Hand-Eye Calibration in Robotic Machining: A Review of Challenges, Solutions, and Applications

As intelligent manufacturing demands greater precision, flexibility, and consistency, robotic machining, characterized by structural compliance, intelligent perception, and cost efficiency, provides an effective solution for machining complex components. However, limited structural stiffness and accumulated multi-source errors undermine the geometric consistency required for perception-guided robotic machining, particularly in flexible machining and in-situ manufacturing of complex surfaces and large-scale structures. Thus, hand-eye calibration (HEC) becomes a critical determinant of robotic machining performance. Serving as the foundational mechanism enabling perception-modeling-machining integration, HEC governs the geometric consistency, trajectory reliability, and long-term maintainability of robotic machining systems. In this paper, a systematic review of the research status and development trends of HEC in robotic machining is presented, organized around key challenges, technical solutions, and representative applications. The key challenges arising from multi-source error coupling, observation degradation, parameter drift, and environmental interference are first elucidated through the lenses of reliability of accuracy, algorithmic robustness, and engineering adaptability. Second, core technical solutions, including calibration methods, algorithmic optimization, adaptive error compensation, and system integration, are synthesized. Their evolution from offline geometric solutions to online adaptive schemes, as well as from single-modality sensing to multi-sensor fusion, is further traced. Finally, through two representative cases, including robotic belt grinding of turbine blades and putty-coating defect repair for high-speed train bodies, the application patterns of HEC in both small-scale high-accuracy machining and large-scale complex-structure refurbishment are demonstrated. Future directions toward more intelligent and efficient HEC for robotic machining are outlined.