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Applications of UAVs in Search and Rescue

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Part of the Synthesis Lectures on Intelligent Technologies book series (SLIT)

Abstract

Unmanned aerial vehicles (UAVs) have grown in popularity over the last decade, with applications ranging from agriculture to commercial and widespread use in search and rescue (SAR) operations. The utilization of UAVs has been shown to enhance the effectiveness of these missions by providing a fast, agile, and cost-effective means of gathering information and performing tasks in challenging environments. This chapter aims to examine the current usage of UAVs in SAR missions, identify the challenges associated with their use, and provide a direction for future research on the topic. This chapter provides a detailed description of the various types of UAVs and sensors that are required to carry out successful SAR missions. Our research has found that UAVs are commonly utilized during the rescue and recovery phases in both indoor and outdoor settings for tasks such as mapping, detection, and intervention. While the number of studies on the deployment of UAVs in SAR operations has significantly increased in the past few years, further investigation is necessary to specifically focus on drone intervention in these missions.

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References

  1. A. Khan, S. Gupta, S.K. Gupta, Emerging uav technology for disaster detection, mitigation, response, and preparedness. J. Field Robot. (2022)

    Google Scholar 

  2. M.B. Bejiga, A. Zeggada, A. Nouffidj, F. Melgani, A convolutional neural network approach for assisting avalanche search and rescue operations with UAV imagery. Remote Sens. 9(2), 100 (2017)

    CrossRef  Google Scholar 

  3. H. Kesteloo, DJI mavic 2 enterprise dual helps hurt hiker rappel 400 ft during rescue at snow canyon state park, UT (2020). https://dronedj.com/2020/01/21/drone-helps-hurt-hikerrappel-400-ft-snow-canyon-state-park/

  4. H. Kesteloo, Thermal camera drone finds lost kayaker within minutes (2022). https://dronexl.co/2022/09/02/thermal-camera-drone-lost-kayaker-minutes/

  5. K.H. Frith, A. Amiri, Emerging technologies center. Nurs. Educ. Perspect. 43(3), 203 (2022)

    CrossRef  Google Scholar 

  6. H. Mohapatra, Offline drone instrumentalized ambulance for emergency situations. IAES Int. J. Robot. Autom. 9(4), 251 (2020)

    Google Scholar 

  7. V. Bowman, I. McTaggart, J. Warrington, C. Hymas, M. McGrath, Drone finds missing man in 20 min (2014). Accessed 23 Aug 2022

    Google Scholar 

  8. B. Siciliano, O. Khatib, Springer Handbook of Robotics, in IEEE Robotics & Automation Magazine, Robotics & Automation Magazine, IEEE, IEEE Robotics & Automation Magazine, vol. 3, (2008), p. 110. issn: 1070-9932. https://doi.org/10.1109/mra.2008.928399

  9. J.L. Casper, R.R. Murphy, Workflow study on human-robot interaction in usar, in IEEE International Conference on Robotics and Automation, 2002. Proceedings. ICRA’02, vol. 2 (IEEE, 2002), pp. 1997–2003

    Google Scholar 

  10. J. Casper, R.R. Murphy, Human-robot interactions during the robotassisted Urban search and rescue response at the world trade center. IEEE Trans. Syst., Man, Cybern., Part B (Cybern.) 33(3), 367–385 (2003)

    CrossRef  Google Scholar 

  11. A hierarchical reinforcement learning based control architecture for semiautonomous rescue robots in cluttered environments, in 2010 IEEE Conference on 2010 IEEE International Conference on Automation Science and Engineering, Automation Science and Engineering (CASE) (2010), p. 948. ISSN: 978-1-4244-5447-1. http://doi.org/10.1109/coase.2010.5584599

  12. J.-i. Meguro, K. Ishikawa, Disaster information collection into geographic information system using rescue robots (2006), pp. 3514–3520

    Google Scholar 

  13. Y. Liu, G. Nejat, Robotic urban search and rescue: a survey from the control perspective. J. Intell. Robot. Syst. 72(2), 147 (2013)

    CrossRef  Google Scholar 

  14. B. Robert, Search and rescue and disaster relief robots: has their time finally come? Ind. Robot.: Int. J. 2, 138 (2016). ISSN: 0143-991X. http://doi.org/10.1108/ir-12-2015-0228

  15. D. Lang, How i learned to make underwater robots (2012). http://edition.cnn.com/2012/10/10/tech/how-ilearned-to-make-underwater-robots/index.html

  16. R. Maruyama, Semi-autonomous snake-like robot for search and rescue (1995)

    Google Scholar 

  17. M. Półka, S. Ptak, Ł Kuziora, The use of UAV’s for search and rescue operations. Procedia Eng. 192, 748–752 (2017)

    CrossRef  Google Scholar 

  18. W. Stecz, K. Gromada, UAV mission planning with SAR application. Sensors 20(4), 1080 (2020)

    CrossRef  Google Scholar 

  19. S. Grogan, R. Pellerin, M. Gamache, The use of unmanned aerial vehicles and drones in search and rescue operations–a survey. Proc. PROLOG (2018)

    Google Scholar 

  20. R.R. Murphy, et al., Search and rescue robotics, in Springer Handbook of Robotics, ed. by B. Siciliano, O. Khatib (Springer Berlin Heidelberg, Berlin, Heidelberg 2008), pp. 1151–1173, ISBN: 978-3-540-30301-5. https://doi.org/10.1007/978-3-540-30301-5_51

  21. M.A. Goodrich et al., Supporting wilderness search and rescue using a camera-equipped mini UAV. J. Field Robot. 25(1–2), 89–110 (2008)

    CrossRef  Google Scholar 

  22. J. Qi, et al., Search and rescue rotary-wing UAV and its application to the Lushan Ms 7.0 earthquake. J. Field Robot. 33(3), 290–321 (2016)

    Google Scholar 

  23. J. Sun, B. Li, Y. Jiang, C.-Y. Wen, A camera-based target detection and positioning UAV system for search and rescue (SAR) purposes. Sensors 16(11), 1778 (2016)

    CrossRef  Google Scholar 

  24. R. Konrad, D. Serrano, P. Strupler, Unmanned aerial systems. Search and Rescue Robotics—From Theory to Practice (2017), pp. 37–52

    Google Scholar 

  25. J.P. Queralta, et al., Collaborative multi-robot search and rescue: planning, coordination, perception, and active vision. IEEE Access 8(191), 617–643 (2020). https://doi.org/10.1109/ACCESS.2020.3030190

  26. Y.-W. Huang, et al., Duckiefloat: a collision-tolerant resource-constrained blimp for long-term autonomy in subterranean environments (2019). arXiv:1910.14275

  27. T. Rouček, et al., Darpa subterranean challenge: multi-robotic exploration of underground environments, in International Conference on Modelling and Simulation for Autonomous Systems (Springer, Berlin, 2019), pp. 274–290

    Google Scholar 

  28. S. Waharte, N. Trigoni, Supporting search and rescue operations with UAVs, in 2010 International Conference on Emerging Security Technologies (IEEE, 2010), pp. 142–147

    Google Scholar 

  29. K. Hatazaki, M. Konyo, K. Isaki, S. Tadokoro, F. Takemura, Active scope camera for urban search and rescue, in 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2007), pp. 2596–2602

    Google Scholar 

  30. G. Loukas, S. Timotheou, Connecting trapped civilians to a wireless ad hoc network of emergency response robots, in 2008 11th IEEE Singapore International Conference on Communication Systems (IEEE, 2008), pp. 599–603

    Google Scholar 

  31. X. Zhang, M. Wu, J. Peng, F. Jiang, A rescue robot path planning based on ant colony optimization algorithm, in 2009 International Conference on Information Technology and Computer Science, vol. 2 (IEEE, 2009), pp. 180–183

    Google Scholar 

  32. F.S.N. Fard, H. Parvar, M.E. Shiri, E. Soleimani, Using self configurable particle swarm optimization for allocation position of rescue robots, in 2010 Second International Conference on Computer and Network Technology (IEEE, 2010), pp. 362–366

    Google Scholar 

  33. H.N. Pishkenari, S. Mahboobi, A. Alasty, Optimum synthesis of fuzzy logic controller for trajectory tracking by differential evolution. Sci. Iran. 18(2), 261–267 (2011)

    CrossRef  Google Scholar 

  34. Y.F. Ding, Q. Pan, Path planning for mobile robot search and rescue based on improved ant colony optimization algorithm, in Applied Mechanics and Materials, vol. 66 (Trans Tech Publications, 2011), pp. 1039–1044

    Google Scholar 

  35. E. Olson et al., Progress toward multi-robot reconnaissance and the magic 2010 competition. J. Field Robot. 29(5), 762–792 (2012)

    CrossRef  Google Scholar 

  36. P. Mirowski, T.K. Ho, S. Yi, M. MacDonald, Signalslam: simultaneous localization and mapping with mixed wifi, bluetooth, lte and magnetic signals, in International Conference on Indoor Positioning and Indoor Navigation (IEEE, 2013), pp. 1–10

    Google Scholar 

  37. A. Özgelen, E.I. Sklar, An approach to supervisory control of multirobot teams in dynamic domains, in Conference Towards Autonomous Robotic Systems (Springer, Berlin, 2015), pp. 198–203

    Google Scholar 

  38. L. Pineda, T. Takahashi, H.-T. Jung, S. Zilberstein, R. Grupen, Continual planning for search and rescue robots, in 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids) (IEEE, 2015), pp. 243–248

    Google Scholar 

  39. M. Elbanhawi, A. Mohamed, R. Clothier, J.L. Palmer, M. Simic, S. Watkins, Enabling technologies for autonomous MAV operations. Prog. Aerosp. Sci. 91, 27–52 (2017)

    CrossRef  Google Scholar 

  40. C. Mouradian, J. Sahoo, R.H. Glitho, M.J. Morrow, P.A. Polakos, A coalition formation algorithm for multi-robot task allocation in large-scale natural disasters, in 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC) (IEEE, 2017), pp. 1909–1914

    Google Scholar 

  41. S. Hayat, E. Yanmaz, T.X. Brown, C. Bettstetter, Multi-objective UAV path planning for search and rescue, in 2017 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2017), pp. 5569–5574

    Google Scholar 

  42. S. Aldhaheri, G. De Masi, È. Pairet, P. Ardón, Underwater robot manipulation: advances, challenges and prospective ventures, in OCEANS 2022-Chennai (IEEE, 2022), pp. 1–7

    Google Scholar 

  43. G. Wang, et al., Coastal dam inundation assessment for the yellow river delta: measurements, analysis and scenario. Remote Sens. 12(21) (2020). ISSN: 2072-4292. https://doi.org/10.3390/rs12213658. https://www.mdpi.com/2072-4292/12/21/3658

  44. I. Moir, Military Avionics Systems (Wiley, 2019)

    Google Scholar 

  45. R. Murphy, Summary of 47 known disaster robot deployments 2001–2015 (2015). http://crasar.org/2015/

  46. T. Stickings, Lifeguard drone saves a drowning woman and six other swimmers, in Mail Online (2018). https://www.dailymail.co.uk/news/article-6070655/Incredible-moment-lifeguard-DRONE-saves-drowningwoman-six-swimmers.html

  47. The Salt Lake Tribune (2019). https://www.sltrib.com/news/2019/01/11/crews-use-dronerescue/

  48. R. Bogue, The role of robots in firefighting. Ind. Robot.: Int. J. Robot. Res. Appl. (2021)

    Google Scholar 

  49. R.R. Murphy, et al., Use of small unmanned aerial systems for emergency management of flooding [techbrief], United States. Federal Highway Administration, Technical Report (2019)

    Google Scholar 

  50. G. De Cubber, H. Balta, D. Doroftei, Y. Baudoin, UAS deployment and data processing during the balkans flooding, in 2014 IEEE International Symposium on Safety, Security, and Rescue Robotics (IEEE, 2014), pp. 1–4

    Google Scholar 

  51. O. Fernandes, R. Murphy, J. Adams, D. Merrick, Quantitative data analysis: crasar small unmanned aerial systems at hurricane harvey, in 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) (IEEE, 2018), pp. 1–6

    Google Scholar 

  52. J. Portmann, S. Lynen, M. Chli, R. Siegwart, People detection and tracking from aerial thermal views, in 2014 IEEE International Conference on Robotics and Automation (ICRA) (2014), pp. 1794–1800. https://doi.org/10.1109/ICRA.2014.6907094

  53. I. Riaz, J. Piao, H. Shin, Human detection by using centrist features for thermal images, in International Conference Computer Graphics, Visualization, Computer Vision and Image Processing (Citeseer, 2013)

    Google Scholar 

  54. J. McGee, S.J. Mathew, F. Gonzalez, Unmanned aerial vehicle and artificial intelligence for thermal target detection in search and rescue applications, in 2020 International Conference on Unmanned Aircraft Systems (ICUAS) (IEEE, 2020), pp. 883–891

    Google Scholar 

  55. K. Akshatha, A.K. Karunakar, S.B. Shenoy, A.K. Pai, N.H. Nagaraj, S.S. Rohatgi, Human detection in aerial thermal images using faster R-CNN and SSD algorithms. Electronics 11(7), 1151 (2022)

    CrossRef  Google Scholar 

  56. Y. Uzun, M. Balcılar, K. Mahmoodi, F. Davletov, M.F. Amasyalı, S. Yavuz, Usage of hog (histograms of oriented gradients) features for victim detection at disaster areas, in 2013 8th International Conference on Electrical and Electronics Engineering (ELECO) (IEEE, 2013), pp. 535–538

    Google Scholar 

  57. D.-X. Xia, S.-Z. Su, S.-Z. Li, P.-M. Jodoin, Lying-pose detection with training dataset expansion, in 2014 IEEE International Conference on Image Processing (ICIP) (IEEE, 2014), pp. 3377–3381

    Google Scholar 

  58. D.-X. Xia, S.-Z. Li, Rotation angle recovery for rotation invariant detector in lying pose human body detection. J. Eng. (2015). ISSN: 2051-3305. https://doaj.org (Visited on 27 Aug 2017)

  59. A. Goian, R. Ashour, U. Ahmad, T. Taha, N. Almoosa, L. Seneviratne, Victim localization in USAR scenario exploiting multi-layer mapping structure. Remote Sens. 11(22), 2704 (2019)

    CrossRef  Google Scholar 

  60. S. Caputo, G. Castellano, F. Greco, C. Mencar, N. Petti, G. Vessio, Human detection in drone images using yolo for search-and-rescue operations, in International Conference of the Italian Association for Artificial Intelligence (Springer, Berlin, 2022), pp. 326–337

    Google Scholar 

  61. W. Liu, et al., SSD: Single shot multibox detector, in European Conference on Computer Vision (Springer, Berlin, 2016), pp. 21–37

    Google Scholar 

  62. J. Han, S. Karaoglu, H.-A. Le, T. Gevers, Object features and face detection performance: analyses with 3d-rendered synthetic data, in 2020 25th International Conference on Pattern Recognition (ICPR) (2021), pp. 9959–9966. https://doi.org/10.1109/ICPR48806.2021.9412915

  63. A. Borji, M.-M. Cheng, Q. Hou, H. Jiang, J. Li, Salient object detection: a survey. Comput. Vis. Media 5(2), 117–150 (2019)

    CrossRef  Google Scholar 

  64. L. Zhang, L. Lin, X. Liang, K. He, Is faster R-CNN doing well for pedestrian detection? (2016)

    Google Scholar 

  65. N. Zhang, F. Nex, G. Vosselman, N. Kerle, Training a disaster victim detection network for UAV search and rescue using harmonious composite images. Remote Sens. 14(13) (2022). ISSN: 2072-4292. https://doi.org/10.3390/rs14132977. https://www.mdpi.com/2072-4292/14/13/2977

  66. S. Hayat, E. Yanmaz, R. Muzaffar, Survey on unmanned aerial vehicle networks for civil applications: a communications viewpoint. IEEE Commun. Surv. Tutor. 18(4), 2624–2661 (2016)

    CrossRef  Google Scholar 

  67. O.S. Oubbati, A. Lakas, P. Lorenz, M. Atiquzzaman, A. Jamalipour, Leveraging communicating UAVs for emergency vehicle guidance in urban areas. IEEE Trans. Emerg. Top. Comput. 9(2), 1070–1082 (2019)

    CrossRef  Google Scholar 

  68. S.H. Alsamhi et al., UAV computing-assisted search and rescue mission framework for disaster and harsh environment mitigation. Drones 6(7), 154 (2022)

    CrossRef  Google Scholar 

  69. Y.-H. Ho, Y.-R. Chen, L.-J. Chen, Krypto: assisting search and rescue operations using Wi-Fi signal with UAV, in Proceedings of the First Workshop on Micro Aerial Vehicle Networks, Systems, and Applications for Civilian Use (2015), pp. 3–8

    Google Scholar 

  70. W.-Y.G. Louie, G. Nejat, A victim identification methodology for rescue robots operating in cluttered USAR environments. Adv. Robot. 27(5), 373–384 (2013)

    CrossRef  Google Scholar 

  71. H.S. Hadi, M. Rosbi, U.U. Sheikh, S.H.M. Amin, Fusion of thermal and depth images for occlusion handling for human detection from mobile robot, in 2015 10th Asian Control Conference (ASCC) (2015), pp. 1–5. https://doi.org/10.1109/ASCC.2015.7244722

  72. A. Rosinol, M. Abate, Y. Chang, L. Carlone, Kimera: an opensource library for real-time metric-semantic localization and mapping (2019). arXiv:1910.02490

  73. I. Kostavelis, A. Gasteratos, Semantic mapping for mobile robotics tasks: a survey. Robot. Auton. Syst. 66, 86–103 (2015)

    CrossRef  Google Scholar 

  74. C. Cadena et al., Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans. Rob. 32(6), 1309–1332 (2016)

    CrossRef  Google Scholar 

  75. D. Lang, D. Paulus, Semantic maps for robotics, in Proceedings of the Work-shop “Workshop on AI Robotics” at ICRA (2014)

    Google Scholar 

  76. I. Kostavelis, K. Charalampous, A. Gasteratos, J.K. Tsotsos, Robot navigation via spatial and temporal coherent semantic maps. Eng. Appl. Artif. Intell. 48, 173–187 (2016)

    CrossRef  Google Scholar 

  77. T. Dang, C. Papachristos, K. Alexis, Visual saliency-aware receding horizon autonomous exploration with application to aerial robotics, in 2018 IEEE International Conference on Robotics and Automation (ICRA) (2018), pp. 2526–2533. https://doi.org/10.1109/ICRA.2018.8460992

  78. T. Dang, C. Papachristos, K. Alexis, Autonomous exploration and simultaneous object search using aerial robots, in 2018 IEEE Aerospace Conference (IEEE, 2018)

    Google Scholar 

  79. R. Ashour, T. Taha, J.M.M. Dias, L. Seneviratne, N. Almoosa, Exploration for object mapping guided by environmental semantics using UAVs. Remote Sens. 12(5), 891 (2020)

    CrossRef  Google Scholar 

  80. R. Ashour, M. Abdelkader, J. Dias, N.I. Almoosa, T. Taha, Semantic hazard labelling and risk assessment mapping during robot exploration. IEEE Access 10(16) 337–349 (2022). https://doi.org/10.1109/ACCESS.2022.3148544

  81. R. da Rosa, M. Aurelio Wehrmeister, T. Brito, J.L. Lima, A.I.P.N. Pereira, Honeycomb map: a bioinspired topological map for indoor search and rescue unmanned aerial vehicles. Sensors 20(3) (2020). ISSN: 1424-8220. https://doi.org/10.3390/s20030907. https://www.mdpi.com/1424-8220/20/3/907

  82. V. San Juan, M. Santos, J.M. Andújar, Intelligent UAV map generation and discrete path planning for search and rescue operations. Complexity (2018)

    Google Scholar 

  83. Risk Assessment: Osh Answers (2022). Accessed 25 Aug 2022

    Google Scholar 

  84. In a first, indonesia is using lidar drones for disaster recovery efforts 2020 (2020). https://www.terra-drone.net/global/2019/05/15/terra-drone-indonesia-lidar-drones-for-disaster-recovery-palu/

  85. T. Luege, Case study no. 14: using drones to create maps and assess building damage in ecuador capacity 4 dev (2020). https://europa.eu/capacity4dev/innov-aid/blog/case-study-no-14-using-drones-create-maps-and-assess-building-damageecuador

  86. I. Jaukovic, A. Hunter, Unmanned aerial vehicles: a new tool for landslide risk assessment (2017)

    Google Scholar 

  87. W. Max, G. Levy, Drones have transformed blood delivery in Rwanda (2022). https://arstechnica.com/science/2022/04/drones-have-transformed-blood-delivery-in-rwanda/2/

  88. G. NHS, Greener NHS » world’s first chemo drone delivery announced on NHS birthday (2022). https://www.england.nhs.uk/greenernhs/2022/07/worlds-first-chemo-drone-deliveryannounced-on-nhs-birthday/

  89. S.E. Eid, S.S. Dol, Design and development of lightweight-high endurance unmanned aerial vehicle for offshore search and rescue operation, in Advances in Science and Engineering Technology International Conferences (ASET) (IEEE, 2019), pp. 1–5

    Google Scholar 

  90. V. Spurny, et al., Autonomous firefighting inside buildings by an unmanned aerial vehicle. IEEE Access 9(15), 872–890

    Google Scholar 

  91. J. Quenzel, et al., Autonomous fire fighting with a UAV-UGV team at MBZIRC 2020, in 2021 International Conference on Unmanned Aircraft Systems (ICUAS) (IEEE, 2021), pp. 934–941

    Google Scholar 

  92. C. Corrado, K. Panetta, Data fusion and unmanned aerial vehicles (UAVs) for first responders, in IEEE International Symposium on Technologies for Homeland Security (HST) (IEEE, 2017), pp. 1–6

    Google Scholar 

  93. L. Merino, F. Caballero, J.R. Martı-nez-de-Dios, I. Maza, A. Ollero, An unmanned aircraft system for automatic forest fire monitoring and measurement. J. Intell. Robot. Syst. 65(1), 533–548 (2012)

    CrossRef  Google Scholar 

  94. A. Chikwanha, S. Motepe, R. Stopforth, Survey and requirements for search and rescue ground and air vehicles for mining applications, in 2012 19th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) (IEEE, 2012), pp. 105–109

    Google Scholar 

  95. J. Zhao, J. Gao, F. Zhao, Y. Liu, A search-and-rescue robot system for remotely sensing the underground coal mine environment. Sensors 17(10), 2426 (2017)

    CrossRef  Google Scholar 

  96. A. Ranjan, H. Sahu, P. Misra, Wireless robotics networks for search and rescue in underground mines: taxonomy and open issues, in Exploring Critical Approaches of Evolutionary Computation (IGI Global, 2019), pp. 286–309

    Google Scholar 

  97. M. Silvagni, A. Tonoli, E. Zenerino, M. Chiaberge, Multipurpose UAV for search and rescue operations in mountain avalanche events. Geomat. Nat. Haz. Risk 8(1), 18–33 (2017)

    CrossRef  Google Scholar 

  98. G. Bryant, An autonomous multi-UAV system for avalanche search, M.S. Thesis, NTNU (2019)

    Google Scholar 

  99. A. Koval, C. Kanellakis, E. Vidmark, J. Haluska, G. Nikolakopoulos, A subterranean virtual cave world for gazebo based on the DARPA SubT challenge (2020). arXiv:2004.08452

  100. F. Pece, J. Kautz, T. Weyrich, Three depth-camera technologies compared, in First BEAMING Workshop, Barcelona (Citeseer, 2011), p. 9

    Google Scholar 

  101. I. Martinez-Alpiste, P. Casaseca-de-la-Higuera, J. Alcaraz-Calero, C. Grecos, Q. Wang, Benchmarking machine-learning-based object detection on a UAV and mobile platform, in 2019 IEEE Wireless Communications and Networking Conference (WCNC) (2019), pp. 1–6. https://doi.org/10.1109/WCNC.2019.8885504

  102. A. Albrigtsen, The application of unmanned aerial vehicles for snow avalanche search and rescue, M.S. Thesis (UiT The Arctic University of Norway, 2016)

    Google Scholar 

  103. M.T. DeGarmo, Issues concerning integration of unmanned aerial vehicles in civil airspace, Center for Advanced Aviation System Development, vol. 4 (2004)

    Google Scholar 

  104. H.B. Abrahamsen, A remotely piloted aircraft system in major incident management: concept and pilot, feasibility study. BMC Emerg. Med. 15(1), 1–12 (2015)

    CrossRef  MathSciNet  Google Scholar 

  105. D. Gerhardt, Feature-based mini unmanned air vehicle video euclidean stabilization with local mosaics (Brigham Young University, 2007)

    Google Scholar 

  106. S.A.H. Mohsan, N.Q.H. Othman, M.A. Khan, H. Amjad, J. Żywiołek, A comprehensive review of micro UAV charging techniques. Micromachines 13(6), 977 (2022)

    CrossRef  Google Scholar 

  107. L. Lin, M.A. Goodrich, UAV intelligent path planning for wilderness search and rescue, in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2009), pp. 709–714

    Google Scholar 

  108. L. Lin, M.A. Goodrich, A Bayesian approach to modeling lost person behaviors based on terrain features in wilderness search and rescue. Comput. Math. Organ. Theory 16(3), 300–323 (2010)

    CrossRef  Google Scholar 

  109. S. Clark, M.A. Goodrich, A hierarchical flight planner for sensor-driven UAV missions, in 2013 IEEE RO-MAN (IEEE, 2013), pp. 509–514

    Google Scholar 

  110. E. Soylemez, N. Usul, Utility of GIS in search and rescue operations, in ESRI Users Group Conference (2006)

    Google Scholar 

  111. D. Ferguson, GIS for wilderness search and rescue, in ESRI Federal User Conference, vol. 2012 (2008), p. 10

    Google Scholar 

  112. F. Serre, G. Pollin, F. Blanc-Paques, D. Drone, Device and method for seeking targets (2012)

    Google Scholar 

  113. T. Niedzielski et al., A real-time field experiment on search and rescue operations assisted by unmanned aerial vehicles. J. Field Robot. 35(6), 906–920 (2018)

    CrossRef  Google Scholar 

  114. F. Mufalli, R. Batta, R. Nagi, Simultaneous sensor selection and routing of unmanned aerial vehicles for complex mission plans. Comput. Oper. Res. 39(11), 2787–2799 (2012). ISSN: 0305-0548. https://doi.org/10.1016/j.cor.2012.02.010. https://www.sciencedirect.com/science/article/pii/S0305054812000366

  115. J. Wu, G. Zhou, Real-time UAV video processing for quick-response to natural disaster, in 2006 IEEE International Symposium on Geoscience and Remote Sensing (IEEE 2006), pp. 976–979

    Google Scholar 

  116. C.-c. Li, G.-s. Zhang, T.-j. Lei, A.-D. Gong, Quick image-processing method of UAV without control points data in earthquake disaster area. Trans. Nonferrous Metals Soc. China 21, s523–s528 (2011)

    Google Scholar 

  117. L. Lin, M.A. Goodrich, Hierarchical heuristic search using a gaussian mixture model for UAV coverage planning. IEEE Trans. Cybern. 44(12), 2532–2544 (2014)

    CrossRef  Google Scholar 

  118. M. Andriluka, et al., Vision based victim detection from unmanned aerial vehicles, in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2010), pp. 1740–1747

    Google Scholar 

  119. D.-J. Lee, P. Zhan, A. Thomas, R.B. Schoenberger, Shape-based human detection for threat assessment, in Visual Information Processing XIII, vol. 5438 (SPIE 2004), pp. 81–91

    Google Scholar 

  120. K. Mikolajczyk, C. Schmid, A. Zisserman, Human detection based on a probabilistic assembly of robust part detectors, in European Conference on Computer Vision (Springer, Berlin, 2004), pp. 69–82

    Google Scholar 

  121. J.W. Davis, M.A. Keck, A two-stage template approach to person detection in thermal imagery, in 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION’05), vol. 1 (IEEE 2005), pp. 364–369

    Google Scholar 

  122. P. Rudol, P. Doherty, Human body detection and geolocalization for UAV search and rescue missions using color and thermal imagery, in 2008 IEEE Aerospace Conference (IEEE, 2008), pp. 1–8

    Google Scholar 

  123. J. Scherer, et al., An autonomous multi-UAV system for search and rescue, in Proceedings of the First Workshop on Micro Aerial Vehicle Networks, Systems, and Applications for Civilian Use (2015), pp. 33–38

    Google Scholar 

  124. R. Murphy, Gaps analysis for rescue robots, ANS 2006: Sharing Solutions for Emergencies and Hazardous Environments (2006)

    Google Scholar 

  125. R.R. Murphy, Trial by fire [rescue robots]. IEEE Robot. Autom. Mag. 11(3), 50–61 (2004)

    CrossRef  Google Scholar 

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Correspondence to Reem Ashour .

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Ashour, R., Aldhaheri, S., Abu-Kheil, Y. (2023). Applications of UAVs in Search and Rescue. In: Abdelkader, M., Koubaa, A. (eds) Unmanned Aerial Vehicles Applications: Challenges and Trends. Synthesis Lectures on Intelligent Technologies. Springer, Cham. https://doi.org/10.1007/978-3-031-32037-8_5

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