近年來，隨著自動化、智慧化技術的進步與應用，工廠轉型為自動化生產已成為趨勢。儘管如此，仍有許多生產流程難以被機器取代，因此高精度量測與補償技術的應用變得越來越重要，這些技術能夠降低誤差、提高產品品質和生產效率，同時推動自動化轉型，增強企業競爭力。在製造與組裝過程中，工件留下製造與組裝誤差也是一大挑戰，這些誤差是造成系統自動化難度提升的原因之一。目前，產業對加工的要求不僅在於提高產能，對於產品的品質要求也要求越來越高，如汽機車業等對於零組件加工與組裝精度與強度的要求增加，量測並補償這些誤差能降低誤差和提高精度，進而實現自動化的生產與加工。
因此，本研究介紹了一套雙目立體視覺系統，能夠同時量測待測物上所有特徵點的，系統結合了影像處理技術與邊緣強化特徵演算法尋找待測物特徵中心，並對特徵中心進行三維點座標重建，取得待加工圓心點的相對三維座標。系統能夠在距離待測物215 mm的位置進行量測，量測範圍可達450 mm × 200 mm，且準確度可達0.1 mm以內，重複度可達0.03 mm以內。雙目視覺技術能有效避免待測物反光所影響，提供解決方案並保持掃描精度與速度，此方法適合不能直接接觸待測物的掃描量測，產出的三維座標轉換至機械手臂世界座標軸，最終解決加工問題與工件製造過程中的誤差疊加問題，有效提升了機械手臂的加工精度。

In recent years, automation and intelligent technology have been increasingly applied in factories, leading to a trend of automated production. However, in many production processes, machines are still difficult to replace humans. High-precision measurement and compensation technology are increasingly important in reducing errors and improving product quality. One major challenge in the manufacturing and assembly process is the errors left by workpieces, which makes automation difficult. The industry now demands not only increased production capacity but also increased precision and strength of component processing and assembly. Measuring and compensating for these errors can achieve automated production and processing.
To address this challenge, this study proposes a binocular stereo vision system that can simultaneously measure all 3D coordinates of feature points on the workpiece. The system combines image processing technique with edge-enhancement feature algorithms to find the feature of workpiece and perform three-dimensional point coordinates reconstruction of the feature centers. The system can measure at 215 mm from the workpiece, with a measurement range of 450 mm × 200 mm, an accuracy within 0.1 mm and a repeatability within 0.03 mm. This method is suitable for non-contact scanning measurement and avoids the influence of workpiece reflection. The proposed system can effectively improve the machining accuracy of the robot’s machining processes and solve the error accumulation problem in the manufacturing process.

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