Data Denosing Processing Of The Operating State Of The Robotic Arm Of Coal Sampling Robot

The operating state information of the robot reflects the working state of the robot. Analysis of this information is an important means to understand the working state and failure of the robot. Taking the coal sampling robot as the research object, in order to extract the effective running state data of the robot under the condition of serious noise pollution. This paper focuses on the research on the denoising method of the running state data of the robot's mechanical arm. The limitations of empirical mode decomposition (EMD) based denoising method and threshold method based wavelet denoising method are analyzed and an improved threshold wavelet denoising algorithm based on EMD is proposed. Finally, the effect of denoising algorithm is measured by signal-to-noise ratio (SNR) and root mean square error (RMSE). It is verified that the denoising algorithm has adaptability to the typical signal of the robotic arm state of coal sampling robot.