  This paper proposes a novel mission planning platform, capable of efficiently
deploying a team of UAVs to cover complex-shaped areas, in various remote
sensing applications. Under the hood lies a novel optimization scheme for
grid-based methods, utilizing Simulated Annealing algorithm, that significantly
increases the achieved percentage of coverage and improves the qualitative
features of the generated paths. Extensive simulated evaluation in comparison
with a state-of-the-art alternative methodology, for coverage path planning
(CPP) operations, establishes the performance gains in terms of achieved
coverage and overall duration of the generated missions. On top of that, DARP
algorithm is employed to allocate sub-tasks to each member of the swarm, taking
into account each UAV's sensing and operational capabilities, their initial
positions and any no-fly-zones possibly defined inside the operational area.
This feature is of paramount importance in real-life applications, as it has
the potential to achieve tremendous performance improvements in terms of time
demanded to complete a mission, while at the same time it unlocks a wide new
range of applications, that was previously not feasible due to the limited
battery life of UAVs. In order to investigate the actual efficiency gains that
are introduced by the multi-UAV utilization, a simulated study is performed as
well. All of these capabilities are packed inside an end-to-end platform that
eases the utilization of UAVs' swarms in remote sensing applications. Its
versatility is demonstrated via two different real-life applications: (i) a
photogrametry for precision agriculture and (ii) an indicative search and
rescue for first responders missions, that were performed utilizing a swarm of
commercial UAVs. The source code can be found at:
https://github.com/savvas-ap/mCPP-optimized-DARP
