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[051] Citation: Abstract
Recent work in real-time distributed computation and control has culminated in a
prototype force-reflecting telemanipulation system having dissimilar master
(cable-driven force-reflecting hand controller) and slave (PUMA 560 robot with
customer controller), extremely high sampling rate (1000 Hz) and low loop
computation delay (5 ms). In a series of experiments with this system and five
trained test operators covering more than 100 h of teleoperation, performance in a
series of generic and application-driven tasks with and without force feedback was
measured, and with control shared between teleoperation and local sensor
referenced control. Measurements defining task performance include 100-Hz
recording of six-axis force-torque information, task completion time, and visual
observation of predefined task errors. The tasks consisted of high precision peg-
i 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.
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Updated: Tue Jul 15 23:54:48 2008
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