2021· International Journal of Intelligent Automation & Robotics Engineering· Vol 4, pp. 01-16· 0 citations
TL;DR
The proposed autonomous calibration framework integrates multi-sensor fusion, machine vision, laser measurement, inertial sensing, machine learning, and adaptive optimization to continuously estimate and compensate for calibration errors in real time to enable self-learning, real-time robotic calibration that supports intelligent, efficient, and sustainable smart manufacturing.
Abstract
Industry 4.0 technologies, including AI, IIoT, robotics, and cyber-physical systems, require industrial robots to maintain high positioning accuracy despite thermal, mechanical, and operational changes. Conventional offline calibration is time-consuming, costly, and unsuitable for dynamic manufacturing environments. The proposed autonomous calibration framework integrates multi-sensor fusion, machine vision, laser measurement, inertial sensing, machine learning, and adaptive optimization to continuously estimate and compensate for calibration errors in real time. By updating robot kinematic models during operation, the system improves positioning accuracy, repeatability, manufacturing quality, equipment utilization, and predictive maintenance while minimizing downtime and human intervention. Overall, the framework enables self-learning, real-time robotic calibration that supports intelligent, efficient, and sustainable smart manufacturing.
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