立环境地图的质量,又提高了建立地图的效率,为实现移动机器人在未知环境中进行快速、有效的地图创建提供了良好的基础,从而提高了机器人自主运行、导航、路径规划的能力。
第四,为提高机器人实现预定任务的效率,研究了多机器人协作地图创建的问题,分析了多机器人地图融合算法,实现了机器人在不知道相互位置情况下的地图融合。基于地图相似度的概念,利用遗传算法在全局空间搜索,其对数据的并行处理能力改善了全局搜索效率和地图融合的效果。
最后,对本文所做的工作和取得的成果加以总结,并分析了可以进一步进行改进的地方并对未来发展进行了展望。
Mobile robots which can autonomously accomplish tasks in unknown environment have wide potentials for application in industry, civilian life and military. Autonomous mobile robots should be able to explore and map unknown environment for accomplishing various tasks. Map building embodies the perception ability and intelligence of robots. The dissertation focuses on the related problems of map building in unknown environment. The main work of this dissertation is as follows:
Firstly, the dissertation reviews the state-of-the-art research of map building of mobile robot and introduces background and main contents of this thesis.
Secondly, D-S evidence theory is applied to fuse the sonar data for occupancy grid map building in indoor structural environment. Self adaptive RCF factor is used to eliminate uncertainty. We have done experiments with the Pioneer3 mobile robot. Experiment results prove that data fusion algorithm based on D-S evidence theory gain satisfactory effect in modeling environment for mobile robot.
Thirdly, hough transform which is usually used in image process problem is applied to extract the line characteristic of original sonar data for eliminating the impact of uncertain information further. After sonar data being preprocessed, not only the quality of the built map but also mapping efficiency is greatly improved. This will provide good basis for fast and efficient map building of mobile robot in unknown environment and improve the ability of autonomous operation, navigation and path planning further.
Fourthly, for improving the efficiency of implementing expected tasks, we have done some research work on map building for multi-robot. After reviewing and analyzing the algorithm of map merging for multi-robot, the paper implements the map merging process for mobile robots without knowing the relative positions of robots. Based on the map similarity concept, this dissertation applies the genetic algorithm search strategy to search in the global world. The parallel search strategy improves the efficiency of globally search and effect of map merging.
Finally, conclusions are provided. Outlook of future research is ananlyzed.
引文
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[45]Zou Yi,Ho Yeong Khing,Chua Chin Seng,and Zhou Xiao Wei,"Multi-ultrasonic Sensor Fusion for Mobile Robots",IEEE Intelligent Vehicles Symposium,Dearborn,Michigan,USA,October 4-5,2000.
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[2]王卫华,陈卫东,席裕庚.基于不确定信息的移动机器人地图创建研究进展.机器人,2001,23(6):563-568.
[3]Kortenkamp D,Weynouth T.Topological mapping for mobile robot using a combination of sonar and vision sensing in Proceeding Twelfth NCA1,AAA1,1994.
[4]Lee S W,Byrne R H.Where am I,Sensors and methods for mobile robot positioning,The University of Michigan.
[5]Kuc R,Siegel M W,Physically based simulation model for acoustic sensor robot navigation.IEEE Trans Pattern analysis machine Intelligence,1987,9(6):766-788.
[6]Jiming Liu,J-B Wu,David A.Malur,Evolutionary self-organization of an artificial potential field map with a group of autonomous robots Evolutionary Computation,1999,CEC 99,Proceedings of the 1999Congress on,1999.
[7]Ioannis M.Rekleitis,Gregory Dudek,Evangelos E.Milios,Multi-robot collaboration for robust exploration,Robotics and Automation,2000,Proceedings,ICRA'00.IEEE International Conference on,2000,4.
[8]Elfes A,Moravec H,High resolution maps from wide angle sonar.IEEE Int Conf Robotics and Automation,1985:116-121.
[9]W.Burgard,A.B.Cremers,D.Fox,et al.Experiences with an interactive museum tour-guide robot.Artificial Intelligence,1999,1,3-55.
[10]Yamauchi Brian,Decentralized coordination for multirobot exploration,Robotics and Autonomous Systems 1999,29,111-118.
[11]Chong K S,Kleeman Lindsay.Mobile robot map building from an advanced sonar array and accurate odometry.International Journal of Robotics Research,1999,18(1):20-26.
[12]王卫华,陈卫东,席裕庚.移动机器人地图创建中的不确定传感信息处理.上海交通大学自动化研究所智能机器人系统与技术研究中心.
[13]Castellanos J A,Martinez J M,Neira J.Simulataneous map building and localization for mobile robots:A multisensor fusion approach.IEEE Int Conf Robotics&Automation,1998:1244-1249
[14]Kortenkamp D,Weynouth T.Topological mapping for mobile robot using a combination of sonar and vision sensing.In Proceeding Twelfth NCA1,AAAI,1994.
[15]Cox,I.J.Blanche.An Experiment in Guidance and Navigation of an Autonomous Mobile Robot,IEEE Transactions Robotics and Automation,7(3),pp.193-204,1991.
[16]Barshan B.and Durrant Whyte,H.F.An Inertial Navigation System for a Mobile Robot.Proceedings of the 1993 IEEE/RSJ International Conference on Intelligent Robotics and Systems,Yokohama,Japan,July 26-30,pp.2243-2248.
[17]Barshan B.and Durrant-Whyte,H.F.Inertial Navigation Systems Mobile Robots.IEEE Transactions on Robotics and Automation,Vol.11,No.3,June,pp.328-342.
[18]Komoriya,K.and Oyama,E.,1994,Position Estimation of a Mobile Robot Using Optical Fiber Gyroscope(OFG).International Conference on Intelligent Robots and Systems(IROS'94).Munich,Germany,Sept.12-16,pp.143-149.
[19]Elfes A.Sonar-based real-world mapping and navigation,IEEE J.Robotics Automat.3(1987)249-265.
[20]Moravec H.Sensor fusion in certainy grids for mobile robots.AI Magazine,Summer 1988,6174.
[21]Thrun S,Fox D,Burgard W.A probabilistic approach to concurrent mapping and localization for mobile robots,Machine Learning,1998,
[22]C.F.Olson.Probabilistic self-localization for mobile robots.IEEE Tran.On Robotics and Automation,2000,16(1),55-66.
[23]蔡鹤皋,金明河,金峰.卡尔曼滤波与多传感器数据融合技术.模式识别与人工智能,2000,13(3):248-253.
[24]Gambino F,Ulivi G,Vendittelli M.The transferable belief model in ultrasonic map building.IEEE Conf.Fuzzy systems,1997 601-608
[25]Pagac D,Nebot E M,Durrant-Whyte H.An evidential approach to map-building for autonomous vehicles.IEEE Trans.Robotics and Automation,14(4),1998,623-629.
[26]Oriolo G,Ulivi G,Vendittelli M.Fuzzy maps:A new tool for mobile robot perception andplanning.Joural of Robotic System 1997,14(3),179-197.
[27]Demirli K,Turksen I B.Sonar based mobile robot localization by using fuzzy triangulation.Robotics and Autonomous Systems 2000,33,109-123.
[28]Jorge Gasos,Ailson Rosetti.Uncertainty representation for mobile robots:perception,modeling and navigation in unknown environments,Fuzzy sets and systems,1999,107,1-24.
[29]S.Thrun.Learning metric-topological maps for indoor mobile robot navigation,Artificial Intelligence 1998,99,21-71.
[30]Reina,J.Gonazalez.A two-stage mobile robot localization method by overlapping segment-based maps,Robotics and autonomous systems,2000,31,213-225.
[31]Simmons R,Apfelbaum D,Burgard W,et al.Coordination for Multi-Robot Exploration andMapping,In Proceedings of the AAAI National Conference on Artificial Intelligence,Austin,TX,2000.AAAI.
[32]Skrzypczynski P,Drapikowski P.Environment modeling in a multi-agent mobile system.IEEE 1999.
[33]Kim Y J,Kim J H.Online map building evolutionary algorithm for multi-agent mobile robots with odometric uncertainty.IEEE Conf Evolutionary Computation, 2000:133-138.
[34] Alberto Elfes, " Sonar-Based Real-World Mapping and Navigation" , IEEE Journal of Robotics and Automation, VOL. RA-3, NO.3, pp.249-265, 1987.
[35] Alberto Elfes. Occupancy Grids, "A Stochastic Spatial Representation for ActiveRobot Perception" , Proceedings of the Sixth Conference on Uncertainty in AI, pp.60-70, July 1990.
[36] Andrew Howard and Les Kitchen, "Generating Sonar Maps in Highly Specular Environments " , Proceedings of the Fourth International Conference on Control, Automation, Robotics and Vision, pp.1870-1874, 1996.
[37] Brian Yamauchi, Alan Schultz, and William Adams, "Mobile Robot Exploration and Map Building with Continuous Localization" , Proceedings of the 1998, IEEE International Conference on Robotics and Automation, pp.3715-3720, 1998.
[38] Daniel Pagac. Eduardo, M. Nebot, and Hugh Durrant-Whyte, "An Evidential Approach to Map-Building for Autonomous Vehicles" , IEEE Transaction on Robotics and Automation, VOL.14, NO.4, pp.623-629, 1998.
[39] Fang Fang, Ma Xu Dong, Dai Xian Zhong, "A Multi-sensor Fusion SLAM Approach for Mobile Robots" , Proceedings of the IEEE International Conference on Mechatronics & Automation, Nigara Falls, Canada, July, 2005.
[40] Hans P. Moravec, "Sensor Fusion in Certainty Grids for Mobile Robots" , AI Magazine, VOL.9, NO.2, pp.61-74, 1988.
[41] Luca, M. Domenico, N. Daniele, "A probabilistic approach to Hough localization," International Conference on Robotics and Automation, pp. 4250-4255, 2001.
[42] John Hwan Lim and Dong Woo Cho, "Specular Reflection Probability in the Certainty Grid Representation" , Transactions of the ASME, VOL. 116, pp.512-520, 1994.
[43]Ken Hughes and Robin Murphy,"Ultrasonic Robot Localization Using Dempster-Shafer Theory",Proceedings of SPIE on Neural and Stochastic Methods in Image and Signal Processing,VOL.1766,pp.2-11,1992.
[44]Michael Drumheller,"Mobile Robot Localization Using Sonar",IEEE Transactions on Pattern Analysis and Machine Intelligence,VOL.PAMI-9,NO.2,pp.325-332,1987.
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