A New Hybrid Calibration Method for Robot Manipulators by Combining Model–Based Identification Technique and a Radial Basis Function–Based Error Compensation

Abstract

Though the kinematic parameters had been well identified, there are still existing some non-negligible non-geometric error sources such as friction, gear backlash, gear transmission, temperature variation etc. They need to be eliminated to further improve the accuracy of the robotic system. In this paper, a new hybrid calibration method for improving the absolute positioning accuracy of robot manipulators is proposed. The geometric errors and joint deflection errors are simultaneously calibrated by robot model identification technique and a radial basis function neural network is applied for compensating the robot positions errors, which are caused by the non-geometric error sources. A real implementation was performed with Hyundai HH800 robot and a laser tracker to demonstrate the effectiveness of the proposed method.