State Transition Recognition in Robotic Assembly Using Hidden Markov Models

Abstract

A process monitor for robotic assembly based on Hidden Markov Models (HMMs) is presented. The measurements are the force/torque signals arising from interaction between the workpiece and the environment for a planar assembly task. The HMMs represent a stochastic knowledge-based system where the models are trained off-line with the Baum-Welch re-estimation algorithm. After the HMMs have been trained, we use them on-line in a robotic system to recognise events as they occur. Process monitoring with an accuracy of 98% was accomplished in 0.5-0.6s. 1 Introduction Process plants must deal with changing states, multiple faults, unexpected situations and unreliable measurements. To handle these problems real-time process monitoring is essential. Process monitoring is widely used as a component in many industrial processes. In robotic assembly, however, there is an increasing need for efficient process monitoring methods to account for existing uncertainties of workpieces and the environment..