本論文研發出一套應用於三軸工具機之視覺輔助定位系統，此視覺輔助定位系統與CNC運動控制系統整合，藉由此研發之系統可對放置於工作台上的工件取像，找出工件上定位標記對應在機械座標系中的位置，從而建立工件座標系與機械座標系間平移及旋轉的關係，將此關係傳給CNC運動控制系統內之座標管理單元，對實際工件安裝之偏置量加以補償，進行工件座標系內的NC程式運動控制。

本論文首先針對現有的機械視覺硬體進行研究，分析其運作原理，找出相機、鏡頭、光源選用配置的條件及方法，以及不同工程應用中硬體搭配的限制，然後將從影像硬體取出的影像，用創新之全形灰階函數積分法加半相位補償進行處理，並比較其與不同定位演算法之優缺點，最後於本實驗室開發之PC-Based工具機控制器中實現此全形灰階函數積分法加半相位補償之十字標記定位。實驗用之CCD像素解析度為40μm時，以雷射驗證此研發之視覺輔助定位系統，證實標記定位之定位平均誤差為-0.1836μm，3標準差定位重現精度高達±0.2694μm，換算純後端軟體影像處理與分析約為1/70 像素。

A vision-assisted positioning system is developed in this study. This vision-assisted positioning system is integrated in a PC-Based CNC controller. With the help of this system, the image of a workpiece on the work table is captured by a CCD camera, and the location of the positioning mark in machine coordinates is calculated. With these data, the translational and rotational relationship between workpiece coordinates and machine coordinates are established and returned to the coordinate management unit in the CNC control system. The actual deviations of the workpiece mounting location are compensated so that the CNC machine tools can perform the motion control and cut the workpiece with NC codes defined in workpiece coordinates.

First, the working principle of the machine vision hardware has been researched to find the way and rules for selecting optimal components including camera, lens and lighting system. Different image processing methods are developed to analysis the captured mark and the accuracy of these methods are compared. In the end, a novel high accuracy method, the full-form grey function integration method, for the cross mark is used and integrated in a PC-Based CNC controller. The accuracy of the developed vision-assisted positioning system is successfully tested on a machine tool. The pixel resolution of the selected CCD camera is 40μm. The achieved average accuracy is -0.1836μm, repeatability for three standard deviations of the mark positioning is ±0.2694μm, which is about 1/70 pixel。

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