智能车动力学模型参数辨识方法研究
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摘要
在行车安全和交通效率问题日益凸显的今天,智能车技术正在快速发展。为了达到更好的车辆控制效果,期望使用基于动力学的车体控制方法。但是在车辆动力学模型中,某些参数的确定非常困难或者测量成本昂贵。为此,本文进行了针对智能车的动力学模型参数辨识研究。
为了便于智能车动力学模型参数辨识的研究,本文首先搭建了以智能小车实验平台,该智能小车具备良好的转向特性和速度特性,而且在结合了嵌入式微控制器、摄像头和测速编码器后能够实现车辆的自主导航。
然后提出了基于激光雷达的智能小车定位方法,该方法把切线角度直方图和XY直方图的优势进行巧妙结合以进行车辆位姿预估,再运用基于模板匹配和鲁棒迭代最近点算法(Iterative Closest Point, ICP)的定位方法对智能小车进行定位,最后使用扩展卡尔曼滤波(Extended Kalman Filter, EKF)进行位姿跟踪。仿真实验和实际实验证明,该定位方法具备较好的定位鲁棒性和精度。
此后,基于以上定位结果,进行了智能小车动力学参数辨识的初步研究,在二自由度动力学模型的基础上推导出参数辨识的方法和步骤,最后基于辨识参数的仿真轨迹与实际的定位轨迹对比,验证了该辨识方法的可行性。
As the prominence of traffic safety and efficiency, intelligent vehicle technology develops rapidly. To get the better driving, it’s reasonable to control based on vehicle dynamics. Unfortunately, some parameters in dynamic model are difficult to determine or costly to measure. Research on identification of dynamic parameters for intelligent vehicle is stated in this paper.
For the convenience of research work, an experiment platform based on smart vehicle is developed. The smart vehicle performs well in steering and speed property. After being integrated with embedded micro-controllers, camera and encoder, it have the ability of autonomous navigation.
Then a localization method of the smart vehicle based on laser radar is given. This method combines the advantages of tangent angle histogram and XY histogram to pre-estimate the pose. Later, model registration and robust Iterative Closest Point (ICP) algorithm is designed to locate the smart vehicle. Finally, for compensating laser radar deficiencies, Extended Kalman Filter (EKF) algorithm is used for Laser Radar and Dead Reckoning data fusion so as to guarantee the accuracy of localization. Simulation and real experiments illustrate that the localization method has high robustness and accuracy.
Based on the location result, preliminary and basic research work is done about identification of the smart vehicle. The parameter identification method is derived from the 2-DOF dynamic model. Contrast of simulation trace based on identified parameters and real trace based on localization indicates the feasibility of the method.
为了便于智能车动力学模型参数辨识的研究,本文首先搭建了以智能小车实验平台,该智能小车具备良好的转向特性和速度特性,而且在结合了嵌入式微控制器、摄像头和测速编码器后能够实现车辆的自主导航。
然后提出了基于激光雷达的智能小车定位方法,该方法把切线角度直方图和XY直方图的优势进行巧妙结合以进行车辆位姿预估,再运用基于模板匹配和鲁棒迭代最近点算法(Iterative Closest Point, ICP)的定位方法对智能小车进行定位,最后使用扩展卡尔曼滤波(Extended Kalman Filter, EKF)进行位姿跟踪。仿真实验和实际实验证明,该定位方法具备较好的定位鲁棒性和精度。
此后,基于以上定位结果,进行了智能小车动力学参数辨识的初步研究,在二自由度动力学模型的基础上推导出参数辨识的方法和步骤,最后基于辨识参数的仿真轨迹与实际的定位轨迹对比,验证了该辨识方法的可行性。
As the prominence of traffic safety and efficiency, intelligent vehicle technology develops rapidly. To get the better driving, it’s reasonable to control based on vehicle dynamics. Unfortunately, some parameters in dynamic model are difficult to determine or costly to measure. Research on identification of dynamic parameters for intelligent vehicle is stated in this paper.
For the convenience of research work, an experiment platform based on smart vehicle is developed. The smart vehicle performs well in steering and speed property. After being integrated with embedded micro-controllers, camera and encoder, it have the ability of autonomous navigation.
Then a localization method of the smart vehicle based on laser radar is given. This method combines the advantages of tangent angle histogram and XY histogram to pre-estimate the pose. Later, model registration and robust Iterative Closest Point (ICP) algorithm is designed to locate the smart vehicle. Finally, for compensating laser radar deficiencies, Extended Kalman Filter (EKF) algorithm is used for Laser Radar and Dead Reckoning data fusion so as to guarantee the accuracy of localization. Simulation and real experiments illustrate that the localization method has high robustness and accuracy.
Based on the location result, preliminary and basic research work is done about identification of the smart vehicle. The parameter identification method is derived from the 2-DOF dynamic model. Contrast of simulation trace based on identified parameters and real trace based on localization indicates the feasibility of the method.
引文
[1] Groen F, Spaan M, Vlassis N. Robot soccer: game or science. http://www.robocup.org.
[2] FIRA. FIRA Robot Sot game rules. http://www.fira.net/soccer/robosot/Robosot.pdf.
[3]王卫华.移动机器人定位技术研究[博士论文].武汉:华中科技大学. 2005.
[4]李磊,叶涛,谭民等.移动机器人技术研究现状与未来.机器人. 2002年9月.第24卷第5期: 475-480.
[5]喻凡,林逸.汽车系统动力学.机械工业出版社, 2005.
[6] Milliken W.F and Milliken D.L., Race car vehicle dynamics, 1995. SAE Press, Warren dale.
[7]方崇智,萧德云.过程辨识.北京:清华大学出版社. 1988.
[8]刘豹,王正欧.系统辨识.机械工业出版社. 1993年.
[9]曾长操,刘奋,张建武.智能转向汽车的模型跟踪控制.上海交通大学学报. 2003年11月.第37卷第11期.
[10]项志宇.基于激光雷达的移动机器人障碍检测和自定位[博士论文].杭州:浙江大学. 2002.
[11] DARPA Grand Challenge: http://www.darpa.mil/grandchallenge.
[12] Yang Ming, Wang Chen, Yang Ruqing, Parent Michel. Cybercars automated vehicles in china, in Proceedings of transportation research board annual meeting 2006. Jannary 22-26, 2006, Washington DC, US.
[13] Yang Ming, Parent Michel, Cybernetic technologies for cars in Chinese cities, in Proceedings of CityTrans China 2004,Vol.17-18,2004, Shanghai, China.
[14] Parent Michel, New technologies for sustainable urban transportation in Europe, in Proceedings of 4th Asia Pacific conference on transportation and the environment, November 8-10, 2005, Xi’an, China.
[15] TORCS官方网站. http://torcs.sourceforge.net.
[16] MSC Software Corporation官方网站. http://www.mscsoftware.com/
[17]李军,刑俊文,覃文洁. ADAMS实例教程[M].北京:北京理工大学出版社, 2002.
[18]蒋荻南,黄开胜.基于虚拟仪器技术的智能车仿真系统[J].电子产品世界.2006(02): 132-134.
[19]全国大学生智能汽车竞赛官方网站. http://www.smartcar.au.tsinghua.edu.cn/4dCar/web /index.asp.
[20]李铁军. ROBOCUP中型组机器人视觉系统的研究与开发[硕士论文].山东:山东大学. 2006.
[21]李开生,张慧慧,费仁元等.定位传感器及其融合技术综述.计算机自动测量与控制. 2001年9月,第9卷第4期: 1-3.
[22]董再励,郝颖明,朱枫.一种基于视觉的移动机器人定位系统.中国图象图形学报. 2005, 5(8): 688-692
[23]董再励,王光辉.自主移动机器人激光全局定位系统研究.机器人. 2000, 22(3).
[24] B C Bloom, Use of lankmarks for mobile robot navigation. In Proc SPIE Conf. on Intelligent Robots and Computer Vision, 1985, 579.
[25]杨明,王宏,张钹.基于激光雷达的移动机器人位姿估计方法综述.机器人. 2002年3月.第24卷第2期.
[26] Stewart D.. A Platform with six Degrees of freedom. Proceedings of the Institution of Mechanical Engineers. Vol 180, No.5, pp.371-378. 1965.
[27] Hainsworth D.W..Teleoperation User Interfaces For Mining Robotics. IEEE International Conference on Robotics and Automation. San Francisco, California. April 2000.
[28] Ralston J.C., Hainsworth D.W.. The Numbat: A Remotely Controlled Mine Emergency Response Vehicle. Proceedings of the International Conference on Field and Service Robotics, pp.48-55, Canberra, Australia, December 1997.
[29] Capt Mark H.Draper, Ph.D. Health A.Ruff. Multi-sensory displays and visualization techniques supporting the control of unmanned air vehicles. IEEE International Conference on Robotics and Automation. San Francisco, California. April 2000.
[30]方兴,杨明.智能车硬件在环仿真系统的设计与实现.华中科技大学学报.第36卷,增刊I, 2008.
[31]陈东,向巍.基于光电管的智能车模自动循线控制系统[J].机械与电子.2007(10):32-36.
[32]周斌,李立国,黄开胜.智能车光电传感器布局对路径识别的影响研究[J].电子产品世界.2006(5):139-140.
[33]张祥合.基于路径识别的车辆智能控制策略与仿真研究[D].吉林:吉林大学软件学院,2007:10-14.
[34]卓晴,王琎,王磊.基于面阵CCD的赛道参数检测方法[J].电子产品世界.2006(04):141-143.
[35] Good, M.C.,“Sensitive of Drive-Vehicle Performance to Vehicle Characteristics Revealed in Open-Loop Tests”, Vehicle System Dynamics, Vol.6, No.4, 1977, pp.245-277.
[36] Durstine, J.W., The Truck Steering System from Hand Wheel to Road Wheel, SAE SP-374, 1973, pp.76.
[37] Ingemar J. Cox, Blanche-An Experiment in Guidance and Navigation of an Autonomous Robot Vehicle, IEEE Transactions on Robotics and Automation, Vol. 7, NO. 2, pp. 193-204, April, 1991.
[38] A. Curran, K. J. Kyriakopoulos, Sensor-Based Self-Localization for Wheeled Mobile Robots, Proceedings of International Conference on Robotics & Automation, pp. 8-13, 1993.
[39] Ulf Larsson, Johan Forsberg, Ake Wernersson, On Robot Navigation Using Identical Landmarks: Integrating Measurements from a Time-of-Flight Laser, Proceedings of International Conference on Multi-sensor Fusion for Intelligent Systems, pp. 17-26, 1994.
[40] John J. Leonard, Hugh F. Durrant Whyte, Mobile Robot Localization by Tracking Geometric Beacons, IEEE Transaction on Robotics & Automation, Vol. 7, pp. 376-382, 1991.
[41] R. Madhavan, G. Dissanayake, H. Durrant-Whyte, Map-Building and Map-Based Localization in an Underground-mine by Statistical Pattern Matching, Proceedings of International Conference on Pattern Recognition, pp. 1744-1746, 1998.
[42] Frank Dellaert, Dieter Fox, Wofram Burgard, etc, Monte Carlo Localization for Mobile Robots, Proceedings of 1999 IEEE International Conference on Robotics & Automation, Detroit, Michigan, pp. 1322-1328, May 1999.
[43]杨明,董斌,王宏等.基于激光雷达的移动机器人实时位姿估计方法研究.自动化学报, 2004年9月,第30卷第5期: 679-687.
[44]赵翊捷,陈卫东.基于地图的移动机器人定位技术新进展.上海交通大学学报. 2002年10月,第36卷第10期: 1435-1447.
[45]钱钧,杨汝清,王晨等.基于路标的智能车辆定位.上海交通大学学报. 2007年.第41卷第6期: 894-898.
[46] Shun-xi Wu, Ming Yang, Landmark Pair based Localization for Intelligent Vehicles using Laser Radar, Proceedings of 2007 IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, IEEE, 2007, pp. 209-214.
[47] Shun-xi Wu, Ming Yang, Jun Qian. ICP based Localization for Intelligent Vehicles using Laser Radar, Proceedings of 2007 International Conference on Intelligent Computing. Qingdao, China: IEEE, 2007.
[48] Atiya, S. and Hager, G.D.,”Real-time Vision-based Robot Localization”, IEEE Trans. On Robotics and Automation, Vol.9, pp.785-800,1993.
[49] Daniel P., Eduardo M.N. and Durrant-Whyte H.. An Evidential approach to map-building for Autonomous Vehicles. IEEE Trans. On Robotics and Automation, No.4, pp.623-629, 1998.
[50] Technical Description:LMS 200 / LMS 211 / LMS 220 / LMS 221 / LMS 291 Laser Measurement Systems. http://www.sick.com.
[51]伍舜喜.基于激光雷达的智能车定位技术研究[硕士论文].上海:上海交通大学. 2007.
[52]杨明,王宏,张钹.基于激光雷达的移动机器人位姿估计方法综述.机器人. 2002年3月.第24卷第2期.
[53] Feng Lu, Evangelos Milios, Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans, Journal of Intelligent and Robotic Systems, Vol. 18, pp. 249-275, 1997.
[54] Gerhard Weiss, Christopher Wetzler,Ewald von Puttkamer. Keeping Track of Position and Orientation of Moving Indoor Systems by Correlation of Range-Finder Scans.
[55] Paul J. Besl, Neil D. McKay, A Method for Registration of 3-D Shape, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2), pp. 239-256.
[56] Zheng-you, Zhang, Iterative Point Matching for Registration of Free-form Curves and Surfaces, INRIA Technical, RR-1658, INRIA, 1992.
[57] Welch G, Bishop G. An introduction to the Kalman filter. TR95-041. USA: ACM, 2001.
[58] Mcacvoy T J. Contemplative Stance for Chemical Process Control [J]. Automatica , 1992, 28(2): 441-442.
[59]李海青,黄志尧.软测量技术原理及其应用[M].北京:化学工业出版社, 2000.
[60]高振海,郑南宁,程洪.基于车辆动力学和Kalman滤波的汽车状态软测量.系统仿真学报. Vol.16 No.1, Jan. 2004.
[2] FIRA. FIRA Robot Sot game rules. http://www.fira.net/soccer/robosot/Robosot.pdf.
[3]王卫华.移动机器人定位技术研究[博士论文].武汉:华中科技大学. 2005.
[4]李磊,叶涛,谭民等.移动机器人技术研究现状与未来.机器人. 2002年9月.第24卷第5期: 475-480.
[5]喻凡,林逸.汽车系统动力学.机械工业出版社, 2005.
[6] Milliken W.F and Milliken D.L., Race car vehicle dynamics, 1995. SAE Press, Warren dale.
[7]方崇智,萧德云.过程辨识.北京:清华大学出版社. 1988.
[8]刘豹,王正欧.系统辨识.机械工业出版社. 1993年.
[9]曾长操,刘奋,张建武.智能转向汽车的模型跟踪控制.上海交通大学学报. 2003年11月.第37卷第11期.
[10]项志宇.基于激光雷达的移动机器人障碍检测和自定位[博士论文].杭州:浙江大学. 2002.
[11] DARPA Grand Challenge: http://www.darpa.mil/grandchallenge.
[12] Yang Ming, Wang Chen, Yang Ruqing, Parent Michel. Cybercars automated vehicles in china, in Proceedings of transportation research board annual meeting 2006. Jannary 22-26, 2006, Washington DC, US.
[13] Yang Ming, Parent Michel, Cybernetic technologies for cars in Chinese cities, in Proceedings of CityTrans China 2004,Vol.17-18,2004, Shanghai, China.
[14] Parent Michel, New technologies for sustainable urban transportation in Europe, in Proceedings of 4th Asia Pacific conference on transportation and the environment, November 8-10, 2005, Xi’an, China.
[15] TORCS官方网站. http://torcs.sourceforge.net.
[16] MSC Software Corporation官方网站. http://www.mscsoftware.com/
[17]李军,刑俊文,覃文洁. ADAMS实例教程[M].北京:北京理工大学出版社, 2002.
[18]蒋荻南,黄开胜.基于虚拟仪器技术的智能车仿真系统[J].电子产品世界.2006(02): 132-134.
[19]全国大学生智能汽车竞赛官方网站. http://www.smartcar.au.tsinghua.edu.cn/4dCar/web /index.asp.
[20]李铁军. ROBOCUP中型组机器人视觉系统的研究与开发[硕士论文].山东:山东大学. 2006.
[21]李开生,张慧慧,费仁元等.定位传感器及其融合技术综述.计算机自动测量与控制. 2001年9月,第9卷第4期: 1-3.
[22]董再励,郝颖明,朱枫.一种基于视觉的移动机器人定位系统.中国图象图形学报. 2005, 5(8): 688-692
[23]董再励,王光辉.自主移动机器人激光全局定位系统研究.机器人. 2000, 22(3).
[24] B C Bloom, Use of lankmarks for mobile robot navigation. In Proc SPIE Conf. on Intelligent Robots and Computer Vision, 1985, 579.
[25]杨明,王宏,张钹.基于激光雷达的移动机器人位姿估计方法综述.机器人. 2002年3月.第24卷第2期.
[26] Stewart D.. A Platform with six Degrees of freedom. Proceedings of the Institution of Mechanical Engineers. Vol 180, No.5, pp.371-378. 1965.
[27] Hainsworth D.W..Teleoperation User Interfaces For Mining Robotics. IEEE International Conference on Robotics and Automation. San Francisco, California. April 2000.
[28] Ralston J.C., Hainsworth D.W.. The Numbat: A Remotely Controlled Mine Emergency Response Vehicle. Proceedings of the International Conference on Field and Service Robotics, pp.48-55, Canberra, Australia, December 1997.
[29] Capt Mark H.Draper, Ph.D. Health A.Ruff. Multi-sensory displays and visualization techniques supporting the control of unmanned air vehicles. IEEE International Conference on Robotics and Automation. San Francisco, California. April 2000.
[30]方兴,杨明.智能车硬件在环仿真系统的设计与实现.华中科技大学学报.第36卷,增刊I, 2008.
[31]陈东,向巍.基于光电管的智能车模自动循线控制系统[J].机械与电子.2007(10):32-36.
[32]周斌,李立国,黄开胜.智能车光电传感器布局对路径识别的影响研究[J].电子产品世界.2006(5):139-140.
[33]张祥合.基于路径识别的车辆智能控制策略与仿真研究[D].吉林:吉林大学软件学院,2007:10-14.
[34]卓晴,王琎,王磊.基于面阵CCD的赛道参数检测方法[J].电子产品世界.2006(04):141-143.
[35] Good, M.C.,“Sensitive of Drive-Vehicle Performance to Vehicle Characteristics Revealed in Open-Loop Tests”, Vehicle System Dynamics, Vol.6, No.4, 1977, pp.245-277.
[36] Durstine, J.W., The Truck Steering System from Hand Wheel to Road Wheel, SAE SP-374, 1973, pp.76.
[37] Ingemar J. Cox, Blanche-An Experiment in Guidance and Navigation of an Autonomous Robot Vehicle, IEEE Transactions on Robotics and Automation, Vol. 7, NO. 2, pp. 193-204, April, 1991.
[38] A. Curran, K. J. Kyriakopoulos, Sensor-Based Self-Localization for Wheeled Mobile Robots, Proceedings of International Conference on Robotics & Automation, pp. 8-13, 1993.
[39] Ulf Larsson, Johan Forsberg, Ake Wernersson, On Robot Navigation Using Identical Landmarks: Integrating Measurements from a Time-of-Flight Laser, Proceedings of International Conference on Multi-sensor Fusion for Intelligent Systems, pp. 17-26, 1994.
[40] John J. Leonard, Hugh F. Durrant Whyte, Mobile Robot Localization by Tracking Geometric Beacons, IEEE Transaction on Robotics & Automation, Vol. 7, pp. 376-382, 1991.
[41] R. Madhavan, G. Dissanayake, H. Durrant-Whyte, Map-Building and Map-Based Localization in an Underground-mine by Statistical Pattern Matching, Proceedings of International Conference on Pattern Recognition, pp. 1744-1746, 1998.
[42] Frank Dellaert, Dieter Fox, Wofram Burgard, etc, Monte Carlo Localization for Mobile Robots, Proceedings of 1999 IEEE International Conference on Robotics & Automation, Detroit, Michigan, pp. 1322-1328, May 1999.
[43]杨明,董斌,王宏等.基于激光雷达的移动机器人实时位姿估计方法研究.自动化学报, 2004年9月,第30卷第5期: 679-687.
[44]赵翊捷,陈卫东.基于地图的移动机器人定位技术新进展.上海交通大学学报. 2002年10月,第36卷第10期: 1435-1447.
[45]钱钧,杨汝清,王晨等.基于路标的智能车辆定位.上海交通大学学报. 2007年.第41卷第6期: 894-898.
[46] Shun-xi Wu, Ming Yang, Landmark Pair based Localization for Intelligent Vehicles using Laser Radar, Proceedings of 2007 IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, IEEE, 2007, pp. 209-214.
[47] Shun-xi Wu, Ming Yang, Jun Qian. ICP based Localization for Intelligent Vehicles using Laser Radar, Proceedings of 2007 International Conference on Intelligent Computing. Qingdao, China: IEEE, 2007.
[48] Atiya, S. and Hager, G.D.,”Real-time Vision-based Robot Localization”, IEEE Trans. On Robotics and Automation, Vol.9, pp.785-800,1993.
[49] Daniel P., Eduardo M.N. and Durrant-Whyte H.. An Evidential approach to map-building for Autonomous Vehicles. IEEE Trans. On Robotics and Automation, No.4, pp.623-629, 1998.
[50] Technical Description:LMS 200 / LMS 211 / LMS 220 / LMS 221 / LMS 291 Laser Measurement Systems. http://www.sick.com.
[51]伍舜喜.基于激光雷达的智能车定位技术研究[硕士论文].上海:上海交通大学. 2007.
[52]杨明,王宏,张钹.基于激光雷达的移动机器人位姿估计方法综述.机器人. 2002年3月.第24卷第2期.
[53] Feng Lu, Evangelos Milios, Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans, Journal of Intelligent and Robotic Systems, Vol. 18, pp. 249-275, 1997.
[54] Gerhard Weiss, Christopher Wetzler,Ewald von Puttkamer. Keeping Track of Position and Orientation of Moving Indoor Systems by Correlation of Range-Finder Scans.
[55] Paul J. Besl, Neil D. McKay, A Method for Registration of 3-D Shape, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2), pp. 239-256.
[56] Zheng-you, Zhang, Iterative Point Matching for Registration of Free-form Curves and Surfaces, INRIA Technical, RR-1658, INRIA, 1992.
[57] Welch G, Bishop G. An introduction to the Kalman filter. TR95-041. USA: ACM, 2001.
[58] Mcacvoy T J. Contemplative Stance for Chemical Process Control [J]. Automatica , 1992, 28(2): 441-442.
[59]李海青,黄志尧.软测量技术原理及其应用[M].北京:化学工业出版社, 2000.
[60]高振海,郑南宁,程洪.基于车辆动力学和Kalman滤波的汽车状态软测量.系统仿真学报. Vol.16 No.1, Jan. 2004.