Abstract:The consequence that the robot end’s six-dimensional force/torque sensor affected by the contact vibration of the grinding wheel causes the force measurement signal to be submerged by noise in robot grinding, so that real-time and effective extraction of the signal is the key to control force. From the characteristics of the force measurement signal after fast Fourier transform, its noise is a complex noise formed by the superposition of Gaussian white noise, vibration noise and colored noise. Double Kalman Filter(DKF) algorithm based on series structure was proposed. Including: 1)The Gaussian white noise and high-frequency vibration peak groups were filtered by the first Kalman Filter(KF) algorithm; 2) To analyze the characteristics of colored noise formed by the superimposition of unfiltered Gaussian white noise and low-frequency vibration noise, a single parameter was introduced to design the time-varying variance to describe them by exponential weighting, so that KF was improved as the second filtering algorithm;3) Two filters formed double Kalman filter in series. Taking the contact grinding process between eyeglass frame and grinding wheel as an example, the experimental results showed that the proposed algorithm is more effective than traditional KF algorithm in suppressing noise in force measurement signal, and has low computational complexity and strong practicability.
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