This paper presents an approach to estimating the contact state between a robot and its environment during task execution. Contact states are modeled by constraint equations parameterized by time-dependent 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 fit 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. Property estimates are obtained for the peg dimensions as well as the hole position and orientation.
Contact State Estimation using Multiple Model Estimation and Hidden Markov Models
2003-06-30
10 pages
Article/Chapter (Book)
Electronic Resource
English
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