  Single-task UAVs are increasingly being employed to carry out surveillance,
parcel delivery, communication support, and other specific tasks. When the
geographical area of operation of single-task missions is common, e.g., in
post-disaster recovery scenarios, it is more efficient to have multiple tasks
carried out as part of a single UAV mission. In these scenarios, the UAVs'
equipment and mission plan must be carefully selected to minimize the carried
load and overall resource consumption. In this paper, we investigate the joint
planning of multitask missions leveraging a fleet of UAVs equipped with a
standard set of accessories enabling heterogeneous tasks. To this end, an
optimization problem is formulated yielding the optimal joint planning and
deriving the resulting quality of the delivered tasks. In addition, two
heuristic solutions are developed for large-scale environments to cope with the
increased complexity of the optimization framework. The joint planning is
applied to a specific scenario of a flood in the San Francisco area. Results
show the effectiveness of the proposed heuristic solutions, which provide good
performance and allow for drastic savings in the computational time required to
plan the UAVs' trajectories with respect to the optimal approach, thus enabling
prompt reaction to the emergency events.
