计算智能在三维表面扫描机器人系统中的应用研究
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摘要
为了将集数字化、信号处理、机器视觉及激光传感器等先进技术于一体的三维表面扫描系统有效地应用于制造加工领域,结合具有灵活性及柔性的六自由度机器人组成三维表面扫描机器人系统。为了提高系统扫描精度,本文围绕系统中有关建模标定方法等问题,引入计算智能中的先进算法。主要研究内容和贡献如下：
1.建立三维表面扫描机器人系统的数学模型,包括机器人本体的数学模型,线结构光视觉传感器的数学模型,摄像机的三种畸变模型。
2.建立Motoman-HP3L机器人运动学仿真环境。建立机器人运动学本体标定的实验平台,提出机器人本体的运动学标定目标函数,采用非线性优化方法得到机器人的实际D-H参数,提高了机器人的绝对定位精度。提出二进制人工蜂群算法并用于标定最优测量形位的选择,进一步提高了机器人的绝对定位精度。
3.研究利用同心圆求得圆心真实投影点的方法。给出同心圆目标的亚像素提取方法及流程。提出一种线结构光视觉传感器的标定新方法,为了验证该方法的有效性,设计并建立一种基于同心圆标定靶的实验平台,实验结果表明该方法能够提高线结构光视觉传感器的测量精度。
4.为了提高三维扫描系统整体精度,在系统中引入微粒群径向基神经网络技术,在分析微粒群算法稳定性的基础上,提出一种基于微粒群径向基神经网络的系统整体误差建模及其补偿实施方案。
5.总结了现有的曲面重构方法并对其进行分类。提出一种改进的径向基神经网络用于曲面重构。提出一种具有分数阶项和动量项的反向传播(BP)算法,将其应用于多层前馈神经网络曲面重构,提高了网络的收敛速度。对比径向基神经网络和多层前馈神经网络用于曲面重构的优缺点。
最后在总结全文工作的基础上,给出进一步的工作设想。
The three-dimensional (3D) surface scanning system is an integration of digitalization, signal processing, machine vision and laser scanner. In order to utilize the 3D laser scanning technology in manufacturing field effectively, a 3D surface scanning robotic system is established based on a flexible six degree-of-freedom industrial robot. The modeling and calibration methods for the 3D surface scanning robotic system are shed light on and advanced algorithms in computational intelligence are employed to enhance its measurement accuracy; the main,contributions are summarized as follows:
1. The mathematical model of the robotic system is established, including the main body mathematical model of robotic system, the mathematical models of line structured light vision sensor and the camera pinhole model with its distortion models.
2. A kinematics simulation platform for Motoman-HP3L is established via Matlab. A test bed for robot calibration is designed. A performance index for robot calibration is proposed so that the absolute robot positioning accuracy is improved due to determination of the real robot D-H parameters via nonlinear optimization methodology. A binary artificial bee colony algorithm (BABC) is developed and then employed for optimal selection of measurement configurations, the real experiments demonstrated that the robot positioning accuracy is further improved.
3. The approach to obtain real circle projected center using concentric circle is discussed. The procedure of sub-pixel edge detection for concentric circle target is also given in detail. A novel approach to calibrate a line structured light vision sensor is proposed and then an experimental platform based on designed concentric circle calibration target is established to validate the effectiveness of proposed novel calibration approach. The real experimental results illustrated that the measurement accuracy of line structured light vision sensor can be enhanced via the novel calibration approach.
4. In order to improve the system measurement accuracy, based on the convergence analysis of PSO, an error modeling and compensation scheme for 3D surface scanning robotic system is also given.
5. The existing surface reconstruction methods are summarized and classified. The artificial neural network (ANN) for surface reconstruction is studied. An improved radial basis function neural network (RBFNN) is employed for surface reconstruction. An enhanced back propagation algorithm, namely gradient descent with fractional power and momentum terms (GDFPM), is proposed and applied for training the multi-layer perceptron neural network (MLPNN). The MLPNN is used for surface reconstruction, the experimental results validates its higher convergence rate compared with traditional BP algorithm. The comparisons between RBFNN and MLPNN for surface reconstruction are also made.
Finally, future work is listed based on the conclusion of whole work.
1.建立三维表面扫描机器人系统的数学模型,包括机器人本体的数学模型,线结构光视觉传感器的数学模型,摄像机的三种畸变模型。
2.建立Motoman-HP3L机器人运动学仿真环境。建立机器人运动学本体标定的实验平台,提出机器人本体的运动学标定目标函数,采用非线性优化方法得到机器人的实际D-H参数,提高了机器人的绝对定位精度。提出二进制人工蜂群算法并用于标定最优测量形位的选择,进一步提高了机器人的绝对定位精度。
3.研究利用同心圆求得圆心真实投影点的方法。给出同心圆目标的亚像素提取方法及流程。提出一种线结构光视觉传感器的标定新方法,为了验证该方法的有效性,设计并建立一种基于同心圆标定靶的实验平台,实验结果表明该方法能够提高线结构光视觉传感器的测量精度。
4.为了提高三维扫描系统整体精度,在系统中引入微粒群径向基神经网络技术,在分析微粒群算法稳定性的基础上,提出一种基于微粒群径向基神经网络的系统整体误差建模及其补偿实施方案。
5.总结了现有的曲面重构方法并对其进行分类。提出一种改进的径向基神经网络用于曲面重构。提出一种具有分数阶项和动量项的反向传播(BP)算法,将其应用于多层前馈神经网络曲面重构,提高了网络的收敛速度。对比径向基神经网络和多层前馈神经网络用于曲面重构的优缺点。
最后在总结全文工作的基础上,给出进一步的工作设想。
The three-dimensional (3D) surface scanning system is an integration of digitalization, signal processing, machine vision and laser scanner. In order to utilize the 3D laser scanning technology in manufacturing field effectively, a 3D surface scanning robotic system is established based on a flexible six degree-of-freedom industrial robot. The modeling and calibration methods for the 3D surface scanning robotic system are shed light on and advanced algorithms in computational intelligence are employed to enhance its measurement accuracy; the main,contributions are summarized as follows:
1. The mathematical model of the robotic system is established, including the main body mathematical model of robotic system, the mathematical models of line structured light vision sensor and the camera pinhole model with its distortion models.
2. A kinematics simulation platform for Motoman-HP3L is established via Matlab. A test bed for robot calibration is designed. A performance index for robot calibration is proposed so that the absolute robot positioning accuracy is improved due to determination of the real robot D-H parameters via nonlinear optimization methodology. A binary artificial bee colony algorithm (BABC) is developed and then employed for optimal selection of measurement configurations, the real experiments demonstrated that the robot positioning accuracy is further improved.
3. The approach to obtain real circle projected center using concentric circle is discussed. The procedure of sub-pixel edge detection for concentric circle target is also given in detail. A novel approach to calibrate a line structured light vision sensor is proposed and then an experimental platform based on designed concentric circle calibration target is established to validate the effectiveness of proposed novel calibration approach. The real experimental results illustrated that the measurement accuracy of line structured light vision sensor can be enhanced via the novel calibration approach.
4. In order to improve the system measurement accuracy, based on the convergence analysis of PSO, an error modeling and compensation scheme for 3D surface scanning robotic system is also given.
5. The existing surface reconstruction methods are summarized and classified. The artificial neural network (ANN) for surface reconstruction is studied. An improved radial basis function neural network (RBFNN) is employed for surface reconstruction. An enhanced back propagation algorithm, namely gradient descent with fractional power and momentum terms (GDFPM), is proposed and applied for training the multi-layer perceptron neural network (MLPNN). The MLPNN is used for surface reconstruction, the experimental results validates its higher convergence rate compared with traditional BP algorithm. The comparisons between RBFNN and MLPNN for surface reconstruction are also made.
Finally, future work is listed based on the conclusion of whole work.
引文
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