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UAVS flight routes optimization in changing weather conditions – constraint programming approach

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The problem of delivering goods in a distribution network is considered in which a fleet of Unmanned Aerial Vehicles (UAV) carries out transport operations. The changing weather conditions in which the transport operations take place and the UAVs energy capacity levels influenced by the weather conditions are taken into account as factors that affect the determination of a collision-free route. The goods must be delivered to the customers in a given time window. Establishing the routes are the focus of this study. Solutions maximizing the level of customer satisfaction are focused and the computational experiments presented in the study show the impact of weather conditions on route determination.
Rocznik
Strony
5--12
Opis fizyczny
Bibliogr. 16 poz., fig., tab.
Twórcy
  • Koszalin University of Technology, Department of Computer Science and Management, Sniadeckich 2, Koszalin, Poland
  • Aalborg University, Department of Materials and Production, Aalborg, Denmark
  • Koszalin University of Technology, Department of Computer Science and Management, Sniadeckich 2, Koszalin, Poland
Bibliografia
  • [1] Adbelhafiz, M., Mostafa, A., & Girard, A. (2010). Vehicle Routing Problem Instances: Application to Multi-UAV Mission Planning. In AIAA Guidance, Navigation, and Control Conference. Toronto, Canada. doi:10.2514/6.2010-8435
  • [2] Bocewicz, G., Nielsen, P., Banaszak, Z., & Thibbotuwawa, A. (2019). Routing and Scheduling of Unmanned Aerial Vehicles Subject to Cyclic Production Flow Constraints. In Advances in Intelligent Systems and Computing Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference (pp. 75–86, vol. 801). doi:10.1007/978-3-319-99608-0_9
  • [3] Chauhan, D., Unnikrishnan, A., & Figliozzi, M. (2019). Maximum coverage capacitated facility location problem with range constrained drones. Transportation Research Part C: Emerging Technologies, 99, 1-18. doi:10.1016/j.trc.2018.12.001
  • [4] Chiang, W., Li, Y., Shang, J., & Urban, T. L. (2019). Impact of drone delivery on sustainability and cost: Realizing the UAV potential through vehicle routing optimization. Applied Energy, 242, 1164–1175. doi:10.1016/j.apenergy.2019.03.117
  • [5] Dai, R., Fotedar, S., Radmanesh, M., & Kumar, M. (2018). Quality-aware UAV coverage and path planning in geometrically complex environments. Ad Hoc Networks, 73, 95–105. doi:10.1016/j.adhoc.2018.02.008
  • [6] Enright, J. J., Frazzoli, E., Pavone, M., & Savla, K. (2014). UAV Routing and Coordination in Stochastic, Dynamic Environments. Handbook of Unmanned Aerial Vehicles, 2079–2109. doi:10.1007/978-90-481-9707-1_28
  • [7] Fügenschuh, A., & Müllenstedt, D. (2015). Flight planning for unmanned aerial vehicles. Hamburg: Professur für Angewandte Mathematik, Helmut-Schmidt-Universität/Universität der Bundeswehr Hamburg, Fachbereich Maschinenbau.
  • [8] Goerzen, C., Kong, Z., & Mettler, B. (2009). A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance. Journal of Intelligent and Robotic Systems, 57(1–4), 65–100. doi:10.1007/s10846-009-9383-1
  • [9] Golden, B. L., Raghavan, S., & Wasil, E. A. (2011). The vehicle routing problem: Latest advances and new challenges (Ser. 43). New York, USA: Springer. doi:10.1007/978-0-387-77778-8
  • [10] Guerriero, F., Surace, R., Loscrí, V., & Natalizio, E. (2014). A multi-objective approach for unmanned aerial vehicle routing problem with soft time windows constraints. Applied Mathematical Modelling, 38(3), 839–852. doi:10.1016/j.apm.2013.07.002
  • [11] Karpenko, S., Konovalenko, I., Miller, A., Miller, B., & Nikolaev, D. (2015). UAV Control on the Basis of 3D Landmark Bearing-Only Observations. Sensors, 15(12), 29802–29820. doi:10.3390/s151229768
  • [12] Thibbotuwawa, A., Nielsen, P., Zbigniew, B., & Bocewicz, G. (2018a). Energy Consumption in Unmanned Aerial Vehicles: A Review of Energy Consumption Models and Their Relation to the UAV Routing. Advances in Intelligent Systems and Computing Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018 (pp. 173–184). doi:10.1007/978-3-319-99996-8_16
  • [13] Thibbotuwawa, A., Nielsen, P., Zbigniew, B., & Bocewicz, G. (2018b). Factors Affecting Energy Consumption of Unmanned Aerial Vehicles: An Analysis of How Energy Consumption Changes in Relation to UAV Routing. Advances in Intelligent Systems and Computing Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018 (pp. 228–238). doi:10.1007/978-3-319-99996-8_21
  • [14] Ullah, S., Kim, K., Kim, K. H., Imran, M., Khan, P., Tovar, E., & Ali, F. (2019). UAV-enabled healthcare architecture: Issues and challenges. Future Generation Computer Systems, 97, 425–432. doi:10.1016/j.future.2019.01.028
  • [15] Wang, X., Poikonen, S., & Golden, B. (2016). The vehicle routing problem with drones: Several worst-case results. Optimization Letters, 11(4), 679–697. doi:10.1007/s11590-016-1035-3
  • [16] Yakıcı, E. (2016). Solving location and routing problem for UAVs. Computers & Industrial Engineering, 102, 294-301. doi:10.1016/j.cie.2016.10.029
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-00c8f0cf-b717-48be-9332-f4da68c4cbc7
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