Identifying predictive metrics for supervisory control of multiple robots
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: 1) predict the number of robots that should be in the team and 2) predict system effectiveness. We do so by comparing predictions with actual data from a user study, which is also described. © 2007 IEEE.
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- Industrial Engineering & Automation
- 4007 Control engineering, mechatronics and robotics
- 0913 Mechanical Engineering
- 0906 Electrical and Electronic Engineering
- 0801 Artificial Intelligence and Image Processing
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Industrial Engineering & Automation
- 4007 Control engineering, mechatronics and robotics
- 0913 Mechanical Engineering
- 0906 Electrical and Electronic Engineering
- 0801 Artificial Intelligence and Image Processing