Density based clustering for detection of robotic operations

This paper tackles the problem of processing measured values in time series of energy consumption data obtained in robotic production cells. The consumed energy is measured at each robot in the cell to get information about the robotic operations that are performed. Such knowledge may serve as a basis for further steps such as minimization of the energy consumption or diagnosis of the robot behavior. For the modeling of the robots, Continuous State Hidden Gaussian-Markov Models (CS-HGMM) were developed in the previous work, which rely on a set of training examples of sequences for unsupervised training. In this paper, segmentation based on signal information contents and unsupervised clustering of the acquired segments is presented. The used clustering methods have been adapted from the OPTICS algorithm, which is a generalization of the popular DBSCAN algorithm. This approach has resulted in the ability to process irregular artefacts in measured data that do not represent any particular robotic operation, and to process and cluster segment candidates that do not have the same length which happens quite often in the industrial applications.