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The International Journal of Robotics Research
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Contact State Estimation Using Multiple Model Estimation and Hidden Markov Models

Thomas J. Debus

Department of Aerospace and Mechanical Engineering Boston University Boston, MA 02445, USA

Pierre E. Dupont

Department of Aerospace and Mechanical Engineering Boston University Boston, MA 02445, USA

Robert D. Howe

Division of Engineering and Applied Sciences Harvard University Cambridge, MA 02138, USA

In this paper we present an approach to estimating the contact state between a robot and its environment during task execution. Contact states are modeled by constraint equations parametrized by timedependent sensor data and time-independent object properties. At each sampling time, multiple model estimation is used to assess the most likely contact state. The assessment is performed by a hidden Markov model, which combines a measure of how well each set of constraint equations fits the sensor data with the probability of specific contact state transitions. The latter is embodied in a task-based contact state network. The approach is illustrated for a three-dimensional peg-in-hole insertion using a tabletop manipulator robot. Using only position sensing, the contact state sequence is successfully estimated without knowledge of nominal property values. Property estimates are obtained for the peg dimensions as well as the hole position and orientation.

Key Words: contact estimation • contact modeling • hidden Markov model • machine perception • multiple model estimation • task network

The International Journal of Robotics Research, Vol. 23, No. 4-5, 399-413 (2004)
DOI: 10.1177/0278364904042195
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J. R. Chen
Constructing Task-Level Assembly Strategies in Robot Programming by Demonstration
The International Journal of Robotics Research, December 1, 2005; 24(12): 1073 - 1085.
[Abstract] [PDF]