Multi-sensor Fusion for High-Precision Robort Arm in Intelligent Manufacturing: A Review

Collaborative robots have seen growing integration into intelligent manufacturing, yet precision challenges in high-demand fields like semiconductor packaging and logistics sortingstemming from mechanical errors, sensor noise, and control limitationsremain critical. This paper reviews multi-sensor fusion strategies for high-precision robotic arms, focusing on integrating joint encoders, laser trackers, 3D vision systems, and FBG sensors. A hierarchical fusion framework is highlighted, combining data-level calibration (e.g., laser trackers correcting encoder errors), feature-level environment-task mapping (via 3D vision), and decision-level structural health management (using FBG sensors). Case studies demonstrate sub-micron accuracy (0.5m) in semiconductor applications by eliminating transmission chain errors and 0.1mm precision in dynamic logistics sorting through vision-inertial coordination. However, barriers like high hardware costs (core sensors comprising >60% of system costs), multi-data synchronization complexities, and limited upgrade incentives in traditional manufacturing are identified. The study underscores the strategys potential to meet Industry 4.0 demands for customization and automation, offering a foundational guide for advancing autonomous robotic systems toward smarter, more reliable precision manufacturing solutions via AI-driven models and cost-effective sensor innovations.