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. An implementation of the the mCPP methodology introduced in this work, as well as a link for a demonstrative video and a link for a fully functional, on-line hosted instance of the presented platform can be found here: https://github.com/savvas-ap/mCPP-optimized-DARP.
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This HFOV was selected as a typical specification for commercial UAVs, based on the sensor that one of the most popular commercial UAVs is equiped with (DJI phantom 4 pro: https://www.dji.com/phantom-4-pro/info#specs)
GSD is calculated from the take off position, considering that the whole ROI has an almost flat topology. In order to have a constant GSD for ROIs with altitude variations, the flight altitude should be adjusted based on the actual distance from the ground, at each point of the region (see alsoGómez-López et al. (2020)).
Dell XPS 9570
Xiaomi Mi Max 2
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This project has received funding from the European Commission under the European Union’s Horizon 2020 research and innovation programme under grant agreement no 833805 (ARESIBO).
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Apostolidis, S.D., Kapoutsis, P.C., Kapoutsis, A.C. et al. Cooperative multi-UAV coverage mission planning platform for remote sensing applications. Auton Robot 46, 373–400 (2022). https://doi.org/10.1007/s10514-021-10028-3
- Cooperative robots
- Motion planning
- Remote sensing
- Aerial robotics