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Carnegie Mellon Robotics Institute

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Abstract

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 [5]. 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).

Notes

Associated Lab(s) / Group(s): Tele-Supervised Autonomous Robotics

Number of pages: 7

Text Reference

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.

BibTeX Reference

@inproceedings{Mau_2007_5894,
   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",
}

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