Bridging Vision and Mechanics: Innovations in Intelligent Robotic Control Systems

Abstract

The convergence of computer vision and mechanical engineering is reshaping robotic control 
systems, enabling greater intelligence, precision, and adaptability. This paper explores how 
advanced visual perception, machine learning, and sensor fusion enhance robotic decision-making 
and interaction with dynamic environments. By integrating these capabilities with sophisticated 
mechanical design and control strategies, robots achieve improved motion accuracy, real-time 
adaptability, and autonomous functionality. Key advancements in feedback control, kinematics, 
and AI-driven actuation are examined, along with challenges and future research directions. This 
study highlights the crucial role of vision-mechanics integration in advancing robotics for 
industrial automation, healthcare, and autonomous systems.