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Aggressive time-optimal control of quadcopters poses a significant challenge in the field of robotics.
M. J. Van Nieuwstadt and R. M. Murray, “Real-time trajectory generation for differentially flat systems,” International Journal of Robust and Nonlinear Control: IFAC-Affiliated Journal , vol. 8, no. 11, pp. 995–1020, 1998
1998
Earlier work this paper cites.
D. Mellinger and V. Kumar, “Minimum snap trajectory generation and control for quadrotors,” in 2011 IEEE International Conference on Robotics and Automation , 2011, pp. 2520–2525
2011
Earlier work this paper cites.
B. Gati, “Open source autopilot for academic research-the paparazzi system,” in American Control Conference (ACC), 2013 . Washington, DC: IEEE, Jun. 2013, pp. 1478–1481
2013
Earlier work this paper cites.
C. Liu, H. Lu, and W.-H. Chen, “An explicit mpc for quadrotor trajectory tracking,” in 2015 34th Chinese Control Conference (CCC) , 2015, pp. 4055–4060
2015
Earlier work this paper cites.
E. J. J. Smeur, Q. Chu, and G. C. H. E. de Croon, “Adaptive incremental nonlinear dynamic inversion for attitude control of micro air vehicles,” Journal of Guidance, Control, and Dynamics , vol. 39, no. 3, pp. 450–461, mar 2016
2016
Earlier work this paper cites.
M. Bangura, M. Melega, R. Naldi, and R. Mahony, “Aerodynamics of rotor blades for quadrotors,” 2016
2016
Earlier work this paper cites.
M. Hassanalian and A. Abdelkefi, “Classifications, applications, and design challenges of drones: A review,” Progress in Aerospace Sciences , vol. 91, pp. 99–131, 2017
2017
Earlier work this paper cites.
M. Faessler, A. Franchi, and D. Scaramuzza, “Differential flatness of quadrotor dynamics subject to rotor drag for accurate tracking of high-speed trajectories,” IEEE Robotics and Automation Letters , vol. 3, no. 2, pp. 620–626, 2017
2017
Earlier work this paper cites.
P. Ru and K. Subbarao, “Nonlinear model predictive control for unmanned aerial vehicles,” Aerospace , vol. 4, no. 2, 2017. [Online]. Available: https://www.mdpi.com/2226-4310/4/2/31
2017
Earlier work this paper cites.
J. Hwangbo, I. Sa, R. Siegwart, and M. Hutter, “Control of a quadrotor with reinforcement learning,” IEEE Robotics and Automation Letters , vol. 2, no. 4, pp. 2096–2103, 2017
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
E. Tal and S. Karaman, “Accurate tracking of aggressive quadrotor trajectories using incremental nonlinear dynamic inversion and differential flatness,” IEEE Transactions on Control Systems Technology , vol. 29, no. 3, pp. 1203–1218, 2020
2020
Cited alongside, same era.
D. Bicego, J. Mazzetto, R. Carli, M. Farina, and A. Franchi, “Nonlinear model predictive control with enhanced actuator model for multi-rotor aerial vehicles with generic designs,” Journal of Intelligent & Robotic Systems , vol. 100, no. 3-4, pp. 1213–1247, Sep. 2020
2020
Cited alongside, same era.
U. Ates, “Long-term planning with deep reinforcement learning on autonomous drones,” in 2020 Innovations in Intelligent Systems and Applications Conference (ASYU) . IEEE, 2020, pp. 1–6
2020
S. Sun, A. Romero, P. Foehn, E. Kaufmann, and D. Scaramuzza, “A comparative study of nonlinear mpc and differential-flatness-based control for quadrotor agile flight,” IEEE Transactions on Robotics , 2022
2022
Later among the works it cites.
D. Hanover, P. Foehn, S. Sun, E. Kaufmann, and D. Scaramuzza, “Performance, precision, and payloads: Adaptive nonlinear mpc for quadrotors,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 690–697, 2022
2022
Later among the works it cites.
R. Penicka, Y. Song, E. Kaufmann, and D. Scaramuzza, “Learning minimum-time flight in cluttered environments,” IEEE Robotics and Automation Letters , vol. 7, no. 3, pp. 7209–7216, 2022
2022
Later among the works it cites.
E. Kaufmann, L. Bauersfeld, and D. Scaramuzza, “A benchmark comparison of learned control policies for agile quadrotor flight,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 10 504–10 510
2022
Later among the works it cites.
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Cited alongside, same era.
G. Torrente, E. Kaufmann, P. Foehn, and D. Scaramuzza, “Data-driven MPC for quadrotors,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 3769–3776, apr 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
K. Nagami and M. Schwager, “Hjb-rl: Initializing reinforcement learning with optimal control policies applied to autonomous drone racing.” in Robotics: science and systems , 2021
2021
Cited alongside, same era.
Y. Song, M. Steinweg, E. Kaufmann, and D. Scaramuzza, “Autonomous drone racing with deep reinforcement learning,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 1205–1212
2021
Cited alongside, same era.
A. Raffin, A. Hill, A. Gleave, A. Kanervisto, M. Ernestus, and N. Dormann, “Stable-baselines3: Reliable reinforcement learning implementations,” Journal of Machine Learning Research , vol. 22, no. 268, pp. 1–8, 2021. [Online]. Available: http://jmlr.org/papers/v22/20-1364.html
2021
Cited alongside, same era.
A. Romero, R. Penicka, and D. Scaramuzza, “Time-optimal online replanning for agile quadrotor flight,” IEEE Robotics and Automation Letters , vol. 7, no. 3, pp. 7730–7737, Jul. 2022
2022
Cited alongside, same era.
Y. Song, A. Romero, M. Müller, V. Koltun, and D. Scaramuzza, “Reaching the limit in autonomous racing: Optimal control versus reinforcement learning,” Science Robotics , vol. 8, no. 82, p. eadg1462, 2023. [Online]. Available: https://www.science.org/doi/abs/10.1126/scirobotics.adg1462
2023
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2023
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E. Kaufmann, L. Bauersfeld, A. Loquercio, M. Müller, V. Koltun, and D. Scaramuzza, “Champion-level drone racing using deep reinforcement learning,” Nature , vol. 620, no. 7976, pp. 982–987, Aug 2023. [Online]. Available: https://doi.org/10.1038/s41586-023-06419-4
2023
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2023
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