AI-Driven Intelligent Control Strategies for Industrial Robotics: A Reinforcement Learning Approach

Abstract

This study proposes an AI-driven adaptive control strategy to enhance the learning, adaptability, and autonomous performance of robotic ‎manipulators in dynamic and unstructured industrial environments. Moving beyond the limitations of conventional model-based controllers, ‎the research introduces a self-learning framework that integrates real-time sensor data from LiDAR and stereo vision cameras. This data ‎continuously informs and optimizes the robot’s motion trajectories in both simulated and real-world tasks. The system’s core innovation lies ‎in combining Reinforcement Learning (RL) with Deep Neural Networks (DNNs) for adaptive trajectory planning and error compensation. ‎Specifically, the Proximal Policy Optimization (PPO) algorithm is employed to fine-tune control strategies based on real-time sensory ‎feedback, allowing the robotic system to autonomously adapt to variations in object positions and unexpected disturbances. An Edge-AI ‎module is embedded into the architecture to enhance decision-making speed and reduce latency during task execution. Experimental ‎validation, including scenarios like arc welding and sealant dispensing, shows the proposed system outperforms traditional PID-based ‎adaptive controllers. The AI-driven solution demonstrated improved precision, faster convergence, and superior adaptability under complex ‎and fluctuating manufacturing conditions. The study also opens pathways for future integration of hybrid AI techniques—such as fuzzy ‎logic and genetic algorithms—for even more intelligent and responsive robotic systems‎.