Abstract
Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout.
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Data Availability
The data that supports the findings of this study is available from Bocewicz Grzegorz and Radzki Grzegorz under request.
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References
Estrada, M.A.R., Ndoma, A.: The uses of unmanned aerial vehicles–UAV’s-(or drones) in social logistic: Natural disasters response and humanitarian relief aid. Procedia Comput. Sci. 149, 375–383 (2019). https://doi.org/10.1016/j.procs.2019.01.151
Restas, A.: Drone applications for supporting disaster management. World J. Eng. Technol. 03(03), 316–321 (2015). https://doi.org/10.4236/wjet.2015.33C047
Panda, K.G., Das, S., Sen, D., Arif, W.: Design and deployment of UAV-aided post-disaster emergency network, IEEE Access. 7, 102985–102999 (2019). https://doi.org/10.1109/ACCESS.2019.2931539
Erdelj, M., Natalizio, E., Chowdhury, K.R., Akyildiz, I.F.: Help from the sky: leveraging UAVs for disaster management, pp. 24–32. PERVASIVE computing (2017)
Pathak, P., Damle, M., Pal, P.R., Yadav, V.: Humanitarian impact of drones in healthcare and disaster management. Int. J. Recent Technol. Eng. (IJRTE)7(5), 2277–3878 (2019)
Wang, X., Poikonen, S., Golden, B.: The vehicle routing problem with drones: several worst-case results. Optim. Lett. 11, 679 (2017). https://doi.org/10.1007/s11590-016-1035-3
Dorling, K., Heinrichs, J., Messier, G.G., Magierowski, S.: Vehicle routing problems for drone delivery, IEEE (2016). https://doi.org/10.1109/TSMC.2016.2582745
Huang, M., Smilowitz, K., Balcik, B.: Models for relief routing: Equity, efficiency and efficacy. Transportation research part E: logistics and transportation review, 48(1), 2–18 (2012). https://doi.org/10.1016/j.tre.2011.05.004
De Vries, H., Van Wassenhove, L.N.: Do optimization models for humanitarian operations need a paradigm shift? Prod. Oper. Manag. 29(1), 55–61 (2020). https://doi.org/10.1111/poms.13092
Thibbotuwawa, A., Nielsen, P., Zbigniew, B., Bocewicz, G.: Energy consumption in unmanned aerial vehicles: a review of energy consumption models and their relation to the UAV routing. In: Adv. Intell. Syst. Comput. 173–184 (2019)
Thibbotuwawa, A., Bocewicz, G., Zbigniew, B., Nielsen, P.: A solution approach for UAV fleet mission planning in changing weather conditions. Appl. Sci. 9, 3972 (2019). https://doi.org/10.3390/app9193972
Thibbotuwawa, A., Bocewicz, G., Radzki, G., Nielsen, P., Banaszak, Z.: UAV mission planning resistant to weather uncertainty. Sensors. 20, 515 (2020)
Radzki, G., Nielsen, P., Bocewicz, G., Banaszak, Z.: A proactive approach to resistant UAV mission planning. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds.) Automation 2020: Towards industry of the future. AUTOMATION 2020. Advances in intelligent systems and computing, vol. 1140. Springer, Cham (2020)
Erdelj, M., Natalizio, E.: UAV-assisted disaster management:applications and open issues, UAV-assisted disaster management: applications and open issues. International Conference on Computing, Networking and Communications (2016). https://doi.org/10.1109/ICCNC.2016.7440563
Heikkilä, A.M., Havlik, D., Schlobinski, S.: Modelling crisis management for improved action and preparedness, VTT Technology 228, Julkaisija – Utgivare – Publisher, Tekniikantie (2015)
Weinstein, A., Schumacher, C.: UAV scheduling via the vehicle routing problem with time windows. AIAA Infotech@Aerospace 2007 Conference and Exhibit (2007). https://doi.org/10.2514/6.2007-2839
Sung, I., Nielsen, P.: Speed optimization algorithm with routing to minimize fuel consumption under time-dependent travel conditions. Prod. Manuf. Res. 8(1), 1–19 (2020). https://doi.org/10.1080/21693277.2020.1732848
Câmara, D.: Cavalry to the rescue: Drones fleet to help rescuers operations over disasters scenarios, 2014 IEEE Conference on Antenna Measurements & Applications (CAMA), Antibes Juan-les-Pins, 1–4. (2014). https://doi.org/10.1109/CAMA.2014.7003421
Tian, J., Shen, L., Zheng, Y.: Genetic algorithm based approach for Multi-UAV cooperative reconnaissance mission planning problem BT—Foundations of intelligent systems, pp. 101–110. Springer, Berlin/Heidelberg (2006)
Bekhti, M., Achir, N., Boussetta, K., Abdennebi, M.: Package delivery: A Heuristic approach for UAVs path planning and tracking. EAI Endorsed Transactions Drone on Internet of Things. 3:1–11 (2017). https://doi.org/10.4108/eai.31-8-2017.153048
Hildmann, H., Kovacs, E.: Review: Using Unmanned Aerial Vehicles (UAVs) as Mobile Sensing Platforms (MSPs) for disaster response, civil security and public safety, drones. 3(59) (2019). https://doi.org/10.3390/drones3030059, https://www.mdpi
Coelho, B.N., Coelho, V.N., Coelho, I.M.: A multi-objective green UAV routing problem. Comput. Oper. Res. 0, 1–10 (2017). https://doi.org/10.1016/j.cor.2017.04.011
Enright, J.J., Frazzoli, E., Pavone, M., Ketan, S.: Handbook of unmanned aerial vehicles. Handb. Unmanned Aer. Veh. (2015). https://doi.org/10.1007/978-90-481-9707-1
Cai, G., Dias, J., Seneviratne, L.: A survey of small-scale unmanned aerial vehicles: recent advances and future development trends. Unmanned Syst. 2(2), 1–26 (2014)
Wu, J., Wang, H., Li, N., Yao, P., Huang, Y., Yang, H.: Path planning for solar-powered UAV in urban environment. Neurocomputing. 275, 2055–2065 (2018). https://doi.org/10.1016/j.neucom.2017.10.037
Weiwei, Z., Wei, W., Nengcheng, C., Chao, W.: Efficient UAV Path Planning with Multiconstraints in a 3D large battlefield environment. 2014, Article ID 597092, 12 (2014). https://doi.org/10.1155/2014/597092
Avellar, G.S.C., Pereira, G.A.S., Pimenta, L.C.A., Iscold, P.: Multi-UAV routing for area coverage and remote sensing with minimum time. 15(11), 27783–27803 (2015). https://doi.org/10.3390/s151127783
Di Puglia Pugliese, L., Guerriero, F., Zorbas, D., Razafindralambo, T.: Modelling the mobile target covering problem using flying drones. Optim. Lett. 10, 1021–1052 (2016). https://doi.org/10.1007/s11590-015-0932-1
Schermer, D., Moeini, M., Wendt, O.: A hybrid VNS/Tabu search algorithm for solving the vehicle routing problem with drones and En route operations. Comput. Oper. Res. 109, 134–158 (2019). https://doi.org/10.1016/j.cor.2019.04.021
Bithas, P.S., Michailidis, E.T., Nomikos, N., Vouyioukas, D., Kanatas, A.G.: A survey on machine-learning techniques for UAV-based communications. Sensors (Basel)19(23), 5170 (2019). https://doi.org/10.3390/s19235170
Ham, A.M.: Integrated scheduling of m-truck, m-drone, and m-depot constrained by time-window, drop-pickup, and m-visit using constraint programming. Transp. Res. Part C Emerg. Technol. 91, 1–14 (2018). https://doi.org/10.1016/j.trc.2018.03.025
Sitek, P., Wikarek, J.: A multi-level approach to ubiquitous modeling and solving constraints in combinatorial optimization problems in production and distribution. Appl. In-tell48(5), 1344–1367 (2018)
Al-Mousa, A., Sababha, B.H., Al-Madi, N., Barghouthi, A., Younisse, R.: UTSim: A framework and simulator for UAV air traffic integration, control, and communication, Int. J. Adv. Rob. Syst. 1–19 (2019). https://doi.org/10.1177/1729881419870937
Hentati, I., Krichen, L., Fourati, M., Fourati, L.C.: Simulation tools, environments and frameworks for UAV systems performance analysis. 14th International Wireless Communications & Mobile Computing Conference (IWCMC), 1495–1500 (2018). https://doi.org/10.1109/IWCMC.2018.8450505
Viloria, DR, Solano Charris, EL, Muñoz Villamizar, A, Montoya Torres, JR: Unmanned aerial vehicles/drones in vehicle routing problems: a literature review. Int. Trans. Oper. Res. (2020). https://doi.org/10.1111/itor.12783
Tseng, C.-M., Chau, C.-K., Elbassioni, K., Khonji, M.: Autonomous recharging and flight mission planning for battery-operated autonomous drones, pp. 1–10 (2017)
Geyer, C., Sanjiv, S., Chamberlain, L.: Avoiding collisions between aircraft: state of the art and requirements for UAVs operating in civilian airspace. Tech. Report, CMU-RI-TR-08-03, Robotics Institute. Carnegie Mellon University, Pittsburgh (2008)
Kazim, M., Azar, A.T., Koubaa, A., Zaidi, A.: Disturbance-rejection-based optimized robust adaptive controllers for UAVs. IEEE Syst. J. 15(2), 3097–3108 (2021)
Shastry, A., Paley, D.A.: UAV state and parameter estimation in wind using calibration trajectories optimized for observability. IEEE Control Syst. Lett. 5(5), 1801–1806 (2020)
Rodríguez-Mata, A.E., Flores, G., Martínez-Vásquez, A.H., Mora-Felix, Z.D., Castro-Linares, R., Amabilis-Sosa, L.E.: Discontinuous high-gain observer in a robust control UAV quadrotor: Real-time application for watershed monitoring. Math. Probl. Eng. 2018, 1–10 (2018)
Traverso, P., Giunchiglia, E., Spalazzi, L., Giunchiglia, F.: Formal theories for reactive planning systems: some considerations raised from an experimental application, AAAI Technical Report WS-96-07, AAAI (https://www.aaai.org), 127–136 (1996). https://www.researchgate.net/publication/2270270
Balcik, B., Beamon, B.M., Smilowitz, K.: Last mile distribution in humanitarian relief. J. Intell. Transp. Syst. 12(2), 51–63 (2008). https://doi.org/10.1080/15472450802023329
Besiou, M., Pedraza-Martinez, A.J., Van Wassenhove, L.N.: OR applied to humanitarian operations. Eur. J. Oper. Res. 269(2), 397–405 (2018). https://doi.org/10.1016/j.ejor.2018.02.046
Beamon, BM, Balcik, B: Performance measurement in humanitarian relief chains. Int. J. Public Sector Manag. (2008). https://doi.org/10.1108/09513550810846087
Holguín-Veras, J, Jaller, M, Van Wassenhove, LN, Pérez, N, Wachtendorf, T: On the unique features of post-disaster humanitarian logistics. J. Oper. Manag. 30(7–8), 494–506 (2012). https://doi.org/10.1016/j.jom.2012.08.003
Bessel, F.W.: Translated by C. F. F. Karney & R. E. Deakin. Astron. Nachr. 331(8) (2010)
Rutstrum, C.: The wilderness route finder, University of Minnesota Press, Minneapolis ISBN 0-8166-3661-3 (2000)
Tan, X., Huang, J.X.: On computational complexity of pickup-and-delivery problems with precedence constraints or time windows. In IProceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 5635–5643. In IJCAI (2019). https://doi.org/10.24963/ijcai.2019/782
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Radzki Grzegorz, Golińska-Dawson Paulina, Bocewicz Grzegorz. The literature review and research gaps were provided by Radzki Grzegorz, Golińska-Dawson Paulina. Declarative model of considered problem was proposed by Radzki Grzegorz, Bocewicz Grzegorz, Banaszak Zbigniew. The first draft of the manuscript was written by Bocewicz Grzegorz, Banaszak Zbigniew, and the revisions were done by Radzki Grzegorz, Golińska-Dawson Paulina. All authors read and approved the final manuscript.
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Radzki, G., Golinska-Dawson, P., Bocewicz, G. et al. Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions. J Intell Robot Syst 103, 63 (2021). https://doi.org/10.1007/s10846-021-01502-2
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DOI: https://doi.org/10.1007/s10846-021-01502-2