Decentralizing Intelligence: Transitioning Machine Learning from Cloud Infrastructures to Edge Microcontrollers in Mechatronic Systems

Abstract: The integration of artificial intelligence into physical hardware has traditionally relied on cloud computing infrastructure, introducing significant latency, bandwidth constraints, and power dependencies. This paper explores the architectural shift from cloud-dependent machine learning to Edge AI (TinyML). By deploying quantized machine learning models directly onto resource-constrained microcontrollers, mechatronic systems achieve real-time, autonomous decision-making capabilities. This paradigm shift is essential for the development of resilient physical infrastructure and decentralized hardware networks. Key Topics & Technologies: Hardware Architecture: Microcontrollers (ATmega2560, ESP32), Mechatronic Actuators, IoT Sensors. Software & ML Pipeline: Edge Impulse, TinyML, Model Quantization (8-bit integer scaling), C++ Library Deployment. Core Concepts: Decentralized physical infrastructure, autonomous fluid management, multimodal sensor fusion, localized offline intelligence.