Various roles for operators in human-machine systems have been proposed. This thesis shows that all of these views have in common the fact that operators perform best when given feedback that matches their intent. Past studies have shown that position control is superior to rate control except when operating large-workspace and/or dynamically slow manipulators and for exact tracking tasks. Operators of large-workspace and/or dynamically slow manipulators do not receive immediate position feedback. To remedy this lack of position feedback, a ghost arm overlay was displayed to operators of a dynamically slow manipulator, giving feedback that matches their intent. Operators performed several simple one- and two-dimensional tasks (point-to-point motion, tracking, path following) with three different controllers (position control with and without a ghost, rate control) to indicate how task conditions influence operator intent. Giving the operator position feedback via the ghost significantly increased performance with the position controller and made it comparable to performance with the rate control. These results were further validated by testing coordinated position control with and without a ghost arm and coordinated rate control on an excavator simulator. The results show that position control with the ghost arm is comparable, but not superior to rate control for the dynamics of our excavator example. Unlike previous work, this research compared the fuel efficiencies of different HMIs, as well as the time efficiencies. This work not only provides the design law of matching the feedback to the operator intent, but also gives a guideline for when to choose position or rate control based on the speed of the system.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/50152 |
| Date | 09 November 2012 |
| Creators | Elton, Mark David |
| Contributors | Book, Wayne |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
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