Calibration of industry robots with consideration of loading effects using Product-Of-Exponential (POE) and Gaussian Process (GP)

Robot calibration is critical for industrial robot applications that require high accuracy. This paper presents a novel calibration method that utilizes Product-Of-Exponential (POE) and Gaussian Process (GP) regression to compensate for both geometric and non-geometric errors within the robot manipulator. Effects of a payload at the end-effector is also considered in the GP regression model in order to further improve robot positioning accuracy in the task space. Simulation and experimental results demonstrate the effectiveness of the proposed method. The experimental results show that the proposed method reduces norm pose error by 65.5% and 50.2% on average compared to conventional base-tool calibration and POE calibration respectively.

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