Abstract
<jats:p>The integration of Computer Vision Systems (CVS) into robotic platforms has revolutionized assembly line operations, delivering substantial improvements in efficiency, accuracy, and flexibility. As manufacturing industries continue to evolve toward intelligent and adaptive automation, the need for advanced systems capable of handling complex and dynamic production tasks has become increasingly critical. CVS empowers robots with the ability to perceive and interpret visual information in real time, enabling them to recognize, locate, and manipulate objects with high precision even in environments characterized by variability and unpredictability. This capability extends automation beyond rigid, repetitive tasks and enables adaptive responses, such as identifying part variations, detecting defects, and supporting collaborative interactions with human workers. However, integrating CVS into robotic assembly lines also presents notable challenges, including high computational demands, sensitivity to variable lighting conditions, object occlusions, and the need for reliable real-time data processing. Additional concerns include ensuring robustness, affordability, and scalability across diverse industrial contexts. Despite these obstacles, continuous advancements in deep learning, sensor technologies, and edge computing offer promising pathways to address existing limitations. These innovations pave the way for greater resilience and adaptability of CVS-enabled robots, ultimately expanding their potential applications in modern manufacturing. This presentation provides a comprehensive overview of the incorporation of CVS into robotic assembly lines, emphasizing its transformative relevance, the technical and practical challenges encountered, and the opportunities for future development. The discussion highlights how this integration contributes to the vision of smart factories, where intelligent automation enhances productivity, quality assurance, and sustainable manufacturing practices.</jats:p>