Vision-Guided Robotics: Integrating Computer Vision and Mechanical Engineering for Precision Control

Abstract

The fusion of computer vision and mechanical engineering is revolutionizing robotic control 
systems, enabling enhanced autonomy, precision, and adaptability. This paper explores how realtime image processing, deep learning, and sensor fusion contribute to advanced robotic perception 
and decision-making. By integrating these vision-based capabilities with sophisticated mechanical 
design and adaptive control mechanisms, robots can navigate complex environments, optimize 
motion planning, and execute tasks with greater efficiency. Key advancements in AI-driven 
actuation, feedback control, and kinematics are discussed, along with challenges and future 
research directions. This study highlights the critical role of vision-driven robotics in transforming 
industrial automation, healthcare, and autonomous systems.