| |
|
|
|
|
|
|
|
|
|
|
|
| |
|
|
|
[052] Citation: Abstract
A new model is developed for prediction and analysis of sensor information
recorded during robotic performance of tasks by telemanipulation. The model uses
the Hidden Markov Model (stochastic functions of Markov nets: HMM) to describe
the task structure, the operator or intelligent controller's goal structure, and
the sensor signals such as forces and torques arising from interaction with the
environment. The Markov process portion encodes the task sequence/subgoal
structure, and the observation densities associated with each subgoal state encode
the expected sensor signals associated with carrying out that subgoal.
Methodology is described for construction of the model parameters based on
engineering knowledge of the task. The Viterbi algorithm is used for model based
analysis of force signals measured during experimental teleoperation and achieves
excellent segmentation of the data into subgoal phases. The Baum-Welch algorithm
is used to identify the most likely HMM from a given experiment. The HMM
achieves a structured knowledge-based model with explicit uncertainties and
mature, optimal identification algorithms.
["I would like a hard copy of this report"]
[Copyright]
[HELP!]
Updated: Tue Aug 19 09:16:07 2008
| |
