A contact state recognition method for robot assembly based on gwa-svm

Abstract

A contact state recognition method for robot assembly based on GWA‑SVM, comprising the following steps: Step 1: use industrial robots to assemble parts, and collect force data during the assembly process; Step 2: set initial parameters; Step 3: convert the data set Carry out standardization processing; Step 4: Initialize the population of SVM parameters using the chaotic logic mapping strategy; Step 5: Use the improved reverse learning strategy to optimize the population of SVM parameters; Step 6: Use the GWA operator to update the population; Step 7: Calculate the individual population , and update the optimal individual; Step 8: If the current iteration reaches the maximum allowable number of iterations, then execute Step 9; otherwise, t=t+1 and return to Step 6; Step 9: End the SVM parameter optimization process, and the most The optimal SVM parameters C and γ and the training data set are substituted into the SVM to establish a contact state recognition model based on GWA-SVM; Step 10: Use the contact state model to identify the test data set and draw the classification result map. The classification accuracy of the present invention is higher.

Description

technical field [0001] The invention belongs to the technical field of machine learning and robot control, and is applicable to the field of contact state identification of parts assembled by industrial robots. Specifically, it relates to a contact state recognition method based on a global optimal whale algorithm (G-best Whale Algorithm, hereinafter referred to as GWA) and a support vector machine (Support Vector Machine, hereinafter referred to as SVM). Background technique [0002] Industrial robots are the core equipment of flexible automation. In the application of production, industrial robots can improve labor productivity, improve product quality, improve working conditions, improve the competitiveness and adaptability of enterprises, promote the establishment and development of new industries, change the labor structure, and promote technological progress in related disciplines. All played a significant social and economic benefits. When the end of the robot arm h...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the shortcomings of the existing classification methods for the low classification accuracy of the assembly force data of industrial robot parts, the present invention provides a contact state recognition method for robot assembly based on GWA-SVM with high classification accuracy

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