Enhanced Noncontact R-Test Sensor System for Spatial Errors Calibration in Industrial Robots Under Variable Load Conditions

The spatial error obtained is a crucial process for industrial robot accuracy calibration. However, the existing spatial error sensor systems are often limited by inflexibility, poor adaptability to varying conditions, or substantial cost. This study proposes a multisensor system for calibrating tool center point (TCP) of the robot, comprising a novel noncontact sensor system based on R-test, and an automated rotary device equipped with several standard spheres across different ranges. The geometric accuracy of the proposed system was validated by coordinate measuring machine (CMM). Spatial error measurement was established based on the geometric modeling and error vector mapping, followed by a novel error identification and compensation algorithm incorporating the Least Squares Method, Cartesian stiffness identification, and Ridge Estimation. A simulation was conducted to demonstrate the capability of the proposed model to identify and correct spatial errors down to the micrometer level. The calibration experiment was conducted on the EPSON C4-A901 industrial robot. The results showed that the proposed method reduced the average error modulus by 36.94%–43.86%, and the maximum error modulus by 35.90%–20.34% as the rotation radius increased from 50 to 100 mm. In addition, a laser tracker was employed for validation, and the spatial errors measured by both systems exhibited a similar trend and deviation. The calibration results obtained using the laser tracker showed a 49 % reduction in average error. This result substantiates the reliability of the proposed calibration algorithm and verifies the accuracy of the measurement system.