Hidden Markov Model Analysis of Force/ Torque Information in Telemanipulation

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) 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 / sub-goal structure, and the observation densities associated with each sub-goal state encode the expected sensor signals associated with carrying out that sub-goal. 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 sub-goal phases. The Hidden Markov Model achieves a structured, knowledge based model with explicit uncertainties and mature optimal identification algorithms.

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