This paper tackles the problem of positioning a swarm of UAVs inside a completely unknown terrain, having as objective to maximize the overall situational awareness. The situational awareness is expressed by the number and quality of unique objects of interest, inside the UAVs' fields of view. YOLOv3 and a system to identify duplicate objects of interest were employed to assign a single score to each UAVs' configuration. Then, a novel navigation algorithm, capable of optimizing the previously defined score, without taking into consideration the dynamics of either UAVs or environment, is proposed. A cornerstone of the proposed approach is that it shares the same convergence characteristics as the block coordinate descent (BCD) family of approaches. The effectiveness and performance of the proposed navigation scheme were evaluated utilizing a series of experiments inside the AirSim simulator. The experimental evaluation indicates that the proposed navigation algorithm was able to consistently navigate the swarm of UAVs to "strategic" monitoring positions and also adapt to the different number of swarm sizes. Source code is available at https://github.com/dimikout3/ConvCAOAirSim.

中文翻译：

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无人机组队监控位置自主协同设计，最大限度提高检测对象的数量和质量

本文解决了在完全未知的地形内定位一群无人机的问题，其目标是最大限度地提高整体态势感知能力。态势感知通过无人机视野内独特感兴趣对象的数量和质量来表达。YOLOv3 和一个识别重复对象的系统被用来为每个无人机的配置分配一个分数。然后，提出了一种新颖的导航算法，能够优化先前定义的分数，而无需考虑无人机或环境的动态。所提出方法的基石是它与块坐标下降 (BCD) 系列方法具有相同的收敛特性。利用 AirSim 模拟器内的一系列实验评估了所提出的导航方案的有效性和性能。实验评估表明，所提出的导航算法能够始终如一地将无人机群导航到“战略”监控位置，并适应不同数量的群规模。源代码可在 https://github.com/dimikout3/ConvCAOAirSim 获得。