本論文旨在開發一套基於工業機械手臂的全自主焊接系統，透過整合雷射視覺感測器，克服機器人焊接中與系統校正相關限制。傳統手動校正過程耗時且容易出錯，在準確焊接操作上又缺乏精度，限制了當前系統校正的成效。為了解決上述限制，本文提出了一種自動化校正方法，用於機器人焊接系統中並導入雷射視覺感測器。該方法目的於改善校正過程的精度和效率，確保機械手臂在焊接過程中的移動精確性。此外，該提出的解決方案為整體基於雷射視覺的機器人焊接研究中提供基礎，以及評估效率和可行性。
同時，本論文還介紹了一種具有智慧縫線提取的路徑規劃方法，應用於所提出的自動化機器人焊接系統中。該方法在透過手動教導與離線編程方式，為機械手臂建立三維曲線焊道軌跡，提供一種高效的替代方案，並且能夠優先考量焊縫位置。此方法使用兩階段程序，首先結合2D影像與3D點雲進行全域規劃，接著實施基於雷射視覺的局部定位，減小與點雲相關誤差。此外，還研發了一個穩健的即時焊縫追踪系統，利用雷射視覺感測器解決特徵點提取、端點檢測和基於軌跡控制策略上的各種挑戰，並在焊縫追踪方面展示了先進性，提供即時監測能力，提升機械手臂在焊接應用上的效率與精度。總而言之，本研究提供一種智慧系統解決方案，可達到路徑規劃、縫線提取與追踪，在工業應用中加強焊接準確性、效率和整體質量，為自動化機械手臂焊接領域做出貢獻。

This thesis aims to develop an autonomous welding system using industrial robots to tackle the limitations associated with system calibration in robotic welding by integrating laser vision sensors. Manual calibration processes that are time-consuming, prone to human error, and lack the precision required for accurate welding operations may limit current system calibration. To address these limitations, an automated calibration method is proposed for laser vision sensors used in robotic welding systems. The method aims to improve the precision and efficiency of the calibration process while ensuring the accurate movement of the robot arm during welding operations. Furthermore, this solution provides the basis for evaluating the efficiency and accuracy of the entire laser vision-based robotic welding system.
The thesis also introduces a path planning approach with intelligent seam extraction for automatic robot welding systems. This approach offers an efficient alternative to manual teaching programming and offline programming for 3D curved welds while prioritizing seam precision. It utilizes a two-stage process that combines 2D images with a 3D point cloud for global planning. A local positioning approach based on laser vision is then implemented to reduce the error associated with the point cloud. Furthermore, a robust real-time tracking system for weld seams is being developed using a laser vision sensor. The system addresses challenges related to feature point extraction, endpoint detection, and trajectory-based control strategies. The system demonstrates advancements in weld seam tracking, offering real-time and precise monitoring capabilities for improved efficiency and accuracy in robotic welding applications. Overall, this research contributes to the field of automatic robot welding by providing intelligent solutions for path planning, seam extraction, and tracking. These solutions improve the accuracy, efficiency, and overall quality of welds in industrial environments.

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