TL;DR
This paper introduces a comprehensive multi-UAV mission planning platform that enhances coverage efficiency and task allocation, demonstrated through real-world remote sensing applications like agriculture and search and rescue.
Contribution
It presents a novel optimization scheme using Simulated Annealing for coverage path planning and employs DARP for task allocation, improving multi-UAV mission efficiency.
Findings
Significant coverage and path quality improvements over existing methods.
Enhanced mission completion times through optimized task allocation.
Successful real-world application in agriculture and rescue scenarios.
Abstract
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…
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