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题名: 面向旋巽飞行机器人的组合导航系统研究
其他题名: Research on Integrated Navigation System for Rotary Wing Aerial Robot
作者: 戴磊
导师: 韩建达
分类号: V249.32
关键词: 组合导航 ; 数据融合 ; 卡尔曼滤波 ; 旋翼飞行机器人
索取号: V249.32/D17/2012
学位专业: 模式识别与智能系统
学位类别: 博士
答辩日期: 2012-05-24
授予单位: 中国科学院沈阳自动化研究所
学位授予地点: 中国科学院沈阳自动化研究所
作者部门: 机器人学研究室
中文摘要: 近年来无人机技术和传感器技术的发展,促进了小型、低功耗组合导航系统的研究。本文对应用于旋翼飞行机器人的组合导航系统开展研究。在查阅大量文献的基础上,本文研究了捷联惯性导航系统、GPS导航系统、组合导航等导航系统的工作原理,重点研究了应用于旋翼飞行机器人组合导航系统的组成、结构、解算原理及数据融合方法。本文的主要工作可归纳为:1)对惯性传感器的敏感原理进行研究,重点研究了微机械陀螺仪和加速度计的敏感原理,分析其误差来源。在此基础上,研究了微机械陀螺仪误差的建模与补偿方法。针对陀螺仪的随机漂移误差,提出了改进软阈值小波阈值降噪的算法。该方法使用db6小波,5尺度分解能够有效降低传感器随机漂移误差,提高组合导航解算的精度。2)对旋翼飞行机器人组合导航系统中的电子磁罗盘的误差来源进行研究。从软/硬件两方面综合分析,总结出影响磁罗盘航向解算精度的5个主要因素。针对上述误差因素提出了软件和硬件解决办法;特别是针对硬磁罗差和标度因数误差的校准,提出了简化的校准步骤和实现算法。简化后的磁罗盘校准步骤避免了磁罗盘校准过程中将载体竖起。实际磁罗盘校准测试表明,与传统方法相比该方法不仅降低操作的难度,而且能有效的修正原始磁场测量值的椭球分布,此方法具有较强的实用性。3)研究旋翼飞行机器人的航姿和速度参考系统实现方法。基于运动学和动力学方程建立了航姿与速度参考系统的数学模型,设计两个独立的扩展Kalman滤波器分别对航姿和速度进行估计;针对传统EKF滤波器对量测噪声敏感的问题,结合旋翼飞行机器人实际飞行数据,提出基于模糊逻辑的自适应Kalman滤波器。该算法能够根据噪声特性的变化和调试经验,自适应调节的滤波参数,克服传感器噪声和飞行模态的影响。利用实际飞行数据的实验证明,该方法对噪声的变化具有较强的自适应性,能有效提高航姿与速度解算精度。4)研究应用于旋翼飞行机器人的组合导航系统的传感器组合方式和数据解算原理,建立了组合导航系统的数学模型;使用EKF、UKF方法进行组合导航系统数据解算的仿真实验。通过与参考系统进行数据对比,证明UKF算法具有更好的稳定性和估计精度,基于EKF方法的组合导航解算具有更好的实时性。5)参考国内外无人直升机导航系统,建立组合导航系统样机。该样机以嵌入式系统作为数据采集和处理的平台,采用芯片级IMU、GPS接收机、气压计和磁罗盘等传感器。针对系统设计及实验中出现的问题,对导航系统的软调度机制、数据处理、隔振器安装方式及硬件设计进行优化。通过上述软/硬件改进,提高数据采集的实时性、减小测量噪声的影响、提高导航解算的速度和精度。大量的实际测试表明该系统具有良好的实时性、较高的可靠性。 通过上述理论研究,将成果应用于实际系统,建立起一套小型组合导航系统。该系统为旋翼飞行机器人的提供其姿态、速度、位置等信息,其精度、实时性和可靠性能够满足旋翼飞行机器人系统的需要。
英文摘要: In recent years, the researches of small and low power consumption integrated navigation system is promoted with the developing of unmanned aerial vehicle technology and sensor technology. This dissertation mainly studies on the integrated navigation system for rotary wing aerial robot. Base on abundant literatures, this dissertation studies the principle of strapdown inertial navigation system, GPS navigation systems, and integrated navigation system. The composition, working principles and data fusion methods of rotary wing aerial robot’s integrated navigation system is analysed. The main work of this dissertation can be summarized as: 1) The sensitive principles of inertial sensors are described in the dissertation. And the sensitive principles, characteristics and error sources of MEMS gyroscope and accelerometer are analysed in particular. Then this dissertation studies the error modeling and compensation methods for MEMS gyroscope. To compensation the random drift error of gyroscope, an improved soft thresholding method based on wavelet denoising algorithm is promoted. With db6 Wavelet, at 5 levels, the random noise of sensor is reduced effectively, and the accuracy of integrated navigation is improved. 2) The errors source of the magnetic compass is researched. Base on the hardware design and the measurement theory of the compass, 5 main factors which affect the heading calculation precision of the magnetic compass, are summarized. In response to the mentioned error factors, software and hardware solutions are proposed. A simplified measurement method for calibration the magnetic compass and the software achievement have been proposed for calibration the hard magnetism errors and scale errors. The steps of calibration have been simplified, and sticking up the robot to calibrate the compass has been avoided. The calibration test of system shows that the new method can effectively correct the measurements’ ellipsoidal distribution of the original magnetic field. This method has a strong practical.  3) The attitude, heading, and velocity reference system of rotary wing aerial robot is studied. Based on kinematics and kinetics equations, two separated extended Kalman filters for attiude, heading, and velocity reference system are designed. Consideing the noise–sensitive character of Kalman filter and the real flight data of rotary wing aerial robot platform, an adaptive Kalman filter based on fuzzy logic is designed to estimate the attitude, heading and velocity of the robot. According the debugging experience and noise characteristics, this method can adaptive adjust the filtering parameters to overcome the effect of sensor noise and flight mode. Using actual flight data, the experiment proved that this method has strong adaptability to overcome the change of noise, and it can improve the attitude and velocity calculation precision.  4) The calculation and structure of rotary wing aerial robot’s integrated navigation system are studied. The mathematical model of integrated navigation system is established according to the feature of the robot. Simulation of integrated navigation systems are conducted with real fly data using EKF and UKF methods. Through the experimental, the UKF method has better accuracy and good stability, and the EKF method has better real-time navigation solution.  5) Learning from the experience of UAV’s navigation system at home and abroad, an integrated navigation system for rotary wing aerial robot is established. This system uses embedded processor as the data acquisition and processing computer. The sensors of this system contains: IMU, GPS receiver, barometer and magnetic compass. These sensors are of chip level. The scheduling of software, data processing, damper installation and hardware design are optimized during the debugging and experiment. Through the hardware and software improvements, the measurement noise is reduced, navigation solution speed and accuracy are premoted, and the real-time calculation feature is kept. By a lot of the actual flying test, this integrated navigation system shows good performance in real time and high reliability. By the above theory study, the results are used in actual system. A small integrated navigation system was established. It can provide attitude, velocity, position and other information for rotary wing aerial robot. The precision, real-time performance, and reliability meet the needs of rotary wing aerial robot.
语种: 中文
产权排序: 1
内容类型: 学位论文
URI标识: http://ir.sia.cn/handle/173321/9399
Appears in Collections:机器人学研究室_学位论文

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Recommended Citation:
戴磊.面向旋巽飞行机器人的组合导航系统研究.[博士学位论文].中国科学院沈阳自动化研究所.2012
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