Abstract
In mass casualty incidents, whether natural or human-made, Search and Rescue Operations (SAR) have a critical role in humanitarian problems. Time and information are crucial in victims’ survival. The high success rate in SAR operations depends on achieving reliable data about the number of victims, the severity of the failure, access status, and location status, etc. as soon as possible. Nowadays, the technology of Unmanned Aerial Vehicle (UAV) presents an opportunity to help the rescue teams with avoiding wasting time and accessing areas where searching by rescue teams are costly and impossible to go there. In this study, we design a mathematical model to get an optimal path planning to steer the UAVs based on the potential risk degree (PRD) of the candidate location in the affected area. The proposed model is inspired by the Travel Salesman Problem (TSP) that selects the optimal tour giving priority to districts with high PRDs obtained using the concept of similarity measure in the spherical fuzzy environment, considering the power limitation of UAVs. The priority of candidate locations is evaluated by Jaccard, exponential, and square root cosine similarity measures. The applicability of the proposed model is demonstrated by applying it to an earthquake case. A sensitivity analysis is performed to approve the validity of the method.
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San Juan, V., Santos, M., Andújar, J.M.: Intelligent UAV map generation and discrete path planning for search and rescue operations. Complexity 2018, 1–17 (2018)
Chen, S.W., Li, Y.Z., Xing, S.Q., Wang, X.S., Sato, M.: Urban damage evaluation using polarimetric SAR data. In International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2754–2757 (2014)
Xu, Z., et al.: Development of an UAS for post-earthquake disaster surveying and its application in Ms7.0 Lushan Earthquake, Sichuan, China. Comput. Geosci. 68(2014), 22–30 (2014)
Chen, J., et al.: Damage degree evaluation of earthquake area using UAV aerial image. Int. J. Aerosp. Eng. 2016, 1–10 (2016)
Waharte, S., Trigoni, N.: Supporting search and rescue operations with UAVs. In 2010 International Conference on Emerging Security Technologies, pp. 142–147 (2010)
Jun, M., Andrea, R.D’.: Path planning for unmanned aerial vehicles in uncertain and adversarial environments. In Butenko, S., Murphey, R., Pardalos, P.M. (eds.) Cooperative Control: Models, Applications and Algorithms, pp. 95–110. Springer, Berlin (2003)
Goodrich, M.A., et al.: Supporting wilderness search and rescue using a camera-equipped mini UAV: research articles. J. Field Robot. 25(1–2), 89–110 (2008)
Półka, M., Ptak, S., Kuziora, Ł., Kuczyńska, A.: The use of unmanned aerial vehicles by urban search and rescue groups. In Drones—Applications, pp. 83–95 (2018)
Bebis, G., et al. (eds.): Advances in Visual Computing, vol. 9474. Springer International Publishing, Cham (2015)
Silvagni, M., et al.: Multipurpose UAV for search and rescue operations in mountain avalanche events avalanche events, vol. 5705 (2017)
Avezum, M., Seitz, A., Bruegge, B.: MODCAP : a platform for cooperative search and rescue missions (2019)
S. K. B et al.: Towards highly reliable autonomy for urban search and rescue robots. Springer International Publishing Switzerland, pp. 118–129 (2015)
Ghazali, S.N.A.M., Anuar, H.A., Alsagoff, S.N.S.Z., Yusoff, Z.: Determining position of target subjects in maritime search and rescue (MSAR) operations using rotary wing unmanned aerial vehicles (UAVs). In 2016 International Conference on Information and Communication Technology (ICICTM), pp. 1–4 (2016)
Rahmes, M., Chester, D., Hunt, J., Chiasson, B.: Optimizing cooperative cognitive search and rescue UAVs. In Autonomous Systems: Sensors, Vehicles, Security and the Internet of Everything (2018)
Cui, J.Q., et al.: Drones for cooperative search and rescue in post-disaster situation. In 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), pp. 167–174 (2015)
Phung, M.D., Quach, C.H., Dinh, T.H., Ha, Q.: Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection. Autom. Construct. 81, 25–33 (2017)
Kallmann, M., Kapadia, M.: Geometric and discrete path planning for interactive virtual worlds. Synth. Lect. Vis. Comput. 8(1), 1–201 (2016)
Sujit, P.B., Saripalli, S., Sousa, J.B.: An evaluation of UAV path following algorithms. In 2013 European Control Conference (ECC), pp. 3332–3337 (2013)
Hernandez-Martinez, E.G., Ferreira-Vazquez, E.D., Fernandez-Anaya, G., Flores-Godoy, J.J.: Formation tracking of heterogeneous mobile agents using distance and area constraints. Complexity 2017, 1–13 (2017)
Kamrani, F., Ayani, R.: UAV path planning in search operations. In Aerial Vehicles, InTech (2009)
Besada-Portas, E., de la Torre, L., Moreno, A., Risco-Martín, J.L.: On the performance comparison of multi-objective evolutionary UAV path planners. Inf. Sci. 238, 111–125 (2013)
Auñón, P.G., Peñas, M.S.: Use of genetic algorithms for unmanned aerial systems path planning. In Decision Making and Soft Computing—Proceedings of the 11th International FLINS Conference, FLINS 2014, pp. 430–435 (2014)
Davoodi, M., Panahi, F., Mohades, A., Hashemi, S.N.: Multi-objective path planning in discrete space. Appl. Soft Comput. 13(1), 709–720 (2013)
Izzo, D.: Optimization of interplanetary trajectories for impulsive and continuous asteroid deflection. J. Guid. Control Dyn. 30(2), 401–408 (2007)
Yang, P., Tang, K., Lozano, J.A.: Estimation of distribution algorithms based unmanned aerial vehicle path planner using a new coordinate system. In 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1469–1476 (2014)
Ruz, J.J., Pajares, G., Jesus, M., Arevalo, O.: UAV trajectory planning for static and dynamic environments. INTECH Open Access Publisher (2009)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Seyfi-Shishavan, S.A., Kutlu Gündoğdu, F., Farrokhizadeh, E., Donyatalab, Y., Kahraman, C.: Novel similarity measures in spherical fuzzy environment and their applications. Eng. Appl. Artif. Intell. 94, 103837 (2020)
Gündoǧdu, F.K., Kahraman, C.: Spherical fuzzy sets and spherical fuzzy TOPSIS method. J. Intell. Fuzzy Syst. 36(1), 337–352 (2019)
Chiclana, F., Tapia García, J.M., del Moral, M.J., Herrera-Viedma, E.: A statistical comparative study of different similarity measures of consensus in group decision making. Inf. Sci. 221, 110–123 (2013)
Garcia-Aunon, P., Peñas, M.S., de la Cruz García, J.M.: Parameter selection based on fuzzy logic to improve UAV path-following algorithms. J. Appl. Logic 24, 62–75 (2017)
Valavanis, K.P., Vachtsevanos, G.J.: UAV mission and path planning: ıntroduction. In Handbook of Unmanned Aerial Vehicles, pp. 1443–1446. Springer Netherlands, Dordrecht (2015)
Huang, C., et al.: A new dynamic path planning approach for unmanned aerial vehicles. Complexity 2018, 1–17 (2018)
Fu, Z., Yu, J., Xie, G., Chen, Y., Mao, Y.: A heuristic evolutionary algorithm of UAV path planning. Wirel. Commun. Mobile Comput. 2018, 1–11 (2018)
Kamranzad, F., Memarian, H., Zare, M.: Earthquake risk assessment for Tehran, Iran. ISPRS Int. J. Geo-Inf. 9(7), 430 (2020)
Salman, F.S., Gül, S.: Deployment of field hospitals in mass casualty incidents. Comput. Ind. Eng. 74(1), 37–51 (2014)
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Seyfi-Shishavan, S.A., Farrokhizadeh, E., Kutlu Gündoğdu, F. (2022). A Novel Mathematical Model to Design UAV Trajectory for Search and Rescue Operations in Disaster. In: Kahraman, C., Aydın, S. (eds) Intelligent and Fuzzy Techniques in Aviation 4.0. Studies in Systems, Decision and Control, vol 372. Springer, Cham. https://doi.org/10.1007/978-3-030-75067-1_22
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