AI-Enabled Automation Framework for Next-Gen Robotic Control Systems

Abstract

As automation becomes central to modern manufacturing and service operations, the need for intelligent robotic control systems has become more pressing than ever. Traditional control algorithms, while stable, often fail to adapt efficiently to dynamic uncertainties such as changing payloads, unforeseen obstacles, and multi-agent interactions. This paper proposes an AI-enabled automation framework integrating reinforcement learning, adaptive trajectory planning, and context-aware decision-making within robotic control architectures. By embedding learning-driven modules, robots can self-adjust motion parameters, predict environmental variations, and execute precise control actions autonomously. A multi-layer control strategy combines high-level AI planning with low-level feedback control loops to maintain stability during learning. Simulation and real-world experiments reveal notable gains in tracking accuracy, energy efficiency, and task completion time. The proposed framework represents a transformative step toward future robotic systems capable of self-optimization, collaborative intelligence, and high-autonomy operation.

KEYWORDS: Robotic Control, Reinforcement Learning, Autonomous Systems, Intelligent Automation, Adaptive Trajectory Planning