Carnegie Mellon Robotics Institute
|This paper describes efficient utilization of human time by two means: prioritization of human tasks and maximizing multirobot team size. We propose an efficient scheduling algorithm for multirobot supervisory control that helps complete a mission faster. The proposed algorithm is superior to existing algorithms by prioritizing human tasks such that robots can regain autonomous control sooner. In simulations of a multirobot area surveying problem, we show that the rate of area coverage is much higher using our algorithm compared to first-in-first-out. We also show that the use of different scheduling algorithms can affect the maximum number of robots a human can manage on a team.
Another significant finding related to maximum team size is that the size is always the same or higher than an often-cited estimate known as fan-out . Since fan-out is derived from an ideal, average case, simulations show that the upper bound on team size is higher than that predicted by the fan-out equation. Fan-out is actually a lower bound on the maximum team size for any practical situation (i.e., where task lengths and periodicity may vary or when robots are heterogeneous).
Associated Lab(s) / Group(s):
Tele-Supervised Autonomous Robotics
Number of pages: 7
|Sandra Mau and John M. Dolan, "Scheduling for Humans in Multirobot Supervisory Control," Proceedings of the International Conference on Intelligent Robots and Systems (IROS '07), October, 2007, pp. 1637 - 1643.|
author = "Sandra Mau and John M Dolan",
title = "Scheduling for Humans in Multirobot Supervisory Control",
booktitle = "Proceedings of the International Conference on Intelligent Robots and Systems (IROS '07)",
pages = "1637 - 1643",
month = "October",
year = "2007",
|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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