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This paper introduces an AI-driven adaptive control strategy for industrial robots using reinforcement learning to improve adaptability and autonomous performance in dynamic environments. They combine real-time sensor data (LiDAR, stereo vision) with Proximal Policy Optimization (PPO) and Deep Neural Networks (DNNs) for adaptive trajectory planning and error compensation. Experimental results in arc welding and sealant dispensing demonstrate that the proposed system outperforms traditional PID-based adaptive controllers in precision, convergence speed, and adaptability.
Forget PID controllers: PPO-based RL lets robots adapt to dynamic industrial environments with greater precision and speed.
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.
