Model-based analysis of multi-UAV path planning for surveying postdisaster building damage.
Model-based analysis of multi-UAV path planning for surveying postdisaster building damage.
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DOI：
10.1038/s41598-021-97804-4
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发表时间：
2021-09-20
影响因子：
4.6
通讯作者：
Koshimura S
中科院分区：
文献类型：
--
作者：
Nagasawa R;Mas E;Moya L;Koshimura S
应急响应人员需要准确和全面的数据来做出明智的决策。此外，应迅速获取和分析数据，以确保有效应对。灾后手头的任务之一是在受影响地区进行损害评估。特别是，应评估建筑物的损坏情况，以说明可能的伤亡和流离失所人口，估计长期住房能力，并评估对依赖基本基础设施的服务（如医院、学校等）的损坏。遥感技术，包括卫星图像，可用于收集这类信息，以便对总体损害进行评估。然而，受损建筑物中的特定兴趣点需要更高分辨率的图像和详细的信息来评估受损情况。这些区域可以通过无人驾驶飞行器和3D模型重建进行进一步评估。针对灾后受损建筑物三维重建问题，提出了一种多无人机覆盖路径规划方法。该方法已在NetLogo3D，多代理模型环境中实现，并在Unity3D的虚拟构建环境中进行了测试。所提出的方法产生周围的目标受损建筑物的摄像机位置点。这些摄像机位置点被过滤以避免碰撞，然后使用K均值或模糊C均值方法进行排序。在聚类相机位置点并将其分配给每个UAV单元之后，路线优化过程作为多个旅行商问题进行。对路径进行最终校正以避开障碍物，并为每个UAV提供平衡飞行距离和时间的最终路径。本文提出的模型和方法的细节，并检查纹理分辨率从所提出的方法和传统的头顶飞行与最低点寻找方法在3D映射。该算法优于传统的方法在生成的3D模型的质量。
Emergency responders require accurate and comprehensive data to make informed decisions. Moreover, the data should be acquired and analyzed swiftly to ensure an efficient response. One of the tasks at hand post-disaster is damage assessment within the impacted areas. In particular, building damage should be assessed to account for possible casualties, and displaced populations, to estimate long-term shelter capacities, and to assess the damage to services that depend on essential infrastructure (e.g. hospitals, schools, etc.). Remote sensing techniques, including satellite imagery, can be used to gathering such information so that the overall damage can be assessed. However, specific points of interest among the damaged buildings need higher resolution images and detailed information to assess the damage situation. These areas can be further assessed through unmanned aerial vehicles and 3D model reconstruction. This paper presents a multi-UAV coverage path planning method for the 3D reconstruction of postdisaster damaged buildings. The methodology has been implemented in NetLogo3D, a multi-agent model environment, and tested in a virtual built environment in Unity3D. The proposed method generates camera location points surrounding targeted damaged buildings. These camera location points are filtered to avoid collision and then sorted using the K-means or the Fuzzy C-means methods. After clustering camera location points and allocating these to each UAV unit, a route optimization process is conducted as a multiple traveling salesman problem. Final corrections are made to paths to avoid obstacles and give a resulting path for each UAV that balances the flight distance and time. The paper presents the details of the model and methodologies, and an examination of the texture resolution obtained from the proposed method and the conventional overhead flight with the nadir-looking method used in 3D mappings. The algorithm outperforms the conventional method in terms of the quality of the generated 3D model.
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