Smart Robot Control: Integrating Computer Vision with Mechanical Engineering for Precision and Adaptability

Abstract

The convergence of computer vision and mechanical engineering is driving significant 
advancements in robot control systems, enabling greater precision, adaptability, and autonomy. 
This paper examines innovative approaches that integrate real-time visual processing with 
advanced mechanical actuation to enhance robotic performance across various domains. By 
leveraging machine learning, sensor fusion, and intelligent control algorithms, robots can perceive 
and respond to dynamic environments with improved accuracy and efficiency. Additionally, 
developments in mechatronics and adaptive feedback mechanisms further optimize motion 
planning and execution. This study explores key technologies, challenges, and future trends in the 
evolution of smart robotic control systems for industrial automation, healthcare, and autonomous 
navigation.