Abstract & Overview
Robotic positioning is a cornerstone of high-precision automation, yet conventional techniques often struggle with environmental variability, sensor drift, and dynamic real-time demands. This review critically analyses the evolving integration of Artificial Intelligence (AI) and metrology in robotic positioning measurement systems. It identifies the limitations of traditional sensor modalities, including optical encoders, inertial units, LiDAR, and GPS, while emphasising the importance of metrology in achieving traceable accuracy and compliance with standards. This paper focuses on systems that integrate physics-based metrology with AI-driven algorithms to support dynamic calibration, traceability, and autonomous error correction. Key AI advancements such as deep learning for vision localisation, reinforcement learning for dynamic control, and sensor fusion for adaptive error mitigation are highlighted. These hybrid systems synergise deterministic precision with learning-based adaptability, providing a promising future for robotic accuracy. Key performance benchmarks, error metrics (e.g., RMSE, MAE), and international standards (ISO 9283, ISO 10360) are analysed to assess real-world applicability. Finally, the study identifies emerging trends, such as blockchain-enabled traceability, Explainable AI (XAI), and quantum-enhanced inference. The convergence of AI and metrology is shown to redefine robotic positioning, advancing toward self-calibrating, regulation-compliant systems with high accuracy and resilience.