Integrating Computer Vision and Mechanical Engineering for Intelligent and Adaptive Robot Control

Abstract

The integration of computer vision with mechanical engineering is transforming robot control 
systems, enabling enhanced intelligence, precision, and adaptability. This paper explores how 
advanced visual perception, deep learning, and real-time image processing can be synergized with 
mechanical actuation and control mechanisms to improve robotic performance. By incorporating 
sensor fusion, adaptive control algorithms, and feedback loops, robots can navigate complex 
environments, perform intricate tasks, and respond dynamically to changing conditions. This study 
highlights key technological advancements, implementation challenges, and future research 
directions in developing intelligent, vision-guided robotic systems for applications in 
manufacturing, healthcare, and autonomous systems.