Paint spraying robot voice recognition method based on multi-scale enhanced BiLSTM model

Abstract

The invention discloses a paint spraying robot voice recognition method based on a multi-scale enhanced BiLSTM model. The method comprises the following steps: 1) acquiring common spraying sound instructions by using a signal acquisition system, wherein NI-9234 is selected as a data acquisition card; 2) repeatedly adding Gaussian white noise to the collected audio signal for 100 times, generating a noisy signal, solving a corresponding Mel spectrum sequence, and then solving an average sequence of 100 Mel spectrum sequences; 3) performing feature extraction on the average Mel spectrum sequence by using a multi-scale convolution filter, and then performing further mining on the extracted features by using a BiLSTM model to obtain corresponding output; 4) splicing outputs of the BiLSTM model together, then inputting the spliced outputs to a full connection layer and a Softmax layer for processing, and finally realizing speech recognition in combination with a CTC algorithm. and 5) embedding the model obtained through training in the steps 1) to 4) into a spraying robot, so that corresponding spraying tasks are intelligently achieved. According to the model, the intelligent voice recognition function of the spraying robot can be realized, and the model has very high practical application value.

Description

technical field [0001] The invention relates to the field of intelligent painting robots, in particular to a speech recognition method for a painting robot based on a multi-scale enhanced BiLSTM model. Background technique [0002] With the rapid development of the domestic construction industry, the decoration industry, which is closely related to the construction industry, also has great market prospects; however, a large part of the work in the decoration industry needs to be done manually. Manual spraying has different spraying effects, and it is difficult to guarantee the construction quality and efficiency. [0003] Manual spraying is labor-intensive for workers, and the spraying distance and spraying speed are not easy to control. It is easy to cause excessive spraying thickness error and rework, or even fail to meet the quality requirements; the paint contains heavy metals, radioactive substances and toxic agricultural production system solvents. The paint needs to ...

AI Technical Summary

Problems solved by technology

The above patents are all scheduled tasks and then the spraying robot performs them, which is not self-adaptive. In reality, the spraying robot needs to make corresponding changes according to different situations, rather than performing tasks mechanically, and the speech recognition function of the spraying robot can be adaptive. Complete the corresponding spraying tasks, which has important practical significance

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