Enhanced Kalman filter methods for end pose measurement of parallel kinematic machine considering the error sensitivity

Abstract

For robust accuracy, this paper proposes a parallel measurement method for parallel kinematic machine (PKM) combining indirect measurement using grating ruler with direct measurement using monocular vision. However, due to differing error sensitivity of two methods, the general Kalman filter (KF) cannot integrate them under the assumption of equal data weighting. Therefore, a novel Enhanced KF data fusion method designed to account for varying data sensitivities is developed.The paper first outlines the principles of parallel measurement approach for PKMs, followed by modeling the error sensitivities associated with each measurement source. Subsequently, an enhanced KF data fusion model is developed, treating these sensitivities as noise, and validated. Compared to the conventional KF method, the root mean squared (RMS) position error is significantly reduced from 0.569 mm to 0.293 mm. This enhanced KF method presents an innovative approach to multi-source data fusion, tailored to the error characteristics of different measurement sources.