Abstract
Task allocation plays a pivotal role in the optimisation of multi-unmanned aerial vehicle (multi-UAV) search and rescue (SAR) missions in which the search time is critical and communication infrastructure is unavailable. These two issues are addressed by the proposed BMUTA algorithm, a bee-inspired algorithm for autonomous task allocation in multi-UAV SAR missions. In BMUTA, UAVs dynamically change their roles to adapt to changing SAR mission parameters and situations by mimicking the behaviour of honeybees foraging for nectar. Four task allocation heuristics (auction-based, max-sum, ant colony optimisation, and opportunistic task allocation) were thoroughly tested in simulated SAR mission scenarios to comparatively assess their performances relative to that of BMUTA. The experimental results demonstrate the ability of BMUTA to achieve a superior number of rescued victims with much shorter rescue times and runtime intervals. The proposed approach demonstrates a high level of flexibility based on its situational awareness, high autonomy, and economic communication scheme.
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