自主式移动机器人研制与基于粒子滤波的移动机器人SLAM研究
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摘要
随着自主式移动机器人技术的不断发展,对移动机器人能够自主完成任务的要求越来越高。自主式移动机器人要能够在各种复杂环境下工作,需解决未知动态环境下自主导航这项艰巨的任务,而未知环境下自主移动机器人的同步自定位与地图创建是实现其自主导航的首要条件。
论文在分析自主式移动机器人功能、结构的基础上,确定了传感器系统和布局,设计了声纳数据采集模块、红外数据采集模块的硬件电路,制定了系统的CAN总线通信协议,编写了各模块的软件和上位机CAN总线通信软件,开发了一种新型的基于CAN总线的移动机器人控制系统。设计了一种新型的基于EPOS运动控制器的移动机器人运动控制系统,并对EPOS运动控制器进行了应用开发。在此基础上,开发研制了QDU-Ⅲ型自主式移动机器人。提出了一种利用声纳传感器信息,基于Rao-Blackwellized粒子滤波器和概率栅格地图的移动机器人同步自定位与地图创建(SLAM)的方法,给出了SLAM程序流程图,采用C++语言编写了相应的SLAM程序。进行了室内走廊环境地图创建实验,验证了提出的基于Rao-Blackwellized粒子滤波和概率栅格地图的SLAM算法可有效地跟踪移动机器人在全局坐标系下的运动轨迹,可满足移动机器人室内环境地图创建需要。
With the continuous development of autonomous mobile robotics, the mobile robots are expected to independently accomplish more and more complicated tasks. The problem of autonomous navigation must be solved firstly, with the ability of SLAM (Simultaneous Localization and Mapping) in unknown environments as the prerequisite, in order that the robots can work in more complex environments.
According to the function and architecture of autonomous mobile robot, sensor system and its layout on robot, circuits of sonar and IR sensor data acquisition module, CAN bus communication protocols and software for CAN bus communication are designed and a new type of mobile robot control system based on CAN bus technology is developed. A new type of mobile robot motion control system based on EPOS motion controller was also designed, together with the software for EPOS application. Based on all of the work above, the QDU-Ⅲautonomous mobile robot was successfully designed and developed. A method of robot simultaneous localization and mapping based on particle filter and probability grid maps using sonar range data is proposed, and a program using C++ programming language was designed for this method based on SLAM program flow chart. The robot mapping experiments was carried out in corridor, and the results shows that the method of robot simultaneous localization and mapping based on particle filter and probability grid maps proposed in the paper can follow the track of mobile robot effectively, meeting the requirements of robot mapping in indoor environment completely.
论文在分析自主式移动机器人功能、结构的基础上,确定了传感器系统和布局,设计了声纳数据采集模块、红外数据采集模块的硬件电路,制定了系统的CAN总线通信协议,编写了各模块的软件和上位机CAN总线通信软件,开发了一种新型的基于CAN总线的移动机器人控制系统。设计了一种新型的基于EPOS运动控制器的移动机器人运动控制系统,并对EPOS运动控制器进行了应用开发。在此基础上,开发研制了QDU-Ⅲ型自主式移动机器人。提出了一种利用声纳传感器信息,基于Rao-Blackwellized粒子滤波器和概率栅格地图的移动机器人同步自定位与地图创建(SLAM)的方法,给出了SLAM程序流程图,采用C++语言编写了相应的SLAM程序。进行了室内走廊环境地图创建实验,验证了提出的基于Rao-Blackwellized粒子滤波和概率栅格地图的SLAM算法可有效地跟踪移动机器人在全局坐标系下的运动轨迹,可满足移动机器人室内环境地图创建需要。
With the continuous development of autonomous mobile robotics, the mobile robots are expected to independently accomplish more and more complicated tasks. The problem of autonomous navigation must be solved firstly, with the ability of SLAM (Simultaneous Localization and Mapping) in unknown environments as the prerequisite, in order that the robots can work in more complex environments.
According to the function and architecture of autonomous mobile robot, sensor system and its layout on robot, circuits of sonar and IR sensor data acquisition module, CAN bus communication protocols and software for CAN bus communication are designed and a new type of mobile robot control system based on CAN bus technology is developed. A new type of mobile robot motion control system based on EPOS motion controller was also designed, together with the software for EPOS application. Based on all of the work above, the QDU-Ⅲautonomous mobile robot was successfully designed and developed. A method of robot simultaneous localization and mapping based on particle filter and probability grid maps using sonar range data is proposed, and a program using C++ programming language was designed for this method based on SLAM program flow chart. The robot mapping experiments was carried out in corridor, and the results shows that the method of robot simultaneous localization and mapping based on particle filter and probability grid maps proposed in the paper can follow the track of mobile robot effectively, meeting the requirements of robot mapping in indoor environment completely.
引文
[1]李磊,叶涛,谭民等.移动机器人技术研究现状与未来[J].机器人,2002,24(5):475-480.
[2]徐国华,谭民.移动机器人的发展现状及其趋势[J].机器人技术与应用,2001(3):7-13.
[3]Leonard J,Durrant-Whyte H F.Mobile Robot Localization by Tracking Geometric Beacons[J].IEEE Transactions on Robotics and Automation,1991,7(3):376-382.
[4]Smith R,Self M,Chessman P.A Stochastic Map for Uncertain Spatial Relationships [C].Proceedings of the 4th International Symposium on Robotic Research.Cambridge MA,MIT Press,1987:467-474.
[5]谢昭贤,黄大贵,明爱国等.基于同时发射声纳环移动机器人导航系统[J].电子科技大学学报,2007,36(2):209-311.
[6]Elfes A.Sonar-based Real world Mapping and Navigation[J].IEEE Journal of Robotics and Automation,1987,3(3):249-265.
[7]Elfes A.Occupancy Grids:A probabilistic Framework for Robot Perception and Navigation [D].Pittsburgh:Department of Electrical and Computer Engineering,Carnegie Mellon University,1989.
[8]Avots D,Lim E,Thibaux R,Thrun S.A Probabilistic Technique for Simultaneous Localization and Door State Estimation with Mobile Robots in Dynamic Environments[J].Proceeding of Conference on Intelligent Robots and Systems,2002.
[9]Aycard O,Charpillet F,Fohr D,Mari J.Place Learning and Recognition Using Hidden Markov Models[C].Proceedings of the IEEE International Conference on Intelligent Robots and Systems,1997:1741-1746.
[10]Simmons R,Koenig S.Probabilistic Robot Navigation in Partially Observable Environments [C[.International Joint Conference on Artificial Intelligence,1995:1080-1087.
[11]Chong K,Kleeman L.Mobile Robot Map Building from an Advanced SonarArray and Accurate Odometry[J].International Journal of Robotics Research,1999,18(1):20-36.
[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[J].自动化学报,2003,29(2):267-274.
[13]李新德,黄心汉,王敏.基于经典DSmT的Sonar栅格地图创建[J].计算机应用研究,2007,24(3):209-211.
[14]Kortenkamp D,Weynouth T.Topological Mapping for Mobile Robots Using a Combination of Sonar and Vision Sensing[C].Proceeding of the 12th National Conference on Artificial Intelligence,Menlo Park:AAAI Press,1994:979-984.
[15]Borenstein J,Everett H.R,Feng L.Mobile Robot Positioning:Sensors and Techniques[J].Journal of Robotic Systems,1997,14(4):231-249.
[16]Guivant J,Nebot E.Optimization of Simultaneous Localization and Map Building Algorithm for Real Time Implementation[J].IEEE Transactions on Robotics and Automation,2001,17(3):242-257.
[17]胡士强,敬忠良.粒子滤波算法综述[J].控制与决策,2005,20(4):361-365.
[18]张共愿,赵忠.粒子滤波及其在导航系统中的应用综述[J].中国惯性技术学报,2006,14(6):91-94.
[19]于金霞,蔡自兴,段琢华.基于粒子滤波的移动机器人定位关键技术研究综述[J].计算机应用研究,2007,24(11):9-14.
[20]余洪山,王耀南.基于粒子滤波器的移动机器人定位和地图创建研究进展[J].机器人,2007,29(3):281-289.
[21]Hahnel D,Burgard W,Fox D.An Efficient FastSLAM Algorithm for Generating Maps of Large-scale Cyclic Environments Form Row Laser Range Measurements[C].Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS 2003),2003:206-211.
[22]Eliazar A,Parr R.DP-SLAM:Fast,Robust Simultaneous Localization and Mapping without Predetermined Landmarks[C].Proceedings of the 18th International Joint Conference on Artificial Intelligence(IJCAI-03),2003(18):1135-1142.
[23]Fox D.Adapting the Sample Size in Particle Filters through KLD-sampling[J].International Journal of Robotic Research,2003,22(1):985-1004.
[24]Grisetti G,Stachniss C,Burgard W.Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling[C].Proceedings of the 2005 IEEE International Conference on Robotics and Automation(ICRA 2005),2005:2432-2437.
[25]Schroter Ch,Bohme H J,Gross H.M.Memory-efficient Gridmaps in Rao-Blackwellized Particle Filters for SLAM Using Sonar Sensors[C].Proceedings of the 3rd European Conference on Mobile Robots(ECMR 2007).2007:138-143.
[26]历茂海,洪炳熔,罗荣华.用改进的Rao-Blackwellized粒子滤波器实现移动机器人同时定位和地图创建[J].吉林大学学报,2007,37(2):402-406.
[27]武二永,项志宇,沈敏一等.大规模环境下基于激光雷达的机器人SLAM算法[J].浙江大学学报,2007,41(12):1982-1986.
[28]吴月华,孟庆浩,姚凤娟等.基于粒子滤波的移动机器人同时定位和环境模型的建立[J].制造业自动化,2007,29(2):57-59.
[29]刘松国,朱世强,刘瑜等.移动机器人的蒙特卡罗自主定位算法研究[J].机电工程,2005,22(4):38-42.
[30]赵广涛,程荫杭.基于超声波传感器的测距系统设计[J].传感器与仪器仪表,2006,22(11):129-130.
[31]饶运涛,邹继军,郑勇芸.现场总线CAN原理与应用技术[M].北京:北京航空航天大学出版社.2003:18.
[32]周立功单片机公司.CAN-bus现场总线基础方案——通讯篇[EB/O L].[2008-05-05].http://www.zlgmcu.com/philips/can/fangan/CAN-bus-tongxun.pdf.
[33]陈全福.智能移动机器人平台控制系统设计[学位论文].黑龙江:哈尔滨工程大学,2006:18-26.
[34]厉茂海,洪炳熔.移动机器人的概率定何方法研究进展[J].机器人,2005,27(4):380-384
[35]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,2007,37(1):40-44
[36]Kwok C,Fox D,Meila M.Real Time Particle Filters[J].Advances in Neural Information Processing Systems,2003,15:1081-1090
[37]Thrun S,BurgardW,Fox D.Probabilistic Robotics[M].New York:MIT,2003.
[38]Eliazar A,Ronald P.Learning Probabilistic Motion Models for Mobile Robots[C].Proceedings of the 21st International Conference on Machine Learning,2004,69(7):32-40
[39]Schroter Ch,Bohme H.J,Gross H.M.Robust Map Building for an Autonomous Robot Using Low-cost Sesors[C].Proceedings of the 2004 IEEE International Conference on System,Man&Cybernetics(SMC2004),2004:5398-5403
[40]张卫明,张炎华,钟由.蒙特卡罗粒子滤波算法应用研究[J].微计算机信息,2007,23(11):295-297
[41]Sanjeev Arulampalam M,Maskell S,Gordon N.A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking[J].IEEE Transactions on Signal Processing,2002,50(2):174-188
[42]云晓.网络移动机器人粒子滤波定位研究[D].天津:天津大学,2006:43-45
[43]郑宏.移动机器人导航和SLAM系统研究[D].上海:上海交通大学,2007:24-28
[44]Elfes A.Sonar-Based Real-world Mapping and Navigation[J].IEEE Journal of Robotics and Automation,1987,3(6):249-265
[45]Grisetti G,Stachniss C,Burgard W.Improved Techniques for Grid Mapping with Rao-Blackwellized Particle Filters[J].IEEE Transactions on Robotics and Automation,2007,23(2):34-46
[46]Grisetti G,Stachniss C,Burgard W.Information Gain-based Exploration Using Rao-Blackwellized Particle Filters[C].Proceedings of the 2005 International Conference on Robotics:Science and Systems(RSS 2005),2005
[47]高丽华,房芳,马旭东.一种基于声纳信息的移动机器人地图创建方法[J].制造业自动化,2006,28(11):33-35
[48]Moravec H,Elfes A.High Resolution Maps for Wide Angle Sonar[C].Proceedings of the 1985IEEE International Conference on Robotics and Automation,1985,2(3):116-121
[49]Matthies L,Elfes A.Integration of Sonar and Stereo Range Data Using a Grid-based Representation[C].Proceedings of the 1988 IEEE International Conference on Robotics and Automation,1988,2(4):727-733
[50]彭月祥,张哓明,王风全.多传感器信息融合技术的应用及改进[J].上海轻工业高等专科学校学报,1998,19(4):1-4
[51]刘钦.基于多传感器信息融合的自主车导航研究[D].青岛:青岛大学,2005:50-52
[2]徐国华,谭民.移动机器人的发展现状及其趋势[J].机器人技术与应用,2001(3):7-13.
[3]Leonard J,Durrant-Whyte H F.Mobile Robot Localization by Tracking Geometric Beacons[J].IEEE Transactions on Robotics and Automation,1991,7(3):376-382.
[4]Smith R,Self M,Chessman P.A Stochastic Map for Uncertain Spatial Relationships [C].Proceedings of the 4th International Symposium on Robotic Research.Cambridge MA,MIT Press,1987:467-474.
[5]谢昭贤,黄大贵,明爱国等.基于同时发射声纳环移动机器人导航系统[J].电子科技大学学报,2007,36(2):209-311.
[6]Elfes A.Sonar-based Real world Mapping and Navigation[J].IEEE Journal of Robotics and Automation,1987,3(3):249-265.
[7]Elfes A.Occupancy Grids:A probabilistic Framework for Robot Perception and Navigation [D].Pittsburgh:Department of Electrical and Computer Engineering,Carnegie Mellon University,1989.
[8]Avots D,Lim E,Thibaux R,Thrun S.A Probabilistic Technique for Simultaneous Localization and Door State Estimation with Mobile Robots in Dynamic Environments[J].Proceeding of Conference on Intelligent Robots and Systems,2002.
[9]Aycard O,Charpillet F,Fohr D,Mari J.Place Learning and Recognition Using Hidden Markov Models[C].Proceedings of the IEEE International Conference on Intelligent Robots and Systems,1997:1741-1746.
[10]Simmons R,Koenig S.Probabilistic Robot Navigation in Partially Observable Environments [C[.International Joint Conference on Artificial Intelligence,1995:1080-1087.
[11]Chong K,Kleeman L.Mobile Robot Map Building from an Advanced SonarArray and Accurate Odometry[J].International Journal of Robotics Research,1999,18(1):20-36.
[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[J].自动化学报,2003,29(2):267-274.
[13]李新德,黄心汉,王敏.基于经典DSmT的Sonar栅格地图创建[J].计算机应用研究,2007,24(3):209-211.
[14]Kortenkamp D,Weynouth T.Topological Mapping for Mobile Robots Using a Combination of Sonar and Vision Sensing[C].Proceeding of the 12th National Conference on Artificial Intelligence,Menlo Park:AAAI Press,1994:979-984.
[15]Borenstein J,Everett H.R,Feng L.Mobile Robot Positioning:Sensors and Techniques[J].Journal of Robotic Systems,1997,14(4):231-249.
[16]Guivant J,Nebot E.Optimization of Simultaneous Localization and Map Building Algorithm for Real Time Implementation[J].IEEE Transactions on Robotics and Automation,2001,17(3):242-257.
[17]胡士强,敬忠良.粒子滤波算法综述[J].控制与决策,2005,20(4):361-365.
[18]张共愿,赵忠.粒子滤波及其在导航系统中的应用综述[J].中国惯性技术学报,2006,14(6):91-94.
[19]于金霞,蔡自兴,段琢华.基于粒子滤波的移动机器人定位关键技术研究综述[J].计算机应用研究,2007,24(11):9-14.
[20]余洪山,王耀南.基于粒子滤波器的移动机器人定位和地图创建研究进展[J].机器人,2007,29(3):281-289.
[21]Hahnel D,Burgard W,Fox D.An Efficient FastSLAM Algorithm for Generating Maps of Large-scale Cyclic Environments Form Row Laser Range Measurements[C].Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS 2003),2003:206-211.
[22]Eliazar A,Parr R.DP-SLAM:Fast,Robust Simultaneous Localization and Mapping without Predetermined Landmarks[C].Proceedings of the 18th International Joint Conference on Artificial Intelligence(IJCAI-03),2003(18):1135-1142.
[23]Fox D.Adapting the Sample Size in Particle Filters through KLD-sampling[J].International Journal of Robotic Research,2003,22(1):985-1004.
[24]Grisetti G,Stachniss C,Burgard W.Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling[C].Proceedings of the 2005 IEEE International Conference on Robotics and Automation(ICRA 2005),2005:2432-2437.
[25]Schroter Ch,Bohme H J,Gross H.M.Memory-efficient Gridmaps in Rao-Blackwellized Particle Filters for SLAM Using Sonar Sensors[C].Proceedings of the 3rd European Conference on Mobile Robots(ECMR 2007).2007:138-143.
[26]历茂海,洪炳熔,罗荣华.用改进的Rao-Blackwellized粒子滤波器实现移动机器人同时定位和地图创建[J].吉林大学学报,2007,37(2):402-406.
[27]武二永,项志宇,沈敏一等.大规模环境下基于激光雷达的机器人SLAM算法[J].浙江大学学报,2007,41(12):1982-1986.
[28]吴月华,孟庆浩,姚凤娟等.基于粒子滤波的移动机器人同时定位和环境模型的建立[J].制造业自动化,2007,29(2):57-59.
[29]刘松国,朱世强,刘瑜等.移动机器人的蒙特卡罗自主定位算法研究[J].机电工程,2005,22(4):38-42.
[30]赵广涛,程荫杭.基于超声波传感器的测距系统设计[J].传感器与仪器仪表,2006,22(11):129-130.
[31]饶运涛,邹继军,郑勇芸.现场总线CAN原理与应用技术[M].北京:北京航空航天大学出版社.2003:18.
[32]周立功单片机公司.CAN-bus现场总线基础方案——通讯篇[EB/O L].[2008-05-05].http://www.zlgmcu.com/philips/can/fangan/CAN-bus-tongxun.pdf.
[33]陈全福.智能移动机器人平台控制系统设计[学位论文].黑龙江:哈尔滨工程大学,2006:18-26.
[34]厉茂海,洪炳熔.移动机器人的概率定何方法研究进展[J].机器人,2005,27(4):380-384
[35]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,2007,37(1):40-44
[36]Kwok C,Fox D,Meila M.Real Time Particle Filters[J].Advances in Neural Information Processing Systems,2003,15:1081-1090
[37]Thrun S,BurgardW,Fox D.Probabilistic Robotics[M].New York:MIT,2003.
[38]Eliazar A,Ronald P.Learning Probabilistic Motion Models for Mobile Robots[C].Proceedings of the 21st International Conference on Machine Learning,2004,69(7):32-40
[39]Schroter Ch,Bohme H.J,Gross H.M.Robust Map Building for an Autonomous Robot Using Low-cost Sesors[C].Proceedings of the 2004 IEEE International Conference on System,Man&Cybernetics(SMC2004),2004:5398-5403
[40]张卫明,张炎华,钟由.蒙特卡罗粒子滤波算法应用研究[J].微计算机信息,2007,23(11):295-297
[41]Sanjeev Arulampalam M,Maskell S,Gordon N.A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking[J].IEEE Transactions on Signal Processing,2002,50(2):174-188
[42]云晓.网络移动机器人粒子滤波定位研究[D].天津:天津大学,2006:43-45
[43]郑宏.移动机器人导航和SLAM系统研究[D].上海:上海交通大学,2007:24-28
[44]Elfes A.Sonar-Based Real-world Mapping and Navigation[J].IEEE Journal of Robotics and Automation,1987,3(6):249-265
[45]Grisetti G,Stachniss C,Burgard W.Improved Techniques for Grid Mapping with Rao-Blackwellized Particle Filters[J].IEEE Transactions on Robotics and Automation,2007,23(2):34-46
[46]Grisetti G,Stachniss C,Burgard W.Information Gain-based Exploration Using Rao-Blackwellized Particle Filters[C].Proceedings of the 2005 International Conference on Robotics:Science and Systems(RSS 2005),2005
[47]高丽华,房芳,马旭东.一种基于声纳信息的移动机器人地图创建方法[J].制造业自动化,2006,28(11):33-35
[48]Moravec H,Elfes A.High Resolution Maps for Wide Angle Sonar[C].Proceedings of the 1985IEEE International Conference on Robotics and Automation,1985,2(3):116-121
[49]Matthies L,Elfes A.Integration of Sonar and Stereo Range Data Using a Grid-based Representation[C].Proceedings of the 1988 IEEE International Conference on Robotics and Automation,1988,2(4):727-733
[50]彭月祥,张哓明,王风全.多传感器信息融合技术的应用及改进[J].上海轻工业高等专科学校学报,1998,19(4):1-4
[51]刘钦.基于多传感器信息融合的自主车导航研究[D].青岛:青岛大学,2005:50-52