基于ARM的航姿参考系统多传感器信息融合技术研究
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摘要
无人机(Unmanned Air Vehicle, UAV)是一种带动力驱动的、无线电遥控或自主飞行的、执行多种任务的无人驾驶飞行器。要实现无人机的智能化自主飞行,其姿态及航向参考系统是非常关键的技术之一。飞行器执行各项任务(如航拍摄影作业)时的性能优劣在很大程度上取决于其飞行姿态的稳定性。由于运动惯性的存在和飞行环境的影响,飞行器在空中是个不稳定的平台,一个很微小的角度倾斜都可以造成运动轨迹的大幅度偏差。为了对小型无人飞行器的惯性器件参数进行测试和补偿,改进飞行器的自主飞行性能,本论文设计出一个基于ARM微控制器平台和多传感器信息融合技术的飞行器姿态参考系统,实时准确的获得飞行器的姿态角度信息,并能对飞行器姿态修正提供准确参考。
首先介绍了基于ARM的小型无人飞行器姿态参考系统的硬件实现,给出了系统整体设计方案和具体的硬件选型,描述了飞行器姿态信号采集单元功能的实现原理和方法、姿态控制单元舵机驱动信号--PWM控制波形的生成程序以及移植μC/OS-II内核系统来实现嵌入式的实时多任务操作系统。随后阐述了采集单元的传感器信号的处理过程,使用低通滤波器和线性卡尔曼滤波器对每个传感器通道采集到的数据进行滤波,并采用联合卡尔曼滤波、最小二乘融合算法和扩展卡尔曼滤波器实现多个传感器的数据融合,提高整个系统的鲁棒性,确保在某个或某些传感器失效的情况下,姿态获取单元仍然能够准确可靠的获得飞行器的姿态角度及运动信息。最后,基于LabVIEW进行数据采集,对姿态获取系统进行半实物仿真,飞行器本身以及某些不宜用实物接入的部分用数学模型来描述,其它以实物方式接入仿真系统,在计算机上实现数值、波形和3D模拟显示以进行实验研究,评价系统运行状况,修改设计方案,优化系统性能。
项目实现了飞行器姿态参考系统的模块化设计,提供与中心处理器的各种接口,整体系统体积小、功耗低、成本低,具有通用性且数字信号处理算法易于开发和升级。经实践操作证明,系统可以精确测量飞行器的姿态,有助于增强飞行器自主飞行的平稳性,提升飞行品质。
An Unmanned Aerial Vehicle (UAV) is a self-propelled aircraft that can fly autonomously and carry out multiple tasks. In order to realize UAV's intelligent and autonomous flight, onboard flight attitude reference system is one of the key techniques that we should solve. UAV's performance in executing an assignment depends largely on the stability of the flight attitude. Due to the presence of motion inertia and environment impact, the aircraft in flight is a highly unstable platform, and a tiny tilting angle can result in a considerable deviation of the aircraft's movement contrail.
To measure and compensate the kinetic parameters of onboard inertia devices and improve the UAV's autonomous flight performance to a great extent, the dissertation has devised a aircraft attitude reference system based on ARM with multi-sensor data fusion methods. The embedded attitude reference system is divided into two parts:signal capturing unit and attitude control unit. The former consists of posture angle sensors and angular velocity sensors, which supply the aircraft's real-time attitude information. Low-pass filtering algorithm and Kalman filtering theory are also applied to inhibit the measurement error of inertial sensors and ensure the measuring accuracy of signal acquisition unit. The latter generates steering-engines driving signal which serves as the actuator in the control of fins of rudders to make adjustments and corrections to aircraft's flight attitude timely and accurately.
Firstly, this paper introduces an ARM implementation including the overall hardware structure and specific sensor selection. The realization principles and methods of the attitude signal acquisition part are analyzed in detail. Then the method of generating the PWM control waveform based on ARM internal timers is present. And then we port the real-time kernel μ C/OS-II to Samsung ARM920TDMI and develop embedded multitask system, which is capable of dispatching attitude data acquisition channels and controlling PWM output channels.
Secondly, We design the linear Kalman filtering algorithm to process each single sensor signals to separate the white Gaussian noise from the useful signals and get the best estimation of angles or angular rates on the least mean-square error rule. In order to improve the robustness, we apply multi-sensors to collect the attitude signals in parallel. Federated Kalman filter, optimally weighted least squares fusion algorithm and extended Kalman filter are designed to process and fuse the data from each separate filter. Eventually, the accuracy and reliability of measurement system can be guaranteed, even if one or some transducers are ineffective.
Finally, the data acquisition in LabVIEW and hardware in-the-loop simulation for the attitude control system that are used to compare with Matlab simulation are discussed. Wave-display and three-dimensional dynamic state display are realized on computer to conduct experimental investigation. According to the experimental results, we could review the system's design for the optimal system.
The design of embedded attitude reference system is based on hardware modularization and various kinds of communication interfaces with ARM9micro processor. The integrate system has several advantages such as low power consumption, low cost, reduced size and better performance. The software is programmed in C++, and the optimization and refinement of algorithms are easy to be accomplished. Many experiments justify that the system can judge the aircraft's attitude angles accurately and balance the aircraft in flight.
首先介绍了基于ARM的小型无人飞行器姿态参考系统的硬件实现,给出了系统整体设计方案和具体的硬件选型,描述了飞行器姿态信号采集单元功能的实现原理和方法、姿态控制单元舵机驱动信号--PWM控制波形的生成程序以及移植μC/OS-II内核系统来实现嵌入式的实时多任务操作系统。随后阐述了采集单元的传感器信号的处理过程,使用低通滤波器和线性卡尔曼滤波器对每个传感器通道采集到的数据进行滤波,并采用联合卡尔曼滤波、最小二乘融合算法和扩展卡尔曼滤波器实现多个传感器的数据融合,提高整个系统的鲁棒性,确保在某个或某些传感器失效的情况下,姿态获取单元仍然能够准确可靠的获得飞行器的姿态角度及运动信息。最后,基于LabVIEW进行数据采集,对姿态获取系统进行半实物仿真,飞行器本身以及某些不宜用实物接入的部分用数学模型来描述,其它以实物方式接入仿真系统,在计算机上实现数值、波形和3D模拟显示以进行实验研究,评价系统运行状况,修改设计方案,优化系统性能。
项目实现了飞行器姿态参考系统的模块化设计,提供与中心处理器的各种接口,整体系统体积小、功耗低、成本低,具有通用性且数字信号处理算法易于开发和升级。经实践操作证明,系统可以精确测量飞行器的姿态,有助于增强飞行器自主飞行的平稳性,提升飞行品质。
An Unmanned Aerial Vehicle (UAV) is a self-propelled aircraft that can fly autonomously and carry out multiple tasks. In order to realize UAV's intelligent and autonomous flight, onboard flight attitude reference system is one of the key techniques that we should solve. UAV's performance in executing an assignment depends largely on the stability of the flight attitude. Due to the presence of motion inertia and environment impact, the aircraft in flight is a highly unstable platform, and a tiny tilting angle can result in a considerable deviation of the aircraft's movement contrail.
To measure and compensate the kinetic parameters of onboard inertia devices and improve the UAV's autonomous flight performance to a great extent, the dissertation has devised a aircraft attitude reference system based on ARM with multi-sensor data fusion methods. The embedded attitude reference system is divided into two parts:signal capturing unit and attitude control unit. The former consists of posture angle sensors and angular velocity sensors, which supply the aircraft's real-time attitude information. Low-pass filtering algorithm and Kalman filtering theory are also applied to inhibit the measurement error of inertial sensors and ensure the measuring accuracy of signal acquisition unit. The latter generates steering-engines driving signal which serves as the actuator in the control of fins of rudders to make adjustments and corrections to aircraft's flight attitude timely and accurately.
Firstly, this paper introduces an ARM implementation including the overall hardware structure and specific sensor selection. The realization principles and methods of the attitude signal acquisition part are analyzed in detail. Then the method of generating the PWM control waveform based on ARM internal timers is present. And then we port the real-time kernel μ C/OS-II to Samsung ARM920TDMI and develop embedded multitask system, which is capable of dispatching attitude data acquisition channels and controlling PWM output channels.
Secondly, We design the linear Kalman filtering algorithm to process each single sensor signals to separate the white Gaussian noise from the useful signals and get the best estimation of angles or angular rates on the least mean-square error rule. In order to improve the robustness, we apply multi-sensors to collect the attitude signals in parallel. Federated Kalman filter, optimally weighted least squares fusion algorithm and extended Kalman filter are designed to process and fuse the data from each separate filter. Eventually, the accuracy and reliability of measurement system can be guaranteed, even if one or some transducers are ineffective.
Finally, the data acquisition in LabVIEW and hardware in-the-loop simulation for the attitude control system that are used to compare with Matlab simulation are discussed. Wave-display and three-dimensional dynamic state display are realized on computer to conduct experimental investigation. According to the experimental results, we could review the system's design for the optimal system.
The design of embedded attitude reference system is based on hardware modularization and various kinds of communication interfaces with ARM9micro processor. The integrate system has several advantages such as low power consumption, low cost, reduced size and better performance. The software is programmed in C++, and the optimization and refinement of algorithms are easy to be accomplished. Many experiments justify that the system can judge the aircraft's attitude angles accurately and balance the aircraft in flight.
引文
[1](英)David H. Titterton, John L. Weston著,张天光,王秀萍,王丽霞等译.捷联惯性导航技术[M].北京：国防工业出版社,2007.
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[4]Yi, Yudan; Grejner-Brzezinska, Dorota A. Tightly-coupled GPS/INS integration using unscented Kalman filter and particle filter[C]. Proceedings of the Institute of Navigation -19th International Technical Meeting of the Satellite Division, ION GNSS 2006, v 4, p 2182-2191.
[5]Hirokawa, Rui; Ohata, Ryusuke; Suzuki, Taro; Ebinuma, Takuji. Performance evaluation of low cost multi-antenna GPS/INS for small UAVs[C].21st International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2008, v 1, p 533-543.
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[20]钱华明,夏全喜,阙兴涛,张强.基于Kalman滤波的MEMS陀螺仪滤波算法[J].哈尔滨工程大学学报,2010,31(9)：1217-1221.
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[23]李红岩.基于ARM的嵌入式PWM直流电动机控制系统[J].工矿自动化,2007,(6)：116-117.
[24]梁磊,王树强,许芹.多路PWM信号产生算法研究[J].电气电子教学学报,2008,30(4)：47-49.
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[2]高同跃.超小型无人直升机飞控系统及自主滞空飞行的研究[D].上海：上海大学,2008.
[3]Allen, John W.; Bevly, David M. Use of vision sensors and lane maps to aid GPS/INS under a limited GPS satellite constellation[C].22nd International Technical Meeting of the Satellite Division of the Institute of Navigation 2009, v 4, p 2143-2152.
[4]Yi, Yudan; Grejner-Brzezinska, Dorota A. Tightly-coupled GPS/INS integration using unscented Kalman filter and particle filter[C]. Proceedings of the Institute of Navigation -19th International Technical Meeting of the Satellite Division, ION GNSS 2006, v 4, p 2182-2191.
[5]Hirokawa, Rui; Ohata, Ryusuke; Suzuki, Taro; Ebinuma, Takuji. Performance evaluation of low cost multi-antenna GPS/INS for small UAVs[C].21st International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2008, v 1, p 533-543.
[6]徐田来,游文虎,崔平远.基于模糊自适应卡尔曼滤波的1NS/GPS组合导航系统算法研究[J].宇航学报,2005,26(5)：571-575.
[7]宋涛,王宏力,陆敬辉.H∞滤波及其在INS/CNS组合导航系统中的应用[J].电子设计工程,2011,19(1)：84-86.
[8]徐鹏.某型无人机姿态航向参考系统的设计与实现[D].西安：西北工业大学,2007.
[9]吴慧.微惯性测量系统关键器件的研究[D].太原：中北大学,2005.
[10]苏维嘉,王旭辉.新型加速度传感器在倾角测量中的应用研究[J].机械研究与应用,2007,20(5)：62-64.
[11]侯向锋,刘蓉,周兆丰.加速度传感器MMA7260在步态特征提取中的应用[J].传感技术学报,2007,20(3)：507-511.
[12]李硕,李小民.电子罗盘在无人机飞控器中的应用[J].兵工自动化,2007,26(12)：20-21.
[13]崔瑞芬,常佶.微型磁阻式电子罗盘的设计及罗差补偿方法的研究[J].计算机测量与控制,2010,18(11)：2608-2611.
[14]许宜申,王寿荣,王元山.单片三轴硅微机械振动陀螺仪研究[J].高技术通讯,2006,6(10)：1034-1038.
[15]任大海,顾启泰,毛刚,尤政,刘学斌.微型惯性测量组合标定技术[J].清华大学学报(自然 科学版),2001,41(8)：21-24.
[16]石志强,房刚,刘治星.角速度传感器误差校正研究[J].价值工程,2010,29(34),36-37.
[17]代刚,李枚,苏伟,邵贝贝.微惯性测量单元的误差整机标定和补偿[J].光学精密工程,2011,19(7)：1620-1625.
[18]Ke HAN, Hao WANG, Zhong-he JIN. Magnetometer-only linear attitude estimation for bias momentum pico-satellite[J]. Journal of Zhejiang University,2010,11(06):455-464.
[19]Chen Wei-cao,Gao Guo-wei,Wang Juan,Liu Li-li,Li Xi-lin. The study of the MEMS gyro zero drift signal based on the adaptive Kalman filter[C]. Key Engineering Materials, v 500, p 635-639, 2012, Advanced Materials in Microwaves and Optics.
[20]钱华明,夏全喜,阙兴涛,张强.基于Kalman滤波的MEMS陀螺仪滤波算法[J].哈尔滨工程大学学报,2010,31(9)：1217-1221.
[21]贺天鹏,王吉东,胡继忠,王松.基于双ARM的无人直升机飞控导航系统设计[J].北京航空航天大学学报,2010,36(3)：338-341.
[22]张悦.基于ARM处理器的无人机飞行控制计算机设计[D].哈尔滨：哈尔滨工程大学,2007.
[23]李红岩.基于ARM的嵌入式PWM直流电动机控制系统[J].工矿自动化,2007,(6)：116-117.
[24]梁磊,王树强,许芹.多路PWM信号产生算法研究[J].电气电子教学学报,2008,30(4)：47-49.
[25]陈杰,陈超,周建军,莫钦华.基于RTOS的小型无人机飞行控制系统[J].电子技术应用,2009,35(4)：36-40.
[26]许伦辉,修科鼎.基于ARM处理器的u COS- II移植[J].科技广场,2010,(7)：87-89.
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