高精度姿态测量平台的设计与实现
摘要
无人机是一个极具挑战性,应用前景异常广阔的领域,内容涉及姿态测量,导航控制,机器人,嵌入式系统,无线传输等方面。而姿态测量作为导航控制的输入,如何做到高精度,小体积,低功耗更是无人机研究的一个重中之重。无人机姿态测量以及导航系统发展至今,已经具有了较为完整的系统体系,已发展出纯惯性导航系统(INS)、GPS辅助惯性导航系统(GPS/INS)、视觉辅助惯性导航系统(Vision/INS)等多种方案。惯导系统由于其独有的特点使其在姿态测量中有着先天的优势,加上近几年兴起的MEMS传感器件更是非常适用于无人机的姿态测量。本文就是介绍如何以各种MEMS传感器作为测量单元,配合数据融合算法,得到一套精度较高的姿态测量系统。
本文首先给出了姿态测量的基础理论,包括常用坐标系以及姿态的常用表示;接着详细介绍了对处理器芯片以及传感器的选型,并进行具体的硬件设计,得到一个实用化的姿态测量硬件平台,这是本文的重点之一;最后介绍数据融合算法KALMAN滤波以及无人机的非线性运动学模型,这部分是全文的理论中心。通过将无人机的非线性模型根据扩展KALMAN滤波的要求进行线性化,得到KALMAN滤波的迭代方程,最后编程实现,结合姿态测量硬件平台实现传感器的原始数据融合,得到最终的姿态输出。文中给出了详细的实验过程调节KALMAN滤波参数,并最后得到了一套完整的姿态测量平台。文中还在最后指出了本设计的不足以及今后的改进方向,具有实践性的指导意义。
Unmanned Aerial Vehicles (UAV) are the field that full of challenge and have a vast use. It contains attitude measurement, navigation, robot, embedded system and wireless transmission. As the input of the navigation control system, attitude measurement to be high performance, small size and low power is the most important thing. At present, the navigation systems have been developed into a complete system, including Inertial Navigation System (INS), GPS aided INS (GPS/INS) and Vision aided INS (Vision/INS) as well. Because of the unique features, INS has the natural advantage in attitude measurement of UAV, especially the MEMS sensors rising recently. This paper is to introduce how to make a high performance attitude measurement system based on the MEMS sensors as the measure unit and coordinating with the data fusion algorithm.
Firstly, this paper has given the base theory of attitude measurement, including common coordinate system and common attitude expression, and then introduce the selection of processor and sensors in detail; hardware design and how to make them up to be a practical attitude measurement platform. That is a key point of this paper. Then, it will introduce the data fusion algorithm KALMAN filter and the nonlinear model of UAV which is the key theory of this paper. According to the EKF, to linearize the nonlinear model, we will obtain the iterative equations and make them into program. Through the attitude measurement platform, we fuse the data from sensors and will get the final attitude. At last, we do a lot of experiment to adjust the parameter of KALMAN filter and complete the system. This paper also indicates the limitation of this system and the improvement which has a practical guide.
本文首先给出了姿态测量的基础理论,包括常用坐标系以及姿态的常用表示;接着详细介绍了对处理器芯片以及传感器的选型,并进行具体的硬件设计,得到一个实用化的姿态测量硬件平台,这是本文的重点之一;最后介绍数据融合算法KALMAN滤波以及无人机的非线性运动学模型,这部分是全文的理论中心。通过将无人机的非线性模型根据扩展KALMAN滤波的要求进行线性化,得到KALMAN滤波的迭代方程,最后编程实现,结合姿态测量硬件平台实现传感器的原始数据融合,得到最终的姿态输出。文中给出了详细的实验过程调节KALMAN滤波参数,并最后得到了一套完整的姿态测量平台。文中还在最后指出了本设计的不足以及今后的改进方向,具有实践性的指导意义。
Unmanned Aerial Vehicles (UAV) are the field that full of challenge and have a vast use. It contains attitude measurement, navigation, robot, embedded system and wireless transmission. As the input of the navigation control system, attitude measurement to be high performance, small size and low power is the most important thing. At present, the navigation systems have been developed into a complete system, including Inertial Navigation System (INS), GPS aided INS (GPS/INS) and Vision aided INS (Vision/INS) as well. Because of the unique features, INS has the natural advantage in attitude measurement of UAV, especially the MEMS sensors rising recently. This paper is to introduce how to make a high performance attitude measurement system based on the MEMS sensors as the measure unit and coordinating with the data fusion algorithm.
Firstly, this paper has given the base theory of attitude measurement, including common coordinate system and common attitude expression, and then introduce the selection of processor and sensors in detail; hardware design and how to make them up to be a practical attitude measurement platform. That is a key point of this paper. Then, it will introduce the data fusion algorithm KALMAN filter and the nonlinear model of UAV which is the key theory of this paper. According to the EKF, to linearize the nonlinear model, we will obtain the iterative equations and make them into program. Through the attitude measurement platform, we fuse the data from sensors and will get the final attitude. At last, we do a lot of experiment to adjust the parameter of KALMAN filter and complete the system. This paper also indicates the limitation of this system and the improvement which has a practical guide.
引文
[1]徐爱钧. IAR EWARM嵌入式系统编程与实践[M].北京航空航天大学出版社, 2006
[2]付梦印,邓志红,张继伟. Kalman滤波理论及其在导航系统中的应用[M].北京:科学出版社,2003
[3]罗沛.空中机器人视觉导航系统的研究与设计[D].华南理工大学自动化科学与工程学院, 2007.5
[4]常锐.小型自主无人直升飞机软件系统设计与实现[D].华南理工大学自动化科学与工程学院, 2007.5
[5]蔡伟峰.无人直升机视觉导航系统的分析与设计[D].华南理工大学自动化科学与工程学院, 2009.5
[6]刘栋炼.无人机飞控系统硬件设计与数据融合算法研究[D].华南理工大学自动化科学与工程学院, 2009.5
[7] Analog Devices, Inc. ADIS16350 Datasheet [M/OL]. http://www.analog.com, 2008-02
[8]Atmel Corporation. AT91SAM7SE Series Preliminary [M/OL]. http://www.atmel.com, 2008-01
[8]陈远炫,裴海龙,陈勇.基于微惯性测量单元的导航系统研究[J].自动化与仪表,2009,24[10]:1-5
[9] Jose. A. Rios, Elecia White. Fusion Filter Algorithm Enhancements For a MEMS GPS/IMU [J]. ION NTM2002, 28-30 January 2002, San Diego, CA.
[10]单茂华,周百令,李宏生,黄丽斌.MIMU定位定向系统数据采集装置的设计[J].数据采集与处理,2002, 17(4): 390-394
[11]蔡鹤皋,金明河,金峰.卡尔曼滤波与多传感器信息融合技术[J].模式识别与人工智能. 2000, 13(3): 248-253
[12]吴宝琳,黄晓瑞,崔平远.信息融合技术及其在组合导航系统中的应用[J].战术导弹技术. 2002, (1): 39-44
[13]杜亚玲,刘建业,熊智,姜涌. GPS/SINS全组合导航系统的姿态组合算法[J].中国空间科学技术. 2006, (1): 53-57
[14]张红梅.非线性滤波及其在姿态确定和导航中的应用[D]: [博士学位论文].哈尔滨工业大学, 2004
[15] David H. Titterton, John L. Weston.捷联惯性导航技术[M].第二版.张天光等.北京:国防工业出版社, 2007:24~34
[16]秦永元.惯性导航[M].北京:科学出版社,2006
[17] Greg Welch, Gary Bishop. An Introduction to the Kalman Filter [M/OL]. University of North Carolina, 2006-6-24
[18]吴俊伟.惯性技术基础[M].哈尔滨:哈尔滨工程大学出版社, 2002
[19] Analog Devices, Inc. ADIS16209 Datasheet [M/OL]. http://www.analog.com, 2009-08
[20] Analog Devices, Inc. ADIS16130 Datasheet [M/OL]. http://www.analog.com, 2008-04
[21]陈宇捷.基于MEMS的微小型嵌入式航姿参考系统研究[D].上海交通大学电子信息与电气工程学院. 2009.1
[22]刘付强.基于MEMS器件的捷联姿态测量系统技术研究[D].哈尔滨工程大学自动化学院. 2007.1
[23] BEAR: Berkeley Aerobot Team[EB/OL]. http://robotics.eecs.berkeley.edu/bear/index.html
[24] Bradford S. Davis. "Using Low-Cost MEMS Accelerometers and Gyroscopes as Strapdown IMUs on Rolling Projectiles"[J]. U.S. Army Research Laboratory, Weapons and Materials Dir., AMSRL-WM-BA, Aberdeen Proving Ground, MD 21005-5066
[25] Zhou Tao, Wang Lei. "SINS and GPS Integrated Navigation System of Small Unmanned Aerial Vehicle"[J]. International Seminar on Future BioMedical Information Engineering, 2008.25, 465-468
[26] Roberts J., Corke P., and Buskey G.“Low-Cost Flight Control System for a Small Autonomous Helicopter”[J]. Australasian Conference on Robotics and Automation, Auckland, New Zealand, 2002
[27] Sven R?nnb?ck. Development of a INS/GPS Navigation Loop for an UAV[D]. Master’s Thesis. Lule? University of Technology,2000
[28]丁化成,耿德根,李君凯. AVR单片机应用设计[M].第一版.北京航空航天大学出版社, 2002
[29]王惠南. GPS导航原理与应用[M].科学出版社, 2003
[30] Jeong Won Kim, Dong-Hwan Hwang and Sang Jeong Lee. A Deeply Coupled GPS/INSIntegrated Kalman Filter Design Using a Linearized Correlator Output [J]. IEEE. 2006: 300~305
[31] R. P. Simgh and W. H. Baily. Fuzzy Logic Application to Multisensor-Multitarget Correlation [J]. IEEE Trans. Aerospace and Electronic Systems, 1997, 33(3): 752-769
[32] M. L. Krieg and D. A. Gray. Multi-Sensor, Probabilistic Multi-Hypothesis Tracking Using Dissimilar Sensor [J]. SPIE, 1997, 3086: 129-138
[33]张崇猛,陈超英,庄良杰,刘飞.信息融合理论及其在INS/GPS/Doppler组合导航系统中的应用[J].中国惯性技术学报. 1999, 7(3): 1-8
[34]马立诚,多传感器导航系统中的联合Kalman滤波器[J].导航. 1995, 4: 100-107
[35]徐绍铨,张华海,杨志强,等. GPS测量原理及应用.修订版.武汉大学出版社, 2003
[36] Schmidt G. T. , and Phillips R. E. INS/GPS integration, Proceedings of AGARD MSP Lecture series LS207, Systems implications and innovative application of satellite navigation, Paper 9, 1997
[37]张谦.基于GPS的综合惯性导航系统[D].华南理工大学自动化科学与工程学院,2004.5
[2]付梦印,邓志红,张继伟. Kalman滤波理论及其在导航系统中的应用[M].北京:科学出版社,2003
[3]罗沛.空中机器人视觉导航系统的研究与设计[D].华南理工大学自动化科学与工程学院, 2007.5
[4]常锐.小型自主无人直升飞机软件系统设计与实现[D].华南理工大学自动化科学与工程学院, 2007.5
[5]蔡伟峰.无人直升机视觉导航系统的分析与设计[D].华南理工大学自动化科学与工程学院, 2009.5
[6]刘栋炼.无人机飞控系统硬件设计与数据融合算法研究[D].华南理工大学自动化科学与工程学院, 2009.5
[7] Analog Devices, Inc. ADIS16350 Datasheet [M/OL]. http://www.analog.com, 2008-02
[8]Atmel Corporation. AT91SAM7SE Series Preliminary [M/OL]. http://www.atmel.com, 2008-01
[8]陈远炫,裴海龙,陈勇.基于微惯性测量单元的导航系统研究[J].自动化与仪表,2009,24[10]:1-5
[9] Jose. A. Rios, Elecia White. Fusion Filter Algorithm Enhancements For a MEMS GPS/IMU [J]. ION NTM2002, 28-30 January 2002, San Diego, CA.
[10]单茂华,周百令,李宏生,黄丽斌.MIMU定位定向系统数据采集装置的设计[J].数据采集与处理,2002, 17(4): 390-394
[11]蔡鹤皋,金明河,金峰.卡尔曼滤波与多传感器信息融合技术[J].模式识别与人工智能. 2000, 13(3): 248-253
[12]吴宝琳,黄晓瑞,崔平远.信息融合技术及其在组合导航系统中的应用[J].战术导弹技术. 2002, (1): 39-44
[13]杜亚玲,刘建业,熊智,姜涌. GPS/SINS全组合导航系统的姿态组合算法[J].中国空间科学技术. 2006, (1): 53-57
[14]张红梅.非线性滤波及其在姿态确定和导航中的应用[D]: [博士学位论文].哈尔滨工业大学, 2004
[15] David H. Titterton, John L. Weston.捷联惯性导航技术[M].第二版.张天光等.北京:国防工业出版社, 2007:24~34
[16]秦永元.惯性导航[M].北京:科学出版社,2006
[17] Greg Welch, Gary Bishop. An Introduction to the Kalman Filter [M/OL]. University of North Carolina, 2006-6-24
[18]吴俊伟.惯性技术基础[M].哈尔滨:哈尔滨工程大学出版社, 2002
[19] Analog Devices, Inc. ADIS16209 Datasheet [M/OL]. http://www.analog.com, 2009-08
[20] Analog Devices, Inc. ADIS16130 Datasheet [M/OL]. http://www.analog.com, 2008-04
[21]陈宇捷.基于MEMS的微小型嵌入式航姿参考系统研究[D].上海交通大学电子信息与电气工程学院. 2009.1
[22]刘付强.基于MEMS器件的捷联姿态测量系统技术研究[D].哈尔滨工程大学自动化学院. 2007.1
[23] BEAR: Berkeley Aerobot Team[EB/OL]. http://robotics.eecs.berkeley.edu/bear/index.html
[24] Bradford S. Davis. "Using Low-Cost MEMS Accelerometers and Gyroscopes as Strapdown IMUs on Rolling Projectiles"[J]. U.S. Army Research Laboratory, Weapons and Materials Dir., AMSRL-WM-BA, Aberdeen Proving Ground, MD 21005-5066
[25] Zhou Tao, Wang Lei. "SINS and GPS Integrated Navigation System of Small Unmanned Aerial Vehicle"[J]. International Seminar on Future BioMedical Information Engineering, 2008.25, 465-468
[26] Roberts J., Corke P., and Buskey G.“Low-Cost Flight Control System for a Small Autonomous Helicopter”[J]. Australasian Conference on Robotics and Automation, Auckland, New Zealand, 2002
[27] Sven R?nnb?ck. Development of a INS/GPS Navigation Loop for an UAV[D]. Master’s Thesis. Lule? University of Technology,2000
[28]丁化成,耿德根,李君凯. AVR单片机应用设计[M].第一版.北京航空航天大学出版社, 2002
[29]王惠南. GPS导航原理与应用[M].科学出版社, 2003
[30] Jeong Won Kim, Dong-Hwan Hwang and Sang Jeong Lee. A Deeply Coupled GPS/INSIntegrated Kalman Filter Design Using a Linearized Correlator Output [J]. IEEE. 2006: 300~305
[31] R. P. Simgh and W. H. Baily. Fuzzy Logic Application to Multisensor-Multitarget Correlation [J]. IEEE Trans. Aerospace and Electronic Systems, 1997, 33(3): 752-769
[32] M. L. Krieg and D. A. Gray. Multi-Sensor, Probabilistic Multi-Hypothesis Tracking Using Dissimilar Sensor [J]. SPIE, 1997, 3086: 129-138
[33]张崇猛,陈超英,庄良杰,刘飞.信息融合理论及其在INS/GPS/Doppler组合导航系统中的应用[J].中国惯性技术学报. 1999, 7(3): 1-8
[34]马立诚,多传感器导航系统中的联合Kalman滤波器[J].导航. 1995, 4: 100-107
[35]徐绍铨,张华海,杨志强,等. GPS测量原理及应用.修订版.武汉大学出版社, 2003
[36] Schmidt G. T. , and Phillips R. E. INS/GPS integration, Proceedings of AGARD MSP Lecture series LS207, Systems implications and innovative application of satellite navigation, Paper 9, 1997
[37]张谦.基于GPS的综合惯性导航系统[D].华南理工大学自动化科学与工程学院,2004.5