Multi-UAV Path Planning Methodology for Postdisaster Building Damage Surveying

Multi-UAV Path Planning Methodology for Postdisaster Building Damage Surveying | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-UAV Path Planning Methodology for Postdisaster Building Damage Surveying Ryosuke Nagasawa, Erick Mas, Luis Moya, Shunichi Koshimura This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-129504/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract 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. Scientific Communication UAVs 3D SfM earthquake Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Full Text Supplementary Files ScientificReportsSM.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 22 Mar, 2021 Reviews received at journal 24 Jan, 2021 Reviews received at journal 21 Jan, 2021 Reviews received at journal 16 Jan, 2021 Reviewers agreed at journal 06 Jan, 2021 Reviews received at journal 06 Jan, 2021 Reviewers agreed at journal 04 Jan, 2021 Reviewers agreed at journal 04 Jan, 2021 Reviewers agreed at journal 04 Jan, 2021 Reviewers invited by journal 04 Jan, 2021 Editor assigned by journal 04 Jan, 2021 Editor invited by journal 22 Dec, 2020 Submission checks completed at journal 21 Dec, 2020 First submitted to journal 15 Dec, 2020 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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(left) and Equation 8 (right) with the redistribution of waypoints based on the degree of attribution.","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-129504/v1/4eded89e5f1bacb02f12108e.jpg"},{"id":4484486,"identity":"a67394ba-06fe-4105-8de5-2ec19a02863b","added_by":"auto","created_at":"2020-12-23 20:08:11","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":60088,"visible":true,"origin":"","legend":"Left - Site B generated with NetLogo3D. Right - Block X generated with Unity3D at site B.","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-129504/v1/f846f88122fa8b0b970ad4ce.jpg"},{"id":4484285,"identity":"aa016b3d-0593-4323-944c-1773d01eb4f7","added_by":"auto","created_at":"2020-12-23 20:05:11","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":81953,"visible":true,"origin":"","legend":"Comparison of two graffiti drawings. The top panel in each pair of images shows the results from the method proposed in this study, while the bottom panel shows the results of the conventional method.","description":"","filename":"Fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-129504/v1/1983930358ffebd7af7b30dd.jpg"},{"id":4484288,"identity":"fd776423-3780-4ec4-8bb2-b79af526f47e","added_by":"auto","created_at":"2020-12-23 20:05:11","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":101991,"visible":true,"origin":"","legend":"Comparison of the extraction of six damage features. 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