Abstract
A multi-trip UAV delivery problem is considered in which trajectories are planned for UAVs operating in a hostile environment. UAV battery capacity and payload weight as well as vehicle reuse are taken into account. A fleet of homogeneous UAVs fly in a 2D plane matching a distribution network to service customers in a collision-free manner. The goal is to obtain a sequence of sub-missions that will ensure delivery of requested amounts of goods to customers, satisfying their demands within a given time horizon under the given weather forecast constraints. In this context, our objective is to establish the relationships linking decision variables such as wind speed and direction, battery capacity and payload weight. Computational experiments which allow to assess alternative strategies of UAV sub-mission planning are presented.
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References
Adbelhafiz, M., Mostafa, A., Girard, A.: Vehicle routing problem instances: application to multi-UAV mission planning. In: AIAA Guidance, Navigation, and Control Conference. American Institute of Aeronautics and Astronautics (2010)
Belkadi, A., Abaunza, H., Ciarletta, L., Castillo, P., Theilliol, D.: Distributed path planning for controlling a fleet of UAVs: application to a team of quadrotors To cite this version: HAL Id: hal-01537777 Distributed Path Planning for Controlling a Fleet of UAVs: Application to a Team of. (2017)
Bocewicz, G., Nielsen, P., Banaszak, Z., Thibbotuwawa, A.: A declarative modelling framework for routing of multiple UAVs in a system with mobile battery swapping stations. In: Intelligent Systems in Production Engineering and Maintenance. ISPEM 2018. Advances in Intelligent Systems and Computing, vol. 835, pp. 429–441 (2018). https://doi.org/10.1007/978-3-319-97490-3_42
Bocewicz, G., Nielsen, P., Banaszak, Z., Thibbotuwawa, A.: Routing and scheduling of unmanned aerial vehicles subject to cyclic production flow constraints. In: Advances in Intelligent Systems and Computing, vol. 801, pp. 75–86 (2019). https://doi.org/10.1007/978-3-319-99608-0_9
Coelho, B.N., Coelho, V.N., Coelho, I.M., Ochi, L.S., Haghnazar, K.R., Zuidema, D., Lima, M.S.F., da Costa, A.R.: A multi-objective green UAV routing problem. Comput. Oper. Res. 0, 1–10 (2017). https://doi.org/10.1016/j.cor.2017.04.011
Dorling, K., Heinrichs, J., Messier, G.G., Magierowski, S.: Vehicle routing problems for drone delivery. IEEE Trans. Syst. Man Cybern. Syst. 47, 70–85 (2017). https://doi.org/10.1109/tsmc.2016.2582745
Drucker, N., Penn, M., Strichman, O.: Cyclic routing of unmanned aerial vehicles. In: Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9676, pp. 125–141 (2016). https://doi.org/10.1007/978-3-319-33954-2_10
Geyer, C., Dey, D., Singh, S.: Prototype sense-and-avoid stemy for UAVs. Report (2009)
Gola, A., Kłosowski, G.: Application of fuzzy logic and genetic algorithms in automated works transport organization. In: Advances in Intelligent Systems and Computing, vol. 620, pp. 29–36 2018. https://doi.org/10.1007/978-3-319-62410-5_4
Goerzen, C., Kong, Z., Mettler, B.: A survey of motion planning algorithms from the perspective of autonomous UAV guidance (2010)
Habib, D., Jamal, H., Khan, S.A.: Employing multiple unmanned aerial vehicles for co-operative path planning. Int. J. Adv. Robot. Syst. 10, 1–9 (2013). https://doi.org/10.5772/56286
Khosiawan, Y., Nielsen, I., Do, N.A.D., Yahya, B.N.: Concept of indoor 3D-route UAV scheduling system. In: Advances in Intelligent Systems and Computing, pp. 79–88 (2016)
LaValle, S.M.: Planning Algorithms. Cambridge University Press, Cambridge (2006)
Tian, J., Shen, L., Zheng, Y.: Genetic algorithm based approach for multi-UAV cooperative reconnaissance mission planning problem. Presented at the BT - Foundations of Intelligent Systems (2006)
Liu, X.F., Guan, Z.W., Song, Y.Q., Chen, D.S.: An optimization model of UAV route planning for road segment surveillance. J. Cent. South Univ. 21, 2501–2510 (2014). https://doi.org/10.1007/s11771-014-2205-z
Rubio, J.C., Kragelund, S.: The trans-pacific crossing: long range adaptive path planning for UAVs through variable wind fields. In: The 22nd Digital Avionics Systems Conference, DASC 2003, p. 8–B. IEEE (2003)
Sitek, P., Wikarek, J.: Capacitated vehicle routing problem with pick-up and alternative delivery (CVRPPAD) – model and implementation using hybrid approach. Ann. Oper. Res. 273, 257–277 (2019). https://doi.org/10.1007/s10479-017-2722-x
Thibbotuwawa, A., Nielsen, P., Banaszak Z., Bocewicz, G.: Energy consumption in unmanned aerial vehicles: a review of energy consumption models and their relation to the UAV routing. In: Advances in Intelligent Systems and Computing, vol. 853, pp. 173–184 (2019). https://doi.org/10.1007/978-3-319-99996-8_16
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Thibbotuwawa, A., Bocewicz, G., Nielsen, P., Banaszak, Z. (2020). UAV Mission Planning Subject to Weather Forecast Constraints. In: Herrera-Viedma, E., Vale, Z., Nielsen, P., Martin Del Rey, A., Casado Vara, R. (eds) Distributed Computing and Artificial Intelligence, 16th International Conference, Special Sessions. DCAI 2019. Advances in Intelligent Systems and Computing, vol 1004. Springer, Cham. https://doi.org/10.1007/978-3-030-23946-6_8
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DOI: https://doi.org/10.1007/978-3-030-23946-6_8
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