Intelligence artificielle

Artificial intelligence is reshaping mechatronics and robotics by enabling machines to perceive unstructured environments, learn from data, reason about uncertainty, and make decisions in real time. This category delves into machine learning, deep learning, computer vision, natural language processing, reinforcement learning, generative models, evolutionary algorithms, and neural network deployment on embedded, edge, and cloud platforms. We cover practical topics including dataset preparation, data augmentation, feature engineering, model architecture selection, training pipelines, hyperparameter tuning, regularization, quantization, pruning, knowledge distillation, inference optimization, and integration with ROS or PLC-based control systems. You will also find discussions on AI ethics, explainability, robustness, data privacy, bias mitigation, and safety-critical validation for robotic and autonomous applications. From perception stacks and predictive maintenance algorithms to generative design, digital twins, and human-robot collaboration, our articles explain how AI augments traditional control theory. Whether you are implementing a vision-based inspection system, tuning a reinforcement learning policy for robot navigation, or exploring large language models for human-machine interfaces, this collection offers the engineering depth needed to turn AI research into reliable mechatronic products. Bookmark this hub to keep pace with the algorithms, tools, and deployment strategies that are reshaping intelligent machines. Each article translates academic advances into engineering workflows you can apply to perception, planning, and decision-making subsystems.