【作者】 刘章青；

【导师】 李方敏；

【作者基本信息】 武汉理工大学 ， 通信与信息系统， 2012， 硕士

【摘要】 随着社会经济的不断发展,科技的进步和人民生活水平的不断提高,要求巷道施工向安全、优质、高效、无人化方向发展,加强掘进机向智能化方向发展是一个大的趋势,开发研制自动掘进机是解决该问题的必然方向,掘进机位置及姿态的自动测定是全自动掘进机必须解决的核心问题之一。为实现掘进机的自动导向,需要对掘进机的姿态角(水平角、滚动角、俯仰角)以及水平偏向位移进行实时的测量。论文研究了激光光斑检测定位系统,通过该系统的光斑定位与跟踪算法获取光斑中心坐标,进而转换为掘进机的水平偏向位移,提供给控制系统,结合航姿仪采集的姿态角数据控制掘进机的自动导向。针对光斑定位提取掘进机姿态参数,论文选取适用于激光光斑识别与跟踪定位的算法,对某些经典算法进行比较,选取适用于本系统的算法,并对其进行优化。运动光斑的识别与跟踪定位算法主要分为预处理、目标识别、形态学滤波和目标跟踪算法四部分。本文对每部分的算法进行了详细分析,预处理部分采用了改进的基于灰度差值的快速中值滤波算法。目标识别则是采用了改进的Otsu阈值分割法,并引入了自适应波门。目标跟踪定位算法主要对重心法和圆拟合法两种算法进行了误差、精度比较分析。此外,还讨论了暗背景和亮背景两种特殊情形下的光斑识别与跟踪算法。另外,考虑到磁场受到干扰时,航姿仪采集水平偏向角不精确,因而提出两束激光提取水平偏向角,并对其进行建模,从而使得水平偏向角的获取更加稳定。最后,本文通过实验测试验证了算法的可行性,高精度性。各种不同环境下的测试,验证本系统光斑定位的精确性,抗干扰性,满足系统需求。并设计了标定系统对掘进机姿态检测系统采集的姿态角及偏向位移进行标定,进一步验证系统的精确性。更多还原

【Abstract】 With the continuous development of the socio-economic,advancement of technology and the improvement of people’s living standards, require construction of the tunnel to the development of safe、high quality、efficient、unmanned direction. strengthening the boring machine to the intelligent direction is a big trend. It is the inevitable direction to develop automatic boring machine, It is one of the core problem to determine position and attitude for the boring machine automatically. To achieve Boring machine auto-oriented, we need to measure the attitude angle of boring machine(deviation, roll, pitch), and horizontal deflection displacementThis paper researched laser spot detection and location system, we use the spot location and tracking algorithm to obtain the coordinates of the spot center, and then converted to a bias level of the boring machine displacement, provided to the control system.combined with the attitude angle data collected by AHRS instrument,to control boring machine auto-oriented.Aim to extract boring machine attitude parameters from spot location. This paper selected the laser spot identification and tracking、positioning algorithm. we selected some classical algorithm and optimize it. It is divided into, pre-processing. target recognition, morphological filtering and target tracking algorithm in four parts. In this paper detailed analysis of every part of the algorithm, the pre-processing part gived the fast median filtering algorithm based on gray level difference. Target recognition using improved Otsu Thresholding Method, and the introduction of adaptive gate. Target locating and tracking of gravity and circle fitting methods were compared of error and accuracy. It also discussed two special cases of the dark background and bright background of the spot identification and tracking algorithm. In addition, taking into account the distribution of the magnetic field, deviation angle collection of AHRS instrument is inaccurate, and thus the two laser beams to extract deviation angle is proposed, and its modeling, to make deviation angle be more stable.Finally, to verify the feasibility of the algorithm, high-precision by further experiments. Under various environmental tests to verify the accuracy of the spot location of the system, interference immunity, to meet this system requirements. I designed a calibration system to calibrate the boring machine attitude and bias displacement extract from the boring machine attitude detection system, to further verify the accuracy of the system.更多还原