Vision-Driven Robotics: Merging Mechanical Engineering and Computer Vision for Advanced Control Systems

Abstract

Vision-driven robotics leverages the power of image processing, machine learning, and sensor technologies to enhance robot perception, navigation, and manipulation in dynamic environments. This paper explores the synergistic relationship between computer vision and mechanical engineering in robotic control systems, highlighting recent advancements in autonomous navigation, object detection, and precision control. By examining case studies and practical applications, this study underscores how vision-guided systems are transforming industries such as manufacturing, healthcare, and autonomous vehicles. We also address the challenges of real-time processing, accuracy, and integration, proposing future research directions to overcome these limitations. The fusion of these two disciplines is driving innovations that improve the autonomy, flexibility, and functionality of modern robots, making them more efficient and adaptable in complex, real-world scenarios.