Classification of assembly operations using machine learning algorithms based on visual sensor data
Patrick Rückert, Björn Papenberg, Kirsten Tracht
Abstract
Image processing enables the acquisition and transfer of data from manual assembly work spaces into a digital environment. The following paper addresses the question to what extent processes of manual assembly can be reliably derived from visual sensor data and classified by machine learning algorithm. Complex assembly processes are transformed into short, discrete assembly operations which are detected by 3D-image processing and processed by a multilayer neuronal network. In order to differentiate assembly operations based on sensor data, methods of machine learning algorithms are implemented and further developed. A recurrent neural network algorithm is investigated regarding its applicability in combination with the sensor data and the robustness in image recognition.