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.
翻译:本文提出一个新的任务规划平台,能够有效地部署一批无人驾驶飞行器,以覆盖各种遥感应用中复杂形状的地区。在顶部下,利用模拟安纳林算法,为电网法的每个成员分配子任务,利用模拟安纳林算法,大大提高了覆盖范围的实现百分比,提高了所生成路径的质量特征。与最先进的替代方法相比,广泛模拟评价对于覆盖路径规划(CPP)业务来说至关重要,因为它有可能在完成飞行任务所需的时间方面实现巨大的绩效改进,同时它释放了广泛的新应用范围,而由于铀航空的精确度有限,DARP算法还被用来向每个成员分配次任务,同时考虑到每个无人驾驶飞行器的感知和业务能力、其初始位置以及任何可能在操作区内界定的禁飞区。这个特征在现实生活中至关重要,因为它有可能在完成飞行任务所需的时间方面实现巨大的绩效改进,同时它也释放了广泛的新应用范围,而这以前由于铀航空的精确度搜索寿命有限,因此不可行,因为铀航空的准确性搜索寿命有限,考虑到每个成员的感测能力、最初位置,因此,在实际使用航空平台上的所有效率收益都是通过磁利用。