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Advisor(s)
Abstract(s)
In this paper, we consider the use of a team of multiple unmanned aerial vehicles (UAVs) to
accomplish a search and rescue (SAR) mission in the minimum time possible while saving the maximum
number of people. A novel technique for the SAR problem is proposed and referred to as the layered search
and rescue (LSAR) algorithm. The novelty of LSAR involves simulating real disasters to distribute SAR
tasks among UAVs. The performance of LSAR is compared, in terms of percentage of rescued survivors
and rescue and execution times, with the max-sum, auction-based, and locust-inspired approaches for multi
UAV task allocation (LIAM) and opportunistic task allocation (OTA) schemes. The simulation results show
that the UAVs running the LSAR algorithm on average rescue approximately 74% of the survivors, which
is 8% higher than the next best algorithm (LIAM). Moreover, this percentage increases with the number
of UAVs, almost linearly with the least slope, which means more scalability and coverage is obtained
in comparison to other algorithms. In addition, the empirical cumulative distribution function of LSAR
results shows that the percentages of rescued survivors clustered around the [78% 100%] range under an
exponential curve, meaning most results are above 50%. In comparison, all the other algorithms have almost
equal distributions of their percentage of rescued survivor results. Furthermore, because the LSAR algorithm
focuses on the center of the disaster, it nds more survivors and rescues them faster than the other algorithms,
with an average of 55% 77%. Moreover, most registered times to rescue survivors by LSAR are bounded
by a time of 04:50:02 with 95% con dence for a one-month mission time.
Description
Keywords
Autonomous agents Drones Search and rescue Unmanned aerial vehicles