Abstract
This paper addresses the use of model predictive control (MPC) for pursuit-evasion games, where a shuttle drone aims at capturing a remotely piloted target drone. Several models for shuttle and target vehicles are developed, considering different degrees of complexity and reliance in inner-loop controllers. Based on these models MPC strategies are defined for each vehicle, for joint tracking operation, as well as for differential pursuit-evasion games between shuttle and target vehicles. Lastly the human-in-the-loop is added to the model and respective MPC algorithms of the fixed-wing target. Simulation results are provided, showing several scenarios where each vehicle has the advantage in terms of physical capabilities, or the disadvantage of being remotely-piloted.
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References
Chipalkatty, R., Daepp, H., Egerstedt, M., Book, W.: Human-in-the-loop: MPC for shared control of a quadruped rescue robot. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4556–4561. IEEE (2011)
Drop, F.M.: Control-Theoretic Models of Feedforward in Manual Control, vol. 46. Logos Verlag Berlin GmbH (2016)
Eklund, J., Sprinkle, J., Sastry, S.: Implementing and testing a nonlinear model predictive tracking controller for aerial pursuit/evasion games on a fixed wing aircraft. In: Proceedings of the 2005, American Control Conference, vol. 3, pp. 1509–1514 (2005). https://doi.org/10.1109/ACC.2005.1470179
Findeisen, R., Imsland, L., Allgower, F., Foss, B.A.: State and output feedback nonlinear model predictive control: an overview. Eur. J. Control. 9(2), 190–206 (2003). https://doi.org/10.3166/ejc.9.190-206
Friedman, A.: Differential Games. Courier Corporation (2013)
Heaney, S.: Drone sighting causes flights to be suspended at Dublin airport. Irish Examiner (2023). https://www.irishexaminer.com/news/arid-41056655.html
Jain, A., Bansal, R., Kumar, A., Singh, K.: A comparative study of visual and auditory reaction times on the basis of gender and physical activity levels of medical first year students. Int. J. Appl. Basic Med. Res. 5(2), 124–127 (2015). https://doi.org/10.4103/2229-516X.157168
Limon, D., Alamo, T., Salas, F., Camacho, E.: Input to state stability of min-max MPC controllers for nonlinear systems with bounded uncertainties. Automatica 42(5), 797–803 (2006). https://doi.org/10.1016/j.automatica.2006.01.001
Lin, W.: Distributed UAV formation control using differential game approach. Aerosp. Sci. Technol. 35, 54–62 (2014). https://doi.org/10.1016/j.ast.2014.02.004
Magnusson, T.: Attitude control of a hexarotor. Master’s thesis, Linkõpings University (2014)
Mayne, D.Q.: Model predictive control: recent developments and future promise. Automatica 50(12), 2967–2986 (2014). https://doi.org/10.1016/j.automatica.2014.10.128
Perelman, A., Shima, T., Rusnak, I.: Cooperative differential games strategies for active aircraft protection from a homing missile. J. Guid. Control. Dyn. 34(3), 761–773 (2011)
Restas, A., et al.: Drone applications for supporting disaster management. World J. Eng. Technol. 3(03), 316 (2015)
Shahmoradi, J., Talebi, E., Roghanchi, P., Hassanalian, M.: A comprehensive review of applications of drone technology in the mining industry. Drones 4(3) (2020). https://doi.org/10.3390/drones4030034
Szafranski, G., Czyba, R.: Different approaches of PID control UAV type quadrotor. In: International Micro Air Vehicle Conference and Competitions (IMAV) (2011)
Tzannetos, G., Marantos, P., Kyriakopoulos, K.J.: A competitive differential game between an unmanned aerial and a ground vehicle using model predictive control. In: 2016 24th Mediterranean Conference on Control and Automation (MED), pp. 1053–1058 (2016). https://doi.org/10.1109/MED.2016.7535979
Vidal, R., Shakernia, O., Kim, H., Shim, D., Sastry, S.: Probabilistic pursuit-evasion games: theory, implementation, and experimental evaluation. IEEE Trans. Robot. Autom. 18(5), 662–669 (2002). https://doi.org/10.1109/TRA.2002.804040
Acknowledgements
This work was partially funded by the FCT projects CAPTURE (https://doi.org/10.54499/PTDC/EEI-AUT/1732/2020), CTS (UIDB/EEA/00066/2020), and LARSYS (UIDB/50009/2020).
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Rodrigues, P., Guerreiro, B. (2024). Non-cooperative Model Predictive Control for Capturing a Remotely Piloted Target Drone. In: Marques, L., Santos, C., Lima, J.L., Tardioli, D., Ferre, M. (eds) Robot 2023: Sixth Iberian Robotics Conference. ROBOT 2023. Lecture Notes in Networks and Systems, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-031-59167-9_7
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