Hidden Markov Model of Force Torque Information in Telemanipulation

Hannaford, B. and Lee, P. (1991) Hidden Markov Model of Force Torque Information in Telemanipulation. International Journal of Robotics Research, 10 (5). pp. 528-539.

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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.

Item Type: Article
Subjects: B Teleoperation > B Teleoperation (General)
B Teleoperation > BB Sensors for Teleoperation
Divisions: Department of Electrical Engineering
Depositing User: Blake Hannaford
Date Deposited: 27 Apr 2016 22:45
Last Modified: 27 Apr 2016 22:45
URI: http://brl.ee.washington.edu/eprints/id/eprint/288

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