隧道凿岩机器人控制系统及定位误差分析与补偿研究
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摘要
本文以国内首台隧道凿岩机器人为研究对象,为实现隧道凿岩机器人的自动控制而在隧道凿岩机器人的运动学建模、车体定位、机械臂运动学求逆、机械臂定位误差分析与补偿、控制系统的实现等方面进行了较为深入的研究：
1.针对两臂隧道凿岩机器人每个工作臂都是有9个自由度的多关节耦合型机械手的独特特点,结合凿岩机器人的实际工作环境特点,采用Denavit-Hartenberg方法进行了凿岩机器人及其工作环境的运动学建模,建立了机械臂末杆相对工作对象——隧道断面的正向运动学方程。并针对其独特机构特点提出了一种线性近似解耦—迭代法运动学求逆方法,将复杂的位姿关系近似线性化,将多变量间的复杂耦合(MIMO)模型近似解耦成多个单输入单输出(SISO)模型,再用迭代法求解运动学反解,通过大量仿真验证与实际应用验证,表明这种方法能满足隧道凿岩机器人控制中的运动学精度要求与实时性要求。
2.车体定位是钻臂凿岩定位精度的基础。采取在工作环境中安装激光的方式进行车体定位,推导了激光束坐标系与机械臂末杆坐标系间的关系矩阵,最终推导得到了机械臂末杆相对隧道断面间的坐标变换矩阵,解决了精确车体定位问题；根据特殊工况的简化分别得到了简单定位和恢复定位两种简便的车体定位方式。
3.通过大量的测试试验,发现凿岩机器人机械臂的钻孔定位存在较大的误差,直接影响着隧道施工精度。为了分析各环节的误差贡献,分别对大臂伸缩产生的误差、推进梁伸缩产生的误差、翻转轴翻转定位产生的误差、传感器输出信号引起的误差等环节进行试验研究与分析。试验表明,这些环节都存在较大的位置误差与姿态误差,其中系统误差为主要因素、伴随有随机误差。影响位姿误差的主要因素包括指令误差、检测误差、杆件受力弹性变形、机械传动误差和机械零部件的制造误差等等。
4.对引起误差的原因进行公式推导,从钻臂机构制造误差和检测误差引起的位姿误差,大臂挠性变形、推进梁的挠性变形、翻转机构定位精度产生的位姿误差等方面进行了计算分析,分析结果表明大臂端部的位置误差主要来源于大臂挠性变形,而推进梁伸缩过程中的误差原因是多方面的,挠性变形只占其中的小部分,液压油的压缩性是翻转机构定位误差产生的主要原因。
5.引入虚拟关节概念,对凿岩机器人钻臂定位误差进行误差补偿方法研究,根据误差原因和误差特征的不同,分别采取坐标变换矩阵直接补偿、增加坐标变换矩阵、增加虚拟关节等方法进行误差补偿,对传感器误差采取滤波与校正系统误差的方法减少误差。由于引起推进器伸缩过程中位姿误差的数学模型难以建立,而运用广义回归神经网络(GRNN)方法求解推进器伸缩过程中的位姿误差,获得了满意的结果。
6.采用主从式双层控制结构,构建了凿岩机器人计算机控制系统的硬件结构,解决了WINDOWS3. X环境下PC机与西门子CPU314之间的通信问题。将中文Windows3.2裁剪到最小可运行系统,应用Borland C++4.5开发了凿岩机器人智能控制系统,并实现了2吨多重的钻臂定位过程中的平稳运动和自动凿岩。
This research takes the first Chinese 2-boom tunnel rock-drilling robot as the research object. In order to automatize the rock-drilling rig, this research comes down to kinematics modeling, carriage positioning, kinematic inversing, positioning error analysis, error compensation, and developing a control system of the rock-drilling robot.
1. According to the unique characteristics of the boom of the two-boom tunnel rock-drilling robot, which is a coupled redundant manipulator with nine degrees of freedom, combined with the actual working environment characteristics, the Denavit-Hartenberg method is used to establish the modeling of rock-drilling robot and its working environment. The positive kinematics equation from the manipulator to the work object (the tunnel cross-section) is obtained. In allusion to the unique characteristics, a linear decoupled and iteration algorithm is put forward to change the complex position and pose relationship into linear approximation relationship, and decouple the complex and coupling multiple-input/ multiple-output(MIMO) model into many single-input/single-output(SISO) models. And then an iteration algorithm is applied to solve the inverse kinematics problem. A large number of simulation and practical application verify that this method can meet the kinematics precision and real-time requirements of the tunnel rock-drilling robot.
2. Carriage positioning is essential to ensure the boom's positioning accuracy. Refered to the laser beam, which is fixed on the wall of the tunnel, the relational matrix between the laser coordinate system and the end-rod coordinate system is derived, and ultimately the coordinate transformation matrix from the rig to the tunnel cross-section was derived, thus the problem of precise carriage positioning is solved. In accordance with the special conditions, two simple positioning ways are respectively got.
3. Through a large number of tests, large errors were found in the boring orientation of the manipulator, that has a strong impact on the accuracy of the tunnel construction. In order to analyze the contribution of various error factors, some experimental study and analysis are carried out on errors, which caused by the telescopic arm, the feeding beam, the rotate/flip shaft, and the output signal of sensors. The test results show that these parts could cause larger position and pose error, and among these error factors, the systematic error is the main factor, accompanied by random error. Main error factors include instruction errors, etection error, link flexibility, mechanical drive error and machining error, and so on.
4. The position and pose errors are try to be formulized. Compution and analysis were carried on into machining and detection error, the arm flexibility, the feeding beam flexibility, the positioning accuracy of the rotate/flip shaft, etc. And the results show that, the error of the arm positioning comes mainly from the arm flexibility, but the error of feeding beam positioning comes from many reasons, the beam flexibility just takes a small part, and the compressibility of hydraulic oil is the main reason for the error of the rotate/flip shaft positioning.
5. The concept of virtual joints is introduced into study on the error compensation of the manipulator positioning. In accordance with the different of the error causes and error characteristics, different methods are applied respectively to compensate the error, for instance, taking compensation directly into coordinate transformation matrix, adding the coordinate transformation matrix, adding the virtual joint, reducing the sensor error by filtering and correcting system error. Because it is difficult to establish the mathematical model of the error caused by the telescopic feeding beam, a GRNN (General Regression Neural Network) method is applied to compute the error, and satisfactory results have been obtained.
6. The hardware structure of rock-drilling robot computer control system is designed as master-slave control structure, and the communication problem between the computer and Siemens CPU314 under Windows3.X is settled. With cutting the Chinese Windows3.2 to the minimum operating system, a tunnel rock-drilling robot control system has been developed in Borland C++4.5, and smooth automatically moving of a 2-ton boom is achieved in the process of positioning, and automatically drilling comes true.
1.针对两臂隧道凿岩机器人每个工作臂都是有9个自由度的多关节耦合型机械手的独特特点,结合凿岩机器人的实际工作环境特点,采用Denavit-Hartenberg方法进行了凿岩机器人及其工作环境的运动学建模,建立了机械臂末杆相对工作对象——隧道断面的正向运动学方程。并针对其独特机构特点提出了一种线性近似解耦—迭代法运动学求逆方法,将复杂的位姿关系近似线性化,将多变量间的复杂耦合(MIMO)模型近似解耦成多个单输入单输出(SISO)模型,再用迭代法求解运动学反解,通过大量仿真验证与实际应用验证,表明这种方法能满足隧道凿岩机器人控制中的运动学精度要求与实时性要求。
2.车体定位是钻臂凿岩定位精度的基础。采取在工作环境中安装激光的方式进行车体定位,推导了激光束坐标系与机械臂末杆坐标系间的关系矩阵,最终推导得到了机械臂末杆相对隧道断面间的坐标变换矩阵,解决了精确车体定位问题；根据特殊工况的简化分别得到了简单定位和恢复定位两种简便的车体定位方式。
3.通过大量的测试试验,发现凿岩机器人机械臂的钻孔定位存在较大的误差,直接影响着隧道施工精度。为了分析各环节的误差贡献,分别对大臂伸缩产生的误差、推进梁伸缩产生的误差、翻转轴翻转定位产生的误差、传感器输出信号引起的误差等环节进行试验研究与分析。试验表明,这些环节都存在较大的位置误差与姿态误差,其中系统误差为主要因素、伴随有随机误差。影响位姿误差的主要因素包括指令误差、检测误差、杆件受力弹性变形、机械传动误差和机械零部件的制造误差等等。
4.对引起误差的原因进行公式推导,从钻臂机构制造误差和检测误差引起的位姿误差,大臂挠性变形、推进梁的挠性变形、翻转机构定位精度产生的位姿误差等方面进行了计算分析,分析结果表明大臂端部的位置误差主要来源于大臂挠性变形,而推进梁伸缩过程中的误差原因是多方面的,挠性变形只占其中的小部分,液压油的压缩性是翻转机构定位误差产生的主要原因。
5.引入虚拟关节概念,对凿岩机器人钻臂定位误差进行误差补偿方法研究,根据误差原因和误差特征的不同,分别采取坐标变换矩阵直接补偿、增加坐标变换矩阵、增加虚拟关节等方法进行误差补偿,对传感器误差采取滤波与校正系统误差的方法减少误差。由于引起推进器伸缩过程中位姿误差的数学模型难以建立,而运用广义回归神经网络(GRNN)方法求解推进器伸缩过程中的位姿误差,获得了满意的结果。
6.采用主从式双层控制结构,构建了凿岩机器人计算机控制系统的硬件结构,解决了WINDOWS3. X环境下PC机与西门子CPU314之间的通信问题。将中文Windows3.2裁剪到最小可运行系统,应用Borland C++4.5开发了凿岩机器人智能控制系统,并实现了2吨多重的钻臂定位过程中的平稳运动和自动凿岩。
This research takes the first Chinese 2-boom tunnel rock-drilling robot as the research object. In order to automatize the rock-drilling rig, this research comes down to kinematics modeling, carriage positioning, kinematic inversing, positioning error analysis, error compensation, and developing a control system of the rock-drilling robot.
1. According to the unique characteristics of the boom of the two-boom tunnel rock-drilling robot, which is a coupled redundant manipulator with nine degrees of freedom, combined with the actual working environment characteristics, the Denavit-Hartenberg method is used to establish the modeling of rock-drilling robot and its working environment. The positive kinematics equation from the manipulator to the work object (the tunnel cross-section) is obtained. In allusion to the unique characteristics, a linear decoupled and iteration algorithm is put forward to change the complex position and pose relationship into linear approximation relationship, and decouple the complex and coupling multiple-input/ multiple-output(MIMO) model into many single-input/single-output(SISO) models. And then an iteration algorithm is applied to solve the inverse kinematics problem. A large number of simulation and practical application verify that this method can meet the kinematics precision and real-time requirements of the tunnel rock-drilling robot.
2. Carriage positioning is essential to ensure the boom's positioning accuracy. Refered to the laser beam, which is fixed on the wall of the tunnel, the relational matrix between the laser coordinate system and the end-rod coordinate system is derived, and ultimately the coordinate transformation matrix from the rig to the tunnel cross-section was derived, thus the problem of precise carriage positioning is solved. In accordance with the special conditions, two simple positioning ways are respectively got.
3. Through a large number of tests, large errors were found in the boring orientation of the manipulator, that has a strong impact on the accuracy of the tunnel construction. In order to analyze the contribution of various error factors, some experimental study and analysis are carried out on errors, which caused by the telescopic arm, the feeding beam, the rotate/flip shaft, and the output signal of sensors. The test results show that these parts could cause larger position and pose error, and among these error factors, the systematic error is the main factor, accompanied by random error. Main error factors include instruction errors, etection error, link flexibility, mechanical drive error and machining error, and so on.
4. The position and pose errors are try to be formulized. Compution and analysis were carried on into machining and detection error, the arm flexibility, the feeding beam flexibility, the positioning accuracy of the rotate/flip shaft, etc. And the results show that, the error of the arm positioning comes mainly from the arm flexibility, but the error of feeding beam positioning comes from many reasons, the beam flexibility just takes a small part, and the compressibility of hydraulic oil is the main reason for the error of the rotate/flip shaft positioning.
5. The concept of virtual joints is introduced into study on the error compensation of the manipulator positioning. In accordance with the different of the error causes and error characteristics, different methods are applied respectively to compensate the error, for instance, taking compensation directly into coordinate transformation matrix, adding the coordinate transformation matrix, adding the virtual joint, reducing the sensor error by filtering and correcting system error. Because it is difficult to establish the mathematical model of the error caused by the telescopic feeding beam, a GRNN (General Regression Neural Network) method is applied to compute the error, and satisfactory results have been obtained.
6. The hardware structure of rock-drilling robot computer control system is designed as master-slave control structure, and the communication problem between the computer and Siemens CPU314 under Windows3.X is settled. With cutting the Chinese Windows3.2 to the minimum operating system, a tunnel rock-drilling robot control system has been developed in Borland C++4.5, and smooth automatically moving of a 2-ton boom is achieved in the process of positioning, and automatically drilling comes true.
引文
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