Cooperative multi-UAV coverage mission planning platform for remote sensing applications

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