论文标题
无人机团队的监视器位置的自主和合作设计，以最大程度地提高检测到的物体的数量和质量
Autonomous and cooperative design of the monitor positions for a team of UAVs to maximize the quantity and quality of detected objects
论文作者
论文摘要
本文解决了将一群无人机在一个完全未知的地形内定位的问题，具有最大化整体情境意识的客观性。情境意识由无人机视野内部的独特对象的数量和质量表达。 Yolov3和一个用于识别重复对象的系统，用于为每个无人机的配置分配一个分数。然后，提出了一种能够优化先前定义的分数的新型导航算法，而无需考虑无人机或环境的动态。提出的方法的一个基石是，它具有与块坐标下降（BCD）方法家族相同的收敛特性。使用了一系列的Airsim模拟器，评估了提议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导航无人机群以“战略性”监测位置，并适应不同数量的群体大小。源代码可在https://github.com/dimikout3/convcaoairsim上找到。
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.
