Hybrid Route Optimisation for Maximum Air to Ground Channel Quality

[EN] The urban air mobility market is expected to grow constantly due to the increased interest in new forms of transportation. Managing aerial vehicles fleets, dependent on rising technologies such as artificial intelligence and automated ground control stations, will require a solid and uninterrupted connection to complete their trajectories. A path planner based on evolutionary algorithms to find the most suitable route has been previously proposed by the authors. Herein, we propose using particle swarm and hybrid optimisation algorithms instead of evolutionary algorithms in this work. The goal of speeding the route planning process and reducing computational costs is achieved using particle swarm and direct search algorithms. This improved path planner efficiently explores the search space and proposes a trajectory according to its predetermined goals: maximum air-to-ground quality, availability, and flight time. The proposal is tested in different situations, including diverse terrain conditions for various channel behaviours and no-fly zones.This study was funded by Airbus Defence and Space gmbh. Universitat Politecnica de Valencia will cover publication costs.Expósito-García, A.; Esteban González, H.; Schupke, D. (2022). Hybrid Route Optimisation for Maximum Air to Ground Channel Quality. Journal of Intelligent & Robotic Systems. 105(2):1-16. https://doi.org/10.1007/s10846-022-01590-81161052Analytical Graphics, Inc. www.stk.com. Accessed: 2021-02-23DCSR algorithm implementation by Matlab. https://cutt.ly/HY45SP8. Accessed: 2021-12-18Genetic algorithm implementation by Matlab. https://cutt.ly/OY47O56. Accessed: 2021-12-18Google Maps, Aerial view of the Alicante area. https://cutt.ly/DjnLvqA. Accessed: 2021-02-23Google Maps, Aerial view of the Alps area. https://cutt.ly/KjnLWuw. Accessed: 2021-02-23Google Maps, Aerial view of the Munich area. https://cutt.ly/1jnLnyu. Accessed: 2021-02-23Nelder-mead algorithm implementation by Matlab. https://cutt.ly/DY45Wxl. Accessed: 2021-12-18Particle swarm implementation by Matlab. https://cutt.ly/PY47TPC. Accessed: 2021-12-18Ali, S.F., Nguyen, L.: UAS C2 data link performance for safe automatic flight guidance and control operation. In: AIAA/IEEE Digital Avionics Systems Conference - Proceedings. https://doi.org/10.1109/DASC.2016.7778017(2016)Besada-Portas, E., De La Torre, L., De La Cruz, J.M., De Andrés-Toro, B.: Evolutionary trajectory planner for multiple UAVs in realistic scenarios. IEEE Trans. Robot. 26(4), 619–634 (2010). https://doi.org/10.1109/TRO.2010.2048610Chaimatanan, S., Delahaye, D., Mongeau, M.: A hybrid metaheuristic optimization algorithm for strategic planning of 4d aircraft trajectories at the continental scale. IEEE Comput. Intell. Mag. 9(4), 46–61 (2014)Cotton, W.B., Wing, D.J.: Airborne trajectory management for urban air mobility. In: 2018 Aviation Technology, Integration, and Operations Conference. https://doi.org/10.2514/6.2018-3674 (2018)de la Cruz, J.M., Besada-Portas, E., Torre-Cubillo, L., Andres-Toro, B., Lopez-Orozco, J.A.: Evolutionary path planner for UAVs in realistic environments. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation - GECCO ’08. https://doi.org/10.1145/1389095.1389383, p 1477 (2008)Dandanov, N., Al-Shatri, H., Klein, A., Poulkov, V.: Dynamic self-optimization of the antenna tilt for best trade-off between coverage and capacity in mobile networks. Wirel. Pers. Commun. 92(1), 251–278 (2017)Di Caprio, D., Ebrahimnejad, A., Alrezaamiri, H., Santos-Arteaga, F.J.: A novel ant colony algorithm for solving shortest path problems with fuzzy arc weights. Alexandria Engineering Journal (2021)Dong, X., He, S., Stojanovic, V.: Robust fault detection filter design for a class of discrete-time conic-type non-linear markov jump systems with jump fault signals. IET Control Theory Applic. 14(14), 1912–1919 (2020)Elouadih, A., Oulad-Said, A., Hassani, M.M.: Design and Simulation of a PIFA Antenna for the Use in 4G Mobile Telecommunications Networks. International Journal of Communications, Network and System Sciences. https://doi.org/10.4236/ijcns.2013.67035 (2013)Exposito, A., Schupke, D., Esteban, H.: Route optimisation for maximum air to ground channel quality. IEEE Access 8, 203619–203630 (2020). https://doi.org/10.1109/ACCESS.2020.3037075Fadlullah, Z.M., Takaishi, D., Nishiyama, H., Kato, N., Miura, R.: A dynamic trajectory control algorithm for improving the communication throughput and delay in UAV-aided networks. IEEE Network. https://doi.org/10.1109/MNET.2016.7389838 (2016)Fang, H., Zhu, G., Stojanovic, V., Nie, R., He, S., Luan, X., Liu, F.: Adaptive optimization algorithm for nonlinear Markov jump systems with partial unknown dynamics. Int. J. Robust Nonlinear Control 31(6), 2126–2140 (2021)Greenberg, E., Levy, P.: Channel characteristics of UAV to ground links over multipath urban environments. In: 2017 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2017. https://doi.org/10.1109/COMCAS.2017.8244753 (2017)Grøtli, E.I., Johansen, T.A.: Path planning for UAVs under communication constraints using SPLAT! and MILP. Journal of Intelligent and Robotic Systems, Theory and Applications. https://doi.org/10.1007/s10846-011-9619-8(2012)Haas, E.: Aeronautical channel modeling. IEEE Trans. Veh. Technol. 51(2), 254–264 (2002). https://doi.org/10.1109/25.994803, http://ieeexplore.ieee.org/document/994803/Hayat, S., Yanmaz, E., Brown, T., Bettstetter, C.: Multi-objective UAV path planning for search and rescue, 5569–5574. https://doi.org/10.1109/ICRA.2017.7989656 (2017)Ingber, L.: Adaptive simulated annealing (asa): Lessons learned arXiv preprint cs/0001018 (2000)Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Kumar, S., Tejani, G.G., Pholdee, N., Bureerat, S., Jangir, P.: Multi-objective teaching-learning-based optimization for structure optimization. Smart Science, 1–12 (2021)Kurban, R., Durmus, A., Karakose, E.: A comparison of novel metaheuristic algorithms on color aerial image multilevel thresholding. Eng. Appl. Artif. Intel. 105, 104410 (2021)Kurban, T., Civicioglu, P., Kurban, R., Besdok, E.: Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding. Appl. Soft Comput. 23, 128–143 (2014)Lagarias, J., Reeds, J., Wright, M., Wright, P.: Convergence Properties of the Nelder–Mead Simplex Method in Low Dimensions. SIAM J. Optim. 9, 112–147 (1998). https://doi.org/10.1137/S1052623496303470Lagarias, J.C., Reeds, J.A., Wright, M.H., Wright, P.E.: Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM J. Optim. 9(1), 112–147 (1998)Matolak, D.W., Sun, R.: Air-ground channel characterization for unmanned aircraft systems: The Hilly suburban environment. In: 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall). https://doi.org/10.1109/VTCFall.2014.6965861, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6965861, pp 1–5. IEEE (2014)Matolak, D.W., Sun, R.: Air-ground channel characterization for unmanned aircraft systems - part III: The suburban and near-urban environments. IEEE Trans. Veh. Technol. 66(8), 6607–6618 (2017). https://doi.org/10.1109/TVT.2017.2659651Matolak, D.W., Sun, R., Jamal, H., Rayess, W.: L- and C-band airframe shadowing measurements and statistics for a medium-sized aircraft. In: 2017 11th European Conference on Antennas and Propagation (EUCAP 2017), pp. 1429–1433. https://doi.org/10.23919/EuCAP.2017.7928054 (2017)Meyer, D., Wypych, T., Petrovic, V., Strawson, J., Kamat, S., Kuester, F.: An air traffic control simulator for test and development of airspace management schemes. In: 2018 IEEE Aerospace Conference, pp. 1–8. https://doi.org/10.1109/AERO.2018.8396575 (2018)Mezura-Montes, E., Coello, C.A.C.: Constraint-handling in nature-inspired numerical optimization: Past, present and future. Swarm Evol. Comput. 1(4), 173–194 (2011). https://doi.org/10.1016/j.swevo.2011.10.001, http://www.sciencedirect.com/science/article/pii/S2210650211000538Morro, J.V., Esteban, H., Soto, P., Boria, V.E., Bachiller, C., Cogollos, S., Gimeno, B.: Automated design of waveguide filters using aggressive space mapping with a segmentation strategy and hybrid optimization techniques. In: IEEE MTT-S International Microwave Symposium Digest, 2003, vol. 2, pp. 1215–1218 (2003)Nedic, N., Prsic, D., Dubonjic, L., Stojanovic, V., Djordjevic, V.: Optimal cascade hydraulic control for a parallel robot platform by pso. Int. J. Adv. Manuf. Technol. 72(5), 1085–1098 (2014)Rizzoli, P., Martone, M., Gonzalez, C., Wecklich, C., Borla Tridon, D., Bräutigam, B., Bachmann, M., Schulze, D., Fritz, T., Huber, M., Wessel, B., Krieger, G., Zink, M., Moreira, A.: Generation and performance assessment of the global TanDEM-X digital elevation model. ISPRS J. Photogram. Remote Sens. 132, 119–139 (2017). https://doi.org/10.1016/j.isprsjprs.2017.08.008, https://linkinghub.elsevier.com/retrieve/pii/S092427161730093XSahingoz, O.K.: Generation of bezier curve-based flyable trajectories for multi-UAV systems with parallel genetic algorithm. J. Intell. Robot. Syst. Theory Applic. 74 (1-2), 499–511 (2014). https://doi.org/10.1007/s10846-013-9968-6Scherer, J., Yahyanejad, S., Hayat, S., Yanmaz, E., Vukadinovic, V., Andre, T., Bettstetter, C., Rinner, B., Khan, A., Hellwagner, H.: An autonomous multi-UAV system for search and rescue. In: DroNet 2015 - Proceedings of the 2015 Workshop on Micro Aerial Vehicle Networks, Systems, and Applications for Civilian Use. https://doi.org/10.1145/2750675.2750683 (2015)Schneckenburger, N., Matolak, D., Jost, T., Fiebig, U.c., del Galdo, G., Jamal, H., Sun, R.: A geometrical-statistical model for the air-ground channel. In: 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC). https://doi.org/10.1109/DASC.2017.8102054, http://ieeexplore.ieee.org/document/8102054/, pp 1–6. IEEE (2017)Sha, J., Xu, M.: Applying hybrid genetic algorithm to constrained trajectory optimization. In: Proceedings of 2011 International Conference on Electronic Mechanical Engineering and Information Technology, vol. 7, pp. 3792–3795 (2011)Shakhatreh, H., Sawalmeh, A.H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., Othman, N.S., Khreishah, A., Guizani, M.: Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges. https://doi.org/10.1109/ACCESS.2019.2909530 (2019)ShangGuan, W., Yan, X., Cai, B., Wang, J.: Multiobjective optimization for train speed trajectory in ctcs high-speed railway with hybrid evolutionary algorithm. IEEE Trans. Intell. Transp. Syst. 16(4), 2215–2225 (2015)Singh, S., Mittal, N., Thakur, D., Singh, H., Oliva, D., Demin, A.: Nature and biologically inspired image segmentation techniques. Archives of Computational Methods in Engineering, 1–28 (2021)Sun, R., Matolak, D.W.: Air-ground channel characterization for unmanned aircraft systems part II: Hilly and mountainous settings. IEEE Trans. Veh. Technol. 66(3), 1913–1925 (2017). https://doi.org/10.1109/TVT.2016.2585504Sun, R., Matolak, D.W., Rayess, W.: Air-ground channel characterization for unmanned aircraft systems-part IV: Airframe shadowing. IEEE Trans. Veh. Technol. 66(9), 7643–7652 (2017). https://doi.org/10.1109/TVT.2017.2677884Szczerba, R.J.: Threat netting for real-time, intelligent route planners. In: 1999 Information, Decision and Control. Data and Information Fusion Symposium, Signal Processing and Communications Symposium and Decision and Control Symposium. Proceedings (Cat. No.99EX251), pp. 377–382 (1999)Wu, Q., Zeng, Y., Zhang, R.: Joint trajectory and communication design for multi-UAV enabled wireless networks. IEEE Trans. Wireless Commun. 17(3), 2109–2121 (2018). https://doi.org/10.1109/TWC.2017.2789293, 1705.02723, http://ieeexplore.ieee.org/document/8247211/Xie, P., Petovello, M.G.: Measuring GNSS Multipath Distributions in Urban Canyon Environments. IEEE Trans. Instrum. Meas. 64(2), 366–377 (2015)Xin, X., Tu, Y., Stojanovic, V., Wang, H., Shi, K., He, S., Pan, T.: Online reinforcement learning multiplayer non-zero sum games of continuous-time markov jump linear systems. Appl. Math. Comput. 126537, 412 (2022)Zeng, Y., Zhang, R., Lim, T.J.: Throughput maximization for mobile relaying systems. 2016 IEEE Globecom Workshops. GC Wkshps 2016 - Proceedings 64(12), 4983–4996 (2016). https://doi.org/10.1109/GLOCOMW.2016.7849066Zhang, G., Wu, Q., Cui, M., Zhang, R.: Securing UAV communications via trajectory optimization. 2017 IEEE Global Communications Conference GLOBECOM 2017 - Proceedings 2018-Janua, pp. 1–6. https://doi.org/10.1109/GLOCOM.2017.8254971 (2018

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