**Abbreviations**

As with the to previous trajectory, the small angle control compensates its limitations with lower gains to guarantee stability, gains are bigger for position

Detailed results and analysis of this trajectory are available in [15].

lation results are easily available with the interface buttons.

may be an introduction to this field of research.

that may not be so clear using other visual tools.

DTV Control is again the best validated strategy. Geometric tracking presents a lower performance due to its sensitivity to noise and nonlinear operation conditions. The DTV control had presented the best validation even in the most chal-

Using the tool the graphical and numerical analysis was easier. Not much programming knowledge was necessary for using and configuration, just basic MatLab programming. Access to quadrotor parameters of the analyzed device, analyzing the influence of measurements noise, and comparing and simulating different control strategies is easier than with other visual platforms. Graphical and numerical simu-

Quadrotor control is a fascinating research area, but the equations involved and

This tool uses RMS and basin of attraction for numerical validation, and the GUI may help to evaluate stability, robustness, and accuracy. It integrates these criteria in a unique interface and helps to measure and visualize details and requirements

programming skills requirements can be arduous for initiating students. It is a worth to develop motivational appliances for beginners. This was the motivation to present a beginner-friendly visual interface tool for the development of quadrotor control strategies. It is easy to understand, device characteristics are simple to configure, and control algorithms can be implemented and analyzed with not much effort. It is not necessary to have a deep knowledge in programming languages, and

control than for attitude.

*Lemniscate shape trajectory.*

*Robotics Software Design and Engineering*

lenging conditions.

**Figure 9.**

**7. Summary**

**116**


## **Nomenclature**



**References**

27. IEEE, 2011.

[1] Annaz F. Uav testbed training platform development using panda3d. Industrial Robot: An International Journal. 2015;**42**(5):450-456

[2] A Zul Azfar and D Hazry. Simple gui design for monitoring of a remotely operated quadrotor unmanned aerial vehicle (uav). In *2011 IEEE 7th International Colloquium on Signal Processing and its Applications*, pages 23–

*DOI: http://dx.doi.org/10.5772/intechopen.97435*

*Quadrotor Unmanned Aerial Vehicles: Visual Interface for Simulation and Control…*

mode techniques applied to an indoor micro quadrotor. In *Proceedings of the 2005 IEEE international conference on robotics and automation*, pages 2247– 2252. IEEE, 2005. ISBN 078038914X.

[10] C Nicol, CJB Macnab, and A Ramirez-Serrano. Robust neural network control of a quadrotor helicopter. In *Electrical and Computer Engineering, 2008. CCECE 2008. Canadian Conference on*, pages 001233–

[11] Steven Lake Waslander, Gabriel M Hoffmann, Jung Soon Jang, and Claire J Tomlin. Multi-agent quadrotor testbed control design: Integral sliding mode vs. reinforcement learning. In *Intelligent Robots and Systems, 2005.(IROS 2005). 2005 IEEE/RSJ International Conference on*, pages 3712–3717. IEEE, 2005.

[12] Fadri Furrer, Michael Burri, Markus Achtelik, and Roland Siegwart. Rotors—

a modular gazebo mav simulator framework. In *Robot Operating System (ROS)*, pages 595–625. Springer, 2016.

[13] Subhan Khan, Mujtaba Hussain Jaffery, Athar Hanif, and Muhammad Rizwan Asif. Teaching tool for a control systems laboratory using a quadrotor as a plant in matlab. *IEEE Transactions on Education*, 60(4): 249–256, 2017.

[14] Manuel A Rendón and Felipe F Martins. Unmanned quadrotor path following nonlinear control tuning using particle swarm optimization. In *2018 Latin American Robotic Symposium, LARC 2018 and 2018 Workshop on Robotics in Education WRE 2018*, pages

[15] Manuel A Rendón, Felipe F Martins, and Luis Gustavo Ganimi. A visual interface tool for development of quadrotor control strategies. *Journal of Intelligent & Robotic Systems*, pages 1–18,

509–514. IEEE, 2018.

2020.

001238. IEEE, 2008.

[3] A Tayebi and S McGilvray. Attitude stabilization of a four-rotor aerial robot. In Decision and Control, 2004. CDC. *43rd IEEE Conference on*, volume 2, pages 1216–1221. IEEE, 2004.

[4] Tiago P Nascimento and Martin Saska. Position and attitude control of multi-rotor aerial vehicles: A survey. *Annual Reviews in Control*, 2019.

[5] Can Dikmen I, Arisoy A, Hakan Temeltas. Attitude control of a

Technologies. *RAST'09*. In: *4th*

727. IEEE. *2009*. p. 2009

quadrotor. In Recent Advances in Space

*International Conference on*, pages 722–

[6] Gabriel M Hoffmann, Haomiao Huang, Steven L Waslander, and Claire J Tomlin. Quadrotor helicopter flight dynamics and control: Theory and experiment. In *Proc. of the AIAA Guidance, Navigation, and Control Conference*, volume 2, page 4, 2007.

[7] Domingues JMB. Quadrotor prototype. *Dissertacio*: *Uneversidade*

[8] Ly Dat Minh and Cheolkeun Ha. Modeling and control of quadrotor mav using vision-based measurement. In *Strategic Technology (IFOST), 2010 International Forum on*, pages 70–75.

[9] Samir Bouabdallah and Roland Siegwart. Backstepping and sliding-

*Tecnica deLisboa*; 2009

IEEE, 2010.

**119**
