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We introduce a new technique for coordinating teams of unmanned aerial vehicles (UAVs) when deployed to collect live aerial imagery of the scene of a disaster. We define this problem as one of task assignment where the UAVs dynamically coordinate over tasks representing the imagery collection requests. To measure the quality of the assignment of one or more UAVs to a task, we propose a novel utility function which encompasses several constraints, such as the task's importance and the UAVs' battery capacity so as to maximise performance. We then solve the resulting optimisation problem using a fully asynchronous and decentralised implementation of the max-sum algorithm, a well known message passing algorithm previously used only in simulated domains. Finally, we evaluate our approach both in simulation and on real hardware. First, we empirically evaluate our utility and show that it yields a better trade off between the quantity and quality of completed tasks than similar utilities that do not take all the constraints into account. Second, we deploy it on two hexacopters and assess its practical viability in the real world.

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