Performance evaluation of a six-axis generalized force-reflecting teleoperator

Hannaford, B. and Wood, Laurie and McAffee, Douglas and Zak, Haya (1991) Performance evaluation of a six-axis generalized force-reflecting teleoperator. Systems, Man and Cybernetics, IEEE Transactions on, 21 (3). pp. 620-633.

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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.

Item Type: Article
Subjects: B Teleoperation > B Teleoperation (General)
Divisions: Department of Electrical Engineering
Depositing User: Blake Hannaford
Date Deposited: 04 Nov 2015 23:23
Last Modified: 04 Nov 2015 23:23
URI: http://brl.ee.washington.edu/eprints/id/eprint/271

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