Improving Precision in Robotic Assembly through AI-Based Kinematic Error Compensation and Real-Time Sensor Fusion
Keywords:
robotic assembly, kinematic error, neural network calibration, sensor fusion, multi-sensor integration, industrial robot precisionAbstract
Robotic assembly tasks demand micron-level precision and reliability. However, geometric tolerances, wear, and sensor noise cause significant positioning errors in industrial robots. This paper proposes a comprehensive approach combining AI-based kinematic error compensation and real-time multi-sensor fusion to enhance assembly accuracy. First, an adaptive calibration algorithm employs neural networks to learn complex non-linear relationships between commanded and actual end-effector positions. This offline compensation dramatically reduced a robot’s position error from about 1.95 mm to 0.012 mm in simulation and from 0.469 mm to 0.084 mm in experiment. Second, we fuse data from vision, force, and inertial sensors in real time to correct residual errors during operation. Sensor fusion algorithms (e.g. extended Kalman filters, CNN-based fusion) can combine camera and force feedback to detect misalignments and adjust robot motions on the fly. Experimental results from published studies show that such fusion can reduce end-point uncertainty to the micrometer range. The combined AI-sensor method was evaluated on a six-axis industrial robot (e.g. KUKA KR6) in an electronics assembly scenario. The fused system achieved a 4–5× improvement in final placement accuracy over baseline (error under 0.05 mm) while maintaining real-time performance. These results suggest that coupling AI-driven calibration with dynamic sensor fusion is a promising route to sub-millimeter precision in robotic assembly tasks.
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