“Hidden Markov Models as a Process Monitor in Robotic Assembly”

Authors: Geir Hovland and Brenan J. McCarragher,
Affiliation: ABB and Australian National University
Reference: 1999, Vol 20, No 4, pp. 201-223.

Keywords: Discrete event systems, sensory perception, robotic assembly

Abstract: A process monitor for robotic assembly based on hidden Markov models (HMMs) is presented. The HMM process monitor is based on the dynamic force/torque signals arising from interaction between the workpiece and the environment. The HMMs represent a stochastic, knowledge-based system in which the models are trained off-line with the Baum-Welch reestimation algorithm. The assembly task is modeled as a discrete event dynamic system in which a discrete event is defined as a change in contact state between the workpiece and the environment. Our method (1) allows for dynamic motions of the workpiece, (2) accounts for sensor noise and friction, and (3) exploits the fact that the amount of force information is large when there is a sudden change of discrete state in robotic assembly. After the HMMs have been trained, the authors use them on-line in a 2D experimental setup to recognize discrete events as they occur. Successful event recognition with an accuracy as high as 97with a training set size of only 20 examples for each discrete event.

PDF PDF (3465 Kb)        DOI: 10.4173/mic.1999.4.2

DOI forward links to this article:
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  title={{Hidden Markov Models as a Process Monitor in Robotic Assembly}},
  author={Hovland, Geir and McCarragher, Brenan J.},
  journal={Modeling, Identification and Control},
  publisher={Norwegian Society of Automatic Control}