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Using tornado-related weather data to route unmanned aerial vehicles to locate damage and victims

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Abstract

This paper presents a framework for the use of unmanned aerial vehicles (UAVs) equipped with cameras and wireless sensors to search an area after the occurrence of a tornado. This paper attempts to demonstrate how tornado weather data can be incorporated into search and rescue procedures to allocate and route the UAVs. Traditionally, the time to assess and search an area after a tornado strikes is on the order of several days. Incorporating UAVs into a search and rescue team’s available tools can reduce this time span to the order of hours. These methods are applied and model in this project to three real-world cases. Several methods for creating ”waypoints,” points of interest for the UAVs to inspect, to route the UAVs were tested. An analysis was performed to compare the time it took to generate the waypoints and the resulting objective function value. It is observed that while there is an opportunity to use exact methods to generate waypoints, our proposed heuristic is sufficient for the rapid response needed in post-disaster relief.

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Availability of data and material

Data used are all publicly available and location of data can be found in Appendix A.

Code availability

Code is publicly available and will be located in the link in Appendix A.

Notes

  1. Although there are freeware or open source implementations such as COIN-OR

  2. Implementation Note: Warm starts were rejected by the Solver.

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Acknowledgements

This work has been supported by the Natural Sciences and Engineering Research Council of Canada and the Jarislowsky/SNC-Lavalin Research Chair in the Management of International Projects. This support is gratefully acknowledged. In addition, we would like to thank Prof. Frédéric Fabry of McGill University, Brad Small of the National Weather Service, and Dr. Patrick Marsh of the Storm Prediction Center for their time and assistance in locating and understanding the meteorological data

Funding

This work has been supported by the Natural Sciences and Engineering Research Council of Canada and the Jarislowsky/SNC-Lavalin Research Chair in the Management of International Projects. This support is gratefully acknowledged.

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Authors and Affiliations

  1. Polytechnique Montréal, Montréal, H3T 1J4, QC, Canada

    Sean Grogan, Robert Pellerin & Michel Gamache

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  1. Sean Grogan
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Correspondence to Sean Grogan.

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This work has been supported by the Natural Sciences and Engineering Research Council of Canada and the Jarislowsky/SNC-Lavalin Research Chair in the Management of International Projects. This support is gratefully acknowledged.

Appendices

Appendix A: Tools used in this paper and where to find data used in this paper

The source code for the paper can be found at https://github.com/seangrogan-researchprojects/tornado-and-routing-project-2021.

The final version of this software was coded in Python 3.7 The Python Software Foundation (2019). In addition to the Python standard library, this paper and research utilize the following packages: MatPlotLib Hunter (2007) for outputting images and graphics. Images with satellite imagery were generated using Google Earth Pro (2018). PyShp Lawhead (2019) from Geospatial Python for reading, processing, and manipulating ESRI shape files such as the LSR, SBW, and road files. Clustering algorithms (K-Means++, DBSCAN, and Mean Shift) were imported from Scikit-learn Pedregosa et al. (2011). Tabu Search uses Pandas, the Python Data Analysis Library McKinney (2010) as an underlying data structure for identifying neighbor solutions. Calculations between points on the Earth were calculated with the Great Circle Calculator Grogan (2019). Testing the model with exact solution methods was done with GUROBI 8.1 Gurobi Optimization, and the model was generated with the PuLP libary Mitchell (2010).

Data for this paper are all open source and available on government websites or the ArcGIS open data hub. Specifically, road data for Oklahoma Sharp and Willoughby (2019), road data for Texas Texas Department of Transportation (2019), The Texas General Land Office (2019), Fire station location data can be found here TechniGraphics, Inc (2010), all SBWs and LSRs were pulled from the historic data on the NWS Chat service NOAA (2019).

Appendix B: Acronyms, abbreviations, and initialisms

The following is a list of acronyms, abbreviations, and initialisms that appear in the proceeding paper (Table 13).

Table 13 Acronyms, abbreviations, and initialisms
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Grogan, S., Pellerin, R. & Gamache, M. Using tornado-related weather data to route unmanned aerial vehicles to locate damage and victims. OR Spectrum 43, 905–939 (2021). https://doi.org/10.1007/s00291-021-00640-1

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