Model-based analysis of multi-UAV path planning for surveying postdisaster building damage

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.

Deep Learning Approach for Classifying the Built Year and Structure of Individual Buildings by Automatically Linking Street View Images and GIS Building Data

“…In addition, comparing the accuracy with that achieved in the previous study demonstrated significant improvement over the results of using accurate Japanese real estate images with built-year and building-structure classifications of 0.367 and 0.786, respectively [17]. A previous study using SVI data achieved an accuracy of 0.869-0.871 in material classification in Chile [25] and an accuracy of 0.614 and 0.81 in built age classification in Austria and in Amsterdam, respectively [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30].…”

“…Although few studies have focused on classifying the built year, several methods have been proposed to classify the number of floors and structures based on field surveys [13], [14], aerial photographs [15], [16], real estate images [17], [18], [19], and methods that use attributes (number of floors and area) of statistical data and GIS building data [20], [21], [22], [23]. However, real estate images bias data collection by essentially covering only trading estate buildings and fewer old buildings.…”

UAV Formation Trajectory Planning Algorithms: A Review

“…Nagasawa et al [38] proposed a multi-UAV trajectory planning method in the case of three-dimensional building damage investigation or disaster, which combined the fuzzy c-means method of assigning positioning points to UAVs and the A* algorithm to calculate the access sequence of each UAV camera positioning point so as to obtain the feasible trajectory of multiple UAVs, which solves the problem of multi-UAV coverage trajectory planning for the 3D reconstruction of damaged buildings after disasters.…”

Area reconnaissance with heterogeneous groups of autonomous systems

“…There are algorithms based on multiagent reinforcement learning [6], on Multi-Agent Path Planning (MAPF) [11], with evolutionary algorithms [9], graph [4] and grid-based [7] solutions, combinations of algorithms for solving partial problems [12]. Multi-objective and multi-UAV path planning [13] are also available as well as UAV path planning to ensure the least time within the effective detection range of radar [14]. Some solutions have collision avoidance capabilitiese.g.…”