Calculation Of The Optimum Number Of Unmanned Air Vehicles Required For Surveillance Missions

Year 2022, Volume: 10 Issue: 2, 101 - 105, 01.05.2022

İkbal Özdemir , Serkan Çaşka

https://doi.org/10.21541/apjess.1113053

Cited By: 2

Abstract

The aim of this study is to determine minimum quantity of unmanned aerial vehicles (UAVs) that should be used in an area where an aerial reconnaissance/observation activity will be carried out. In order to make this kind of calculation, firstly, the energy consumption of a UAV while flying with a constant speed was examined and then % energy level consumed by the UAV while passing each meter was obtained approximately. In this study, by considering the length of the trajectory which a UAV will navigate, required % energy level of a UAV to complete a single tour is calculated. If it is determined that one UAV can not complete its assigned trajectory, the number of UAVs are increased until each UAV complete its trajectory. In this study, the vehicle routing problem approach was used to calculate the UAV trajectories. Genetic algorithm method that is one of the metaheuristic optimization methods, was used obtain the solution of the vehicle routing problem (VRP). The developed algorithm has been run in Matlab environment. By changing the parameters of crossing rate and the population number in the genetic algorithm (GA) method, the lowest number of UAVs that is enough to carry out aerial observation over a target area and the shortest UAV trajectories were obtained. Results were presented in table form.

Keywords

unmanned aerial vehicles, vehicle routing problem, genetic algorithm

References

• E. Dönmez, and A. F. Kocamaz, “Çoklu hedeflerin çoklu robotlara paylaştırılması için bir yük dengeleme sistemi”, Bitlis Fen BEÜ Fen Bilimleri Dergisi, 8, no 2, pp. 533-548, June 2019.
• S. Çaşka, and A. Gayretli, “An Algorithm for Collaborative Patrolling Systems with Unmanned Air Vehicles and Unmanned Ground Vehicles”, 7. International Conference on Recent Advances in Space Technologies-RAST2015, İstanbul, 660-663, (2015).
• T. Oktay, and S. Çoban, “Simultaneous longitudinal and lateral flight control system design for both passive and active morphing TUAVs”, Elektronika Ir Elektrotechnika, vol.23, no 5, pp.15-20, October 2017.
• O. Köse, T. Oktay, “Simultaneous Quadrotor Autopilot System and Collective Morphing System Design”, Aircraft Engineering And Aerospace Technology, vol.92, no 7, pp. 1093-1100, June 2020.
• Ç. Koç, “Zaman Bağlı Araç Rotalama Problemi”, MSc. thesis, Konya Selçuk Üniversitesi Fen Bilimleri Fakültesi, Konya, 2012.
• B. Keçeci, F. Altıparmak, and İ. Kara, “Heterojen Eş-Zamanlı Topla-Dağıt Araç Rotalama Problemi: Matematiksel Modeller ve Sezgisel Bir Algoritma”, Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, vol. 20, no 2, pp. 185-195, June 2015.
• A. Weinstein, and C. Schumacher, “UAV Scheduling via the Vehicle Routing Problem with Time Windows”, Tech. Rep. AFRL-VA-WP-TP, Air Force Research Laboratory, California, 306, (2007).
• M. Faied, A. Mostafa, and A. Girard, “Vehicle Routing Problem Instances: Application to Multi-UAV Mission Planning”, AIAA Guidance Navigation and Control Conference, Canada, pp. 1-11, (2010).
• D. R. Viloria, E. L. Solano-Charris, A. Munoz, and J. R. Montoya-Torres, “Unmanned aerial vehicles/drones in vehicle routing problems: a literature review”, International Trasnactions in Operational Research, vol. 28, no 4, pp. 1626-1657, March 2020.
• I. Khoufi, A. Laouiti, and C. Adjih, “A Survey of Recent Extended Variants of the Traveling Salesman and Vehicle Routing Problems for Unmanned Aerial Vehicles”, Drones, vol. 3, no 3, pp. 66, August 2019.
• S. Seçkiner, A. Shumye, and S. Geçer, “Minimizing Solid Waste Collection Routes Using Ant Colony Algorithm: A Case Study in Gaziantep District”, Journal of Transportation and Logistics, vol. 6, no 1, pp. 29-47, June 2021.
• E. Okur, and M. Atlas, “Solution of Vehicle Routing Problem with Genetic Algorithm”, Anadolu University Journal of Social Sciences, vol. 20, no 3, pp. 227-254, September 2020.
• B. Urazel, and K. Keskin, “A Hybrid Solution Approach for Electric Vehicle Routing Problem with Soft Time-Windows”, El-Cezerî Journal of Science and Engineering, vol. 8, no 2, pp. 994-1006, May 2021.
• A.K.M. Masum, M. Shahjalal, F. Faruque, and I. Hasan, “Solving the Vehicle Routing Problem Using Genetic Algorithm”, International Journal of Advanced Computer Science and Applications, vol. 2, no 7, pp. 126, August 2011.
• J. Berger, and M. Barkaoui, “A Hybrid Genetic Algorithm for The Capacitated Vehicle Routing Problem”, Genetic and Evolutionary Computation Conference GECCO 2003, Canada, 646-656, (2003).
• M. E. Berberler, and U. G. Nuriyev, “A New Genetic Algorithm for the 0-1 Knapsack Problem”, Academic Platform Journal of Engineering and Science, vol. 4, no 3, pp. 9-14, October 2016.
• Ardupilot, URL: https://ardupilot.org/copter/docs/common-apm25-and-26-overview.html (Visited on Jan. 13,2022)
• E. Kivelevitch, B. Sharma, N. Ernest, M. Kumar, and K. Cohen, “A Market Based Solution to the Multiple Traveling Salesmen Problem”, Journal of Intelligent Robotic Systems, vol. 72, pp. 21-40, January 2013.
• A. Dastanpour, and R. Mahmood, “Feature Selection Based on Genetic Algorithm and Support Vector Machine for Intrusion Detection System”, The Second International Conference on Informatics Engineering & Information Science, Malaysia, 12-14, (2013).
• A. Thibbotuwawa, G. Bocewicz, P. Nielsen, and Z. Banaszak, “Unmanned Aerial Vehicle Routing Problems:A Literature Review”, Applied Sciences, vol. 10, no 13, pp. 1-20, June 2020.