Identifying Predictive Metrics for Supervisory Control of Multiple Robots
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CrandallCummingsTRO2007.pdf
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Author(s)
Crandall, J. W.
Cummings, M. L.
Date Issued
2007
Publisher
IEEE Transactions on Robotics
Citation
Crandall, J.W., & Cummings, M.L., Identifying Predictive Metrics for Supervisory Control of Multiple Robots, IEEE Transactions on Robotics – Special Issue on Human-Robot Interaction , Vol. 23, issue 5, p. 942-951, 2007.
Abstract
In recent years, much research has focused on making possible single operator control of multiple robots. In these high workload situations, many questions arise including how many robots should be in the team, which autonomy levels should they employ, and when should these autonomy levels
change? To answer these questions, sets of metric classes should be identified that capture these aspects of the human-robot team. Such a set of metric classes should have three properties. First, it should contain the key performance parameters of the system. Second, it should identify the limitations of the agents in the system. Third, it should have predictive power. In this paper, we decompose a human-robot team consisting of a single human and multiple robots in an effort to identify such a set of metric classes.
We assess the ability of this set of metric classes to (a) predict the number of robots that should be in the team and (b) predict system effectiveness. We do so by comparing predictions with actual data from a user study, which is also described.
Subjects
Metrics
human-robot teams
supervisory control
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