Perception-Driven Control Systems: Bridging Sensing, Cognition, and Action in Intelligent Automation

Abstract

Perception and control are foundational pillars of intelligent systems, enabling robots, automated machines, and intelligent devices to interact with dynamic environments effectively. This paper explores the integration of advanced perception technologies with adaptive control systems, highlighting how real-time sensing, cognitive processing, and responsive actuation collectively enhance system performance in complex scenarios. It examines key challenges in perception-control loops, including sensor noise, latency, and environmental variability, and presents innovative solutions such as hybrid sensing architectures, machine learning-based adaptive control, and edge computing for low-latency processing. Through case studies in industrial robotics, autonomous navigation, and smart manufacturing, the paper demonstrates the practical impact of perception-driven control on efficiency, accuracy, and robustness. By synthesizing theoretical advancements and real-world applications, this work contributes to the growing body of knowledge at the intersection of perception and control, offering insights for future research in intelligent automation.