Real-Time Embedded AI for Industrial Robot Monitoring

Abstract

The rapid advancement of Industry 4.0 and intelligent manufacturing has significantly increased the adoption of industrial robots in automated production environments. Modern robotic systems require continuous monitoring to ensure operational efficiency, safety, reliability, and predictive maintenance. Conventional monitoring systems primarily rely on centralized cloud processing and threshold-based diagnostics, which often introduce communication latency, increased bandwidth consumption, and limited responsiveness during critical operational events. Real-time embedded artificial intelligence (AI) has emerged as an effective solution by enabling intelligent data processing directly at the edge using embedded processors integrated within robotic platforms. This study presents a comprehensive research framework for real-time embedded AI-based industrial robot monitoring that combines edge computing, embedded deep learning, multi-sensor fusion, anomaly detection, predictive maintenance, and intelligent decision-making. The proposed architecture integrates vision sensors, vibration sensors, temperature sensors, current sensors, and inertial measurement units with embedded AI accelerators to perform low-latency inference and continuous robot health assessment. A comparative analysis demonstrates that embedded AI significantly improves monitoring accuracy, reduces response time, minimizes communication overhead, and enhances predictive maintenance capability compared with conventional cloud-based monitoring systems. Furthermore, the study discusses research gaps, implementation challenges, and future opportunities involving federated learning, digital twins, explainable AI, and collaborative industrial robotics. The proposed framework provides a scalable, energy-efficient, and intelligent monitoring solution suitable for next-generation smart factories and Industry 5.0 environments.