flexible part assembly process contact state identification method based on a Gaussian mixture model Bayesian algorithm

Abstract

A flexible part assembling process contact state recognition method based on a Gaussian mixture model Bayesian algorithm comprises the following steps that 1, collecting force data in multiple sets ofassembling processes and establishing a training data set and a test data set are e; 2, calculating the prior probability of training data; Step 3, initializing GMM parameters; 4, dividing the training data into categories closest to each other; 5, calculating the mean value of all training data in each category; 6, if t = T, executing the step 7, otherwise, returning to the step 4; 7, estimatingprobability density distribution of the training data; 8, calculating a posterior probability; Step 9, obtaining a new GMM parameter; 10, calculating a new log likelihood function, and if ln * p (x |Phi, u, sigma)-is greater than 1, calculating a new log likelihood function; If ln (x |Phi, u, sigma) (L, executing the step 11, otherwise, returning to the step 8, using the test data to calculate the Bayesian posterior probability, and classifying the data into the category with the maximum posterior probability.

Description

technical field [0001] The invention belongs to the technical field of machine learning and robot compliance control, and is applicable to the field of contact state recognition of flexible parts assembled by industrial robots. Specifically, it relates to a contact state identification method based on Gaussian Mixture Model (Gaussian MixtureModel, hereinafter referred to as GMM), maximum likelihood estimation (Expectation Maximization, hereinafter referred to as EM) and Bayesian classification algorithm. 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 e...

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 flexible parts, the present invention provides a contact state identification method for flexible parts assembly process based on Gaussian mixture model Bayesian algorithm with high classification accuracy

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