Weighted Multi-Sensor Data Fusion Based on Fuzzy Kalman Filter for Seam Tracking of the Welding Robots

Abstract:

In order to resolve the problem of seam tracking of the welding robots with unknown noise characteristics, a Weighted Multi-Sensor Data Fusion (MSDF) algorithm based on the fuzzy Kalman filter algorithm is proposed. Firstly, each Fuzzy Kalman Filter (FKF) uses a fuzzy inference system based on a covariance matching technique to adjust the weight coefficient of measurement noise covariance matrix, so it makes measurement noise close to the true noise level. Secondly, a membership function in fuzzy set is used to measure the mutual support degree matrix of each FKF and corresponding weight coefficients are allocated by this matrix’s maximum modulus eigenvectors, hence, the final expression of data fusion is obtained. Finally, simulation results show that MSDF in seam tracking has both high precision and strong ability of stableness.

• Periodical: Advanced Materials Research (Volumes 542-543)
• Pages: 800-805
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.542-543.800
• Online since: June 2012
• Authors: Jun Du, Mei Sun, Liang Hua, Jia Sheng Ge, Ju Ping Gu
• Keywords: Fuzzy Kalman Filter, Fuzzy Logic, Multi-Sensor Data Fusion, Seam Tracking, Welding Robot

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