Embedded vision equipment of industrial robot for inline detection of product errors by clustering–classification algorithms. (11th October 2016)

• Record Type: Journal Article
• Authors: Zidek, Kamil; Maxim, Vladislav; Pitel, Jan; Hosovsky, Alexander
• Is Part Of: International journal of advanced robotic systems. Volume 13:Number 5(2016)
• Publication Date: 2016-10-11
• Subjects: error diagnostics -- image patterns -- clustering -- machine teaching -- prediction; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881416664901
• Languages: English
• ISSNs: 1729-8806

Abstract

The article deals with the design of embedded vision equipment of industrial robots for inline diagnosis of product error during manipulation process. The vision equipment can be attached to the end effector of robots or manipulators, and it provides an image snapshot of part surface before grasp, searches for error during manipulation, and separates products with error from the next operation of manufacturing. The new approach is a methodology based on machine teaching for the automated identification, localization, and diagnosis of systematic errors in products of high-volume production. To achieve this, we used two main data mining algorithms: clustering for accumulation of similar errors and classification methods for the prediction of any new error to proposed class. The presented methodology consists of three separate processing levels: image acquisition for fail parameterization, data clustering for categorizing errors to separate classes, and new pattern prediction with a proposed class model. We choose main representatives of clustering algorithms, for example, K-mean from quantization of vectors, fast library for approximate nearest neighbor from hierarchical clustering, and density-based spatial clustering of applications with noise from algorithm based on the density of the data. For machine learning, we selected six major algorithms of classification: support vector machines, normal Bayesian classifier, K-nearest neighbor, gradient boosted trees, random trees, and neural networks. The selected algorithms were compared for speed and reliability and tested on two platforms: desktop-based computer system and embedded system based on System on Chip (SoC) with vision equipment.