Abstract
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.
Highlights
A large body of work on 3D scanning, path planning and 3D reconstruction is available in the literature, here we briefly present the most salient work in 3D reconstruction and Coverage Path Planning (CPP) algorithms
From the discussions presented above, we can conclude that the clustering of the camera location points in accordance with the fuzzy C-means (FCM) method is quite effective in making the costs for multiple UAVs minimal and uniform when solving the multiple traveling salesman problem (MTSP)
Regarding the redistribution of camera location points, the results show that the UAV costs when applying FCM are more uniform than those achieved by the K-means method
Summary
After camera location points were generated for each structure, these were clustered using the K-means and FCM, optimizing the distance or flight time as explained in “Obstacle avoidance” section. These results suggest that the redistribution of the camera location points among the UAVs based on the attribution degree function of the FCM method drives the cost functions for all UAVs to approach the average value. From the discussions presented above, we can conclude that the clustering of the camera location points in accordance with the FCM method is quite effective in making the costs for multiple UAVs minimal and uniform when solving the MTSP. Even on a shady side where only a few tie points could be extracted, the proposed method precisely reproduced cracks and graffiti
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