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.
For effective disaster relief decision-making, responders require extensive and rapid information on the damage situation in affected areas. Areas with unknown conditions pose a high risk of injury, and working on the ground limits the coverage and speed of information acquisition. An alternative is to exploit aerial observations and, in particular, unmanned aerial vehicles (UAVs). UAVs can be rapidly deployed to access remote areas without risking survey teams. Moreover, large-scale disasters impact wide areas, and multiple UAVs are needed to increase coverage without compromising resolution or speed. Of particular importance for evaluation are assets such as hospitals, shelters and essential infrastructures. UAVs can survey such structures to construct three-dimensional (3D) models for inspection.A structure-from-motion (SfM) survey generates 3D models from multiple images. However, most path planning algorithms for SfM focus on points of interest taken from an individual UAV and consider a single structure. Here, we propose a path design method for multi-UAV SfM surveys. By designing flight paths with sufficient overlap and sidelap ratios for all faces of the surveyed objects, more precise 3D models can be constructed than with conventional methods. The fuzzy C-means method is adopted to reduce the UAV flight loads to a uniform minimum to ensure full battery utilization.
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