Skip to Main Content
In many applications, Unmanned Aerial Vehicles (UAVs) provide an indispensable platform for gathering information about the situation on the ground. However, to maximise information gained about the environment, such platforms require increased autonomy to coordinate the actions of multiple UAVs. This has led to the development of flight planning and coordination algorithms designed to maximise information gain during sensing missions. However, these have so far neglected the need to maintain wireless network connectivity. In this paper, we address this limitation by enhancing an existing multi-UAV planning algorithm with two new features that together make a significant contribution to the state-of-the-art: (1) we incorporate an on-line learning procedure that enables UAVs to adapt to the radio propagation characteristics of their environment, and (2) we integrate flight path and network routing decisions, so that modelling uncertainty and the affect of UAV position on network performance is taken into account.