Abstract
Unmanned Aerial Vehicles (UAV) have become more popular for usage due to the low cost of deployment and maintenance. Single UAV employment allows remote area monitoring and transferring different payloads to inaccessible or dangerous zones for human. In order to deal with flight tasks that are more complex, UAV swarms are applied. The main challenge of UAV swarm formation and flight control is to avoid vehicle collisions. In this case, artificial intelligence is responsible for flight performance in the airspace in such way that collision is avoided. The main requirements to the method, which will provide conflict-free maneuvers, are safety (collision avoidance), liveness (decentralized control, destination area reachability) and flyability (UAV flight performance constraints are satisfied). Artificial force field method fulfills all of these demands. It allows to detect a potential conflict between multiple UAVs in a swarm and other static or moving obstacles found in airspace, to provide collision resolution by changing UAVs flight parameters through maintaining minimum separation distance, including cases when manned vehicles are found in the same airspace. There can be distinguished by a wide range of obstacles: static (buildings, restricted areas and bed weather conditions) and dynamic ones (other UAVs, manned aircraft). Method allows keeping UAV swarm shape on the flight path, taking into account ground speed and turn bank angle values restrictions according to UAV’s flight performance characteristics.
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Boucher, P.: Domesticating the drone: the demilitarisation of unmanned aircraft for civil markets. Sci. Eng. Ethics 21, 1393–1412 (2015)
Debajit, D., Kumar, S.: A novel approach towards the designing of an antenna for aircraft collision avoidance system. AEU – Int. J. Electr. Commun. 71, 53–71 (2017)
Kuchar, J.K., Yang, L.C.: A review of conflict detection and resolution modeling methods. IEEE Trans. Intell. Transp. Syst. 1(4), 179–189 (2000)
Murray, R.M.: Recent research in cooperative control of multivehicle systems. ASME: J. Dyn. Syst. Meas. Control 129(5), 571–583 (2007). https://doi.org/10.1115/1.2766721
Geser, A., Muñoz, C.: A geometric approach to strategic conflict detection and resolution. In: Proceedings of the 21st Digital Avionics Systems Conference, vol. 1, pp. 6B1-1–6B1-11 (2002)
Park, J.-W., Oh, H., Tahk, M.-J.: UAV collision avoidance based on geometric approach. In: Proceedings of the 2008 SICE Annual Conference, 20th-22nd August 2008, Tokyo, pp. 2122–2126 (2008)
Matsuno, Y., Tsuchiya, T.: Probabilistic conflict detection in the presence of uncertainty. Air Traffic Management and Systems. LNEE, vol. 290, pp. 17–33. Springer, Tokyo (2014). https://doi.org/10.1007/978-4-431-54475-3_2
Borrelli, F., Subramanian, D., Raghunathan, A., Biegler, L.: MILP and NLP techniques for centralized trajectory planning of multiple unmanned air vehicles. In: Proceedings American Control Conference, pp. 5763–5768 (2006)
Chepizhenko, V.I.: Energy-potential method of dynamic objects polyconflicts guaranteed collision resolution. In: Cybernetics and Computer Engineering, no. 168, pp. 80–87 (2012)
Leonard, N.E., Fiorelli, E.: Virtual leaders, artificial potentials and coordinated control of groups. In: Proceedings of the 40th IEEE Conference on Decision and Control, vol. 3, pp. 2968–2973 (2001)
Nguyen, B.Q., et al.: Virtual attractive-repulsive potentials for cooperative control of second order dynamic vehicles on the Caltech MVWT. In: Proceedings of the American Control Conference, vol. 2, pp. 1084–1089 (2005)
Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res. 5(1), 90–98 (1986). https://doi.org/10.1177/027836498600500106
Liu, X., Ge, S.S., Goh, C.H.: Formation potential field for trajectory tracking control of multi-agents in constrained space. Int. J. Control 90, 1–15 (2016)
Yin, H., Cam, L.L., Roy, U.: Formation control for multiple unmanned aerial vehicles in constrained space using modified artificial potential field. Math. Model. Eng. Probl. 4(2), 100–105 (2017). https://doi.org/10.18280/mmep.040207
Ruibin, X., Gaohua, C.: Formation flight control of multi-UAV system with communication constraints. J. Aerosp. Technol. Manag. 8(2), 203–210 (2016)
Pavlova, S.V., Pavlov, V.V., Chepizhenko, V.I.: Virtual Einstein force fields in synergy of navigation environment of difficult ergatic systems. In: Proceedings of the National Aviation University, no. 3, pp. 15–27 (2012)
Chepizhenko, V.I.: Synthesis of artificial gravitational fields virtual meters for the polyconflicts resolution in the aeronavigation environment. In: Proceedings of the National Aviation University, no. 2, pp. 60–69 (2012)
Vadakkepat, P., Tan, K.C., Ming-Liang, W.: Evolutionary artificial potential fields and their application in real time robot path planning. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 1, pp. 256–263 (2000). https://doi.org/10.1109/cec.2000.870304
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Skyrda, I. (2019). Decentralized Autonomous Unmanned Aerial Vehicle Swarm Formation and Flight Control. In: Ermolayev, V., Suárez-Figueroa, M., Yakovyna, V., Mayr, H., Nikitchenko, M., Spivakovsky, A. (eds) Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2018. Communications in Computer and Information Science, vol 1007. Springer, Cham. https://doi.org/10.1007/978-3-030-13929-2_10
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DOI: https://doi.org/10.1007/978-3-030-13929-2_10
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