微装配机器人关键技术研究
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摘要
微装配机器人是协助人类在微纳米空间进行精密作业的有效工具。显微视觉伺服是目前微装配机器人的主要控制手段，对提高装配效率、保证装配精度、实现全自动微装配有重要意义。本文以基于微装配机器人的微靶自动装配为研究对象，在实验室原有成果的基础上，对微装配机器人微夹持器系统，微夹持器和微小零件的多目标快速识别与分类，视觉跟踪及显微视觉伺服等若干关键技术进行了深入研究。
微夹持器是微装配机器人系统的末端执行机构，研究结构合理、功能完善的微夹持器是实现微装配的基础。本文针对微装配系统对末端执行器的要求，研究了两种不同类型的微夹持器：压电双晶片微夹持器和真空吸附微夹持器。为了实现微力检测，分别采用基于电阻应变计和基于聚偏二氟乙烯的两种传感器对微夹持器进行受力检测。实验结果表明，在显微视觉下，微夹持器可以安全可靠工作，为后续的微装配提供了保障。
多目标的识别是实现微装配机器人显微视觉伺服的基础。本文提出了一种改进的Zernike不变矩特征提取方法，将传统的笛卡尔坐标系下的Zernike不变矩映射到极坐标系下运算，从而大大提高了Zernike不变矩的计算准确性和旋转不变性。针对Zernike不变矩计算效率低下的问题，本文提出了Zernike不变矩的快速算法，将Zernike不变矩映射到十六分之一圆中进行计算，使所需计算像素点为原有极坐标Zernike矩方法的6.25%，新算法大大减少了Zernike不变矩的计算时间。同时，基于支持向量机的微装配机器人多目标识别实验结果表明，基于极坐标Zernike不变矩的分类正确率比基于笛卡尔坐标Zernike不变矩的分类正确率有明显提高。
对于微装配机器人显微视觉伺服系统，实时性一直是一个难以解决的重要问题。当前时刻的控制信号实际上是在上一时刻采集到的图像的图像特征信息，这样的控制信号会使机器人在开始运动时就产生偏差。提高位置预测精度和对目标运动变化的自适应性是解决上述问题的重要途径，本文基于当前统计模型，运用改进的模糊自适应卡尔曼滤波器来估计协方差矩阵Q(k)和R(k)，通过去除历史数据对系统的影响，从而准确预测出运动物体下一时刻的运动状态。实验数据表明改进的模糊自适应卡尔曼滤波器可以减少预测误差，同时可以快速检测出运动物体运动状态的改变。
为了建立基于微装配机器人显微视觉系统的精确预测模型，本文建立了精确的视觉伺服分时模型，从而得到了精确的视觉伺服延迟时间，从而利用改进的模糊自适应卡尔曼滤波算法对机械手和目标运动轨迹进行预测。同时，本文分析了微装配机器人显微视觉伺服运动路径，建立了基于模糊自适应卡尔曼滤波的控制结构，根据模糊自适应卡尔曼滤波的预测结果设计微装配机器人变结构视觉控制器，最终实现了微装配机器人显微视觉伺服下的精确轨迹跟踪。
最后，本文对微装配机器人的硬件结构以及各个组成部分的主要功能做了简要介绍，同时对微装配机器人系统的软件结构和软件控制流程进行了阐述。通过一系列实验验证了微夹持器系统，多目标识别系统和基于模糊自适应卡尔曼滤波的视觉伺服系统的可靠性。最终给出了微零件装配实验结果。
The microscope visual servoing approach and its key technology of micro-assembly robot are very important to improve the efficiency of assembly and ensure the accuracy of assembly. The dissertation mainly aims to develop a robotic system for sub-millimeter size microassembly. Some key technologies of microassembly robot system have been investigated, including micro-assembly system platforms, the microgrippers, the multi-target classification and identification, target tracking with micro vision, visual delay analysis and microscopic vision control et al.
Many kinds of microgrippers have been developed for handling and manipulating micro-sized objects in the fields of various applications. However, the micro-force sensing is still one of the most troublesome problems to improve the reliability of manipulation. To circumvent the problem, in this paper, two kinds of microgrippers, which are driven by two PZT bimorphs and the vacuum generator, are designed for the requirement of microassembly. In order to obtain the micro force, two kinds of sensors are introduced sensing the signal because of the strain of the cantilever, which are resistance strain gauge sensor and polyvinylidene difluoride (PVDF) sensor. The microgrippers can automatically pick and release the object by means of the PC monitoring system.
Multi micro parts recognition is one of the important tasks for the assembly of multi micro objects in microassembly. We propose a polar coordinate based algorithm for the computation of Zernike moments, which improves the invariance properties dramatically. Due to the symmetry property of the Zernike basis functions, Zernike moments can be obtained by computing only one sixteenth of the Zernike basis functions, which means that the number of pixels involved in the computation of Zernike moments is only6.75%of previous method. This leads to significant reduction in the computational complexity requirements. To achieve multi micro parts recognition with higher performance, we present a support vector machine algorithm, which employs polar Zernike moments based on edge extraction to obtain feature attribute to identify and classify the targets. The obtained results show the superiority of the proposed method.
Visual servoing has been around for decades, but the time delay is still one of the most troublesome problems to achieve target tracking. To circumvent the problem, in this paper, the Kalman filter is employed to estimate future position of the object. We present a current statistical model for moving target. An improved fuzzy adaptive Kalman filter, which is evolved from the Kalman filter, is put forward based on the current statistical model. The results show that the modified adaptive filter can improve the ability of maneuvering target tracking.
In order to introduce the improved fuzzy adaptive Kalman filter, the accurate time delays, which include the processing lag and the motion lag, need to be obtained. Thus, the delays of the visual control servoing system are discussed, and a generic timing model for the system is provided. Based on the timing model, the improved fuzzy adaptive Kalman filter is employed to predict manipulator and target motion. We build the visual servoing system construction based on fuzzy adaptive Kalman filter. According to the characteristics of micro visual servoing, design the servoing path of XY-plane and YZ-plane and establish the control structure of three-dimensional visual servoing. Finally, a variable structure control law for micromanipulation is presented. The results show that the proposed adaptive Kalman filter can improve the ability of moving target tracking based on visual servoing.
Finally, we analyze a typical structure of micromanipulation firstly, and describe the hardware system and software system of the microassembly system we have built. A series of experimental results show the reliability of the microgrippers, the polar Zernike moments based multi micro parts recognition system and the improved fuzzy adaptive Kalman filter based visual servoing system, which are mentioned above.
微夹持器是微装配机器人系统的末端执行机构，研究结构合理、功能完善的微夹持器是实现微装配的基础。本文针对微装配系统对末端执行器的要求，研究了两种不同类型的微夹持器：压电双晶片微夹持器和真空吸附微夹持器。为了实现微力检测，分别采用基于电阻应变计和基于聚偏二氟乙烯的两种传感器对微夹持器进行受力检测。实验结果表明，在显微视觉下，微夹持器可以安全可靠工作，为后续的微装配提供了保障。
多目标的识别是实现微装配机器人显微视觉伺服的基础。本文提出了一种改进的Zernike不变矩特征提取方法，将传统的笛卡尔坐标系下的Zernike不变矩映射到极坐标系下运算，从而大大提高了Zernike不变矩的计算准确性和旋转不变性。针对Zernike不变矩计算效率低下的问题，本文提出了Zernike不变矩的快速算法，将Zernike不变矩映射到十六分之一圆中进行计算，使所需计算像素点为原有极坐标Zernike矩方法的6.25%，新算法大大减少了Zernike不变矩的计算时间。同时，基于支持向量机的微装配机器人多目标识别实验结果表明，基于极坐标Zernike不变矩的分类正确率比基于笛卡尔坐标Zernike不变矩的分类正确率有明显提高。
对于微装配机器人显微视觉伺服系统，实时性一直是一个难以解决的重要问题。当前时刻的控制信号实际上是在上一时刻采集到的图像的图像特征信息，这样的控制信号会使机器人在开始运动时就产生偏差。提高位置预测精度和对目标运动变化的自适应性是解决上述问题的重要途径，本文基于当前统计模型，运用改进的模糊自适应卡尔曼滤波器来估计协方差矩阵Q(k)和R(k)，通过去除历史数据对系统的影响，从而准确预测出运动物体下一时刻的运动状态。实验数据表明改进的模糊自适应卡尔曼滤波器可以减少预测误差，同时可以快速检测出运动物体运动状态的改变。
为了建立基于微装配机器人显微视觉系统的精确预测模型，本文建立了精确的视觉伺服分时模型，从而得到了精确的视觉伺服延迟时间，从而利用改进的模糊自适应卡尔曼滤波算法对机械手和目标运动轨迹进行预测。同时，本文分析了微装配机器人显微视觉伺服运动路径，建立了基于模糊自适应卡尔曼滤波的控制结构，根据模糊自适应卡尔曼滤波的预测结果设计微装配机器人变结构视觉控制器，最终实现了微装配机器人显微视觉伺服下的精确轨迹跟踪。
最后，本文对微装配机器人的硬件结构以及各个组成部分的主要功能做了简要介绍，同时对微装配机器人系统的软件结构和软件控制流程进行了阐述。通过一系列实验验证了微夹持器系统，多目标识别系统和基于模糊自适应卡尔曼滤波的视觉伺服系统的可靠性。最终给出了微零件装配实验结果。
The microscope visual servoing approach and its key technology of micro-assembly robot are very important to improve the efficiency of assembly and ensure the accuracy of assembly. The dissertation mainly aims to develop a robotic system for sub-millimeter size microassembly. Some key technologies of microassembly robot system have been investigated, including micro-assembly system platforms, the microgrippers, the multi-target classification and identification, target tracking with micro vision, visual delay analysis and microscopic vision control et al.
Many kinds of microgrippers have been developed for handling and manipulating micro-sized objects in the fields of various applications. However, the micro-force sensing is still one of the most troublesome problems to improve the reliability of manipulation. To circumvent the problem, in this paper, two kinds of microgrippers, which are driven by two PZT bimorphs and the vacuum generator, are designed for the requirement of microassembly. In order to obtain the micro force, two kinds of sensors are introduced sensing the signal because of the strain of the cantilever, which are resistance strain gauge sensor and polyvinylidene difluoride (PVDF) sensor. The microgrippers can automatically pick and release the object by means of the PC monitoring system.
Multi micro parts recognition is one of the important tasks for the assembly of multi micro objects in microassembly. We propose a polar coordinate based algorithm for the computation of Zernike moments, which improves the invariance properties dramatically. Due to the symmetry property of the Zernike basis functions, Zernike moments can be obtained by computing only one sixteenth of the Zernike basis functions, which means that the number of pixels involved in the computation of Zernike moments is only6.75%of previous method. This leads to significant reduction in the computational complexity requirements. To achieve multi micro parts recognition with higher performance, we present a support vector machine algorithm, which employs polar Zernike moments based on edge extraction to obtain feature attribute to identify and classify the targets. The obtained results show the superiority of the proposed method.
Visual servoing has been around for decades, but the time delay is still one of the most troublesome problems to achieve target tracking. To circumvent the problem, in this paper, the Kalman filter is employed to estimate future position of the object. We present a current statistical model for moving target. An improved fuzzy adaptive Kalman filter, which is evolved from the Kalman filter, is put forward based on the current statistical model. The results show that the modified adaptive filter can improve the ability of maneuvering target tracking.
In order to introduce the improved fuzzy adaptive Kalman filter, the accurate time delays, which include the processing lag and the motion lag, need to be obtained. Thus, the delays of the visual control servoing system are discussed, and a generic timing model for the system is provided. Based on the timing model, the improved fuzzy adaptive Kalman filter is employed to predict manipulator and target motion. We build the visual servoing system construction based on fuzzy adaptive Kalman filter. According to the characteristics of micro visual servoing, design the servoing path of XY-plane and YZ-plane and establish the control structure of three-dimensional visual servoing. Finally, a variable structure control law for micromanipulation is presented. The results show that the proposed adaptive Kalman filter can improve the ability of moving target tracking based on visual servoing.
Finally, we analyze a typical structure of micromanipulation firstly, and describe the hardware system and software system of the microassembly system we have built. A series of experimental results show the reliability of the microgrippers, the polar Zernike moments based multi micro parts recognition system and the improved fuzzy adaptive Kalman filter based visual servoing system, which are mentioned above.
引文
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