IEEE - Aerospace and Electronic Systems - May 2020 - 46

UAVs Formation Approach Using Fast Marching Square Methods
and in addition, generates trajectories compatibles with
the kinematics of the UAV.
In further research, the explicit relationship
between the adjustment parameters p1 and p2 and the
kinematic and dynamic constraints of the UAV will be
studied.

[11] M. Zhang, Y. Shen, Q. Wang, and Y. Wang, "Dynamic
artificial potential field based multi-robot formation control," in Proc. IEEE Instrum. Meas. Technol. Conf., 2010,
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"Formation constrained multi-robot system in unknown
environments," in Proc. IEEE Int. Conf. Robot. Autom.,
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ACKNOWLEDGMENTS

[13] A. Ahmad, T. Nascimento, A. Conceicao, A. Molina, and

This research was supported by RoboCity2030-DIH-CM
Madrid Robotics Digital Innovation Hub (Robotica aplicada
a la mejora de la calidad de vida de los ciudadanos, Fase IV;
S2018/NMT-4331), funded by Programas de Actividades I
+D en la Comunidad de Madrid and cofunded by Structural
Funds of the EU.

P. Lima, "Perception-driven multi-robot formation control," in Proc. IEEE Int. Conf. Robot. Autom., 2013,
pp. 1851-1856.
[14] K. Kanjanawanishkul and A. Zell, "A model-predictive
approach to formation control of omnidirectional mobile
robots," in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst.,
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[15] R. Olfati-Saber, "Flocking for multi-agent dynamic sys-

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46

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