Scheduling for Humans and Team Size Issues in Multirobot Supervisory Control

Abstract

This body of research describes efficient utilization of human time by two means: prioritization of human tasks and maximizing multirobot team size. An efficient scheduling algorithm for multirobot supervisory control (dSSPT) is proposed which schedules tasks such that a mission is completed faster. This algorithm is superior to existing algorithms by prioritizing human tasks such that robots can regain autonomous control sooner. In simulations of a single resource (e.g. human) scheduling problem, it is found that downtime is lower for dSSPT and the rate of human task completion is faster compared to other standard or similar algorithms. In simulations of a multirobot area surveying problem, we show that the rate of area coverage is much higher using dSSPT compared to first-in-first-out(FIFO). This work also looks at maximum multirobot team size with the notion that handling more robots on the team can potentially mean more work gets done by the robots without increasing human time. The factors that affect team size are examined mathematically and verified through simulation. It is found that variance in interaction time between humans and robots, variance in neglect time during which a robot is autonomous, and the use of different scheduling algorithms can all 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, it turns out that the upper bound on team size predicted by the fan-out equation 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)