Skip to main content

Autonomous Navigation Based on Proportional Controller with GPS Setpoint for UAV in External Environments

  • Conference paper
  • First Online:
Developments and Advances in Defense and Security (MICRADS 2020)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 181))

Included in the following conference series:

  • 674 Accesses

Abstract

In this paper, we propose the implementation of an autonomous navigation system for quadcopter UAVs through a trajectory established by GPS as a setpoint, where the control system, in this case, we will use the proportional one, will guide the UAV through said plotted trajectory, achieving the objective precisely, which is a very useful application in military operations since a system of this nature is required to carry out observation and recognition missions for decision making and to have clear knowledge of the scenario that is lived. To control a trajectory, the system communicates by means of a computer, acquires data by means of GPS on board and transfers them to the PC to be processed by means of calculations of conversion of distance and angle systems using global positioning data and from there, sends orders and flight plans by means of a proportional controller that allows corrections to be made with tolerances established by the controller to follow your flight plan and reach the exact point. Once reached the point, the system is validated for its automatic return, following the path of return drawn and then concludes with the landing that will be validated by artificial vision; even though the GPS system has a certain degree of accuracy, it is required to recognize a base and that you make your descent at the base where you started your flight plan, through the UAV camera vision, keeping in the system an image pattern that when identified by a certain number of points, will fulfill a landing action, ending the mission.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Venkatesh, G.A., Sumanth, P., Jansi, K.R.: Fully autonomous UAV. In: Proceedings: 2017 International Conference on Technical Advancements in Computers and Communications, ICTACC 2017, pp. 41–44, Oct 2017

    Google Scholar 

  2. Orbea, D., Moposita, J., Aguilar, W.G., Paredes, M., León, G., Jara-Olmedo, A.: Math model of UAV multi rotor prototype with fixed wing aerodynamic structure for a flight simulator. In: International Conference on Augmented Reality, Virtual Reality and Computer Graphics, pp. 199–211 (2017)

    Google Scholar 

  3. Rathbun, D., Kragelund, S.: IEEE Xplore—an evolution based path planning algorithm for autonomous motion of a UAV through uncertain environments. Digital Avionics (2002). Available from: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1052946%5Cnpapers2://publication/uuid/8479FE2D-6988-48FE-B714-669DA578E2E3

  4. Aguilar, W.G., Morales, S.G.: 3D environment mapping using the Kinect V2 and path planning based on RRT algorithms. Electronics 5(4), 70 (2016)

    Article  Google Scholar 

  5. Sani, M.F., Karimian, G.: Automatic navigation and landing of an indoor AR. Drone quadrotor using ArUco marker and inertial sensors. In: 1st International Conference on Computer and Drone Applications: Ethical Integration of Computer and Drone Technology for Humanity Sustainability, IConDA 2017, pp. 102–107, Jan 2018

    Google Scholar 

  6. Aguilar, W.G., Manosalvas, J.F., Guillén, J.A., Collaguazo, B.: Robust motion estimation based on multiple monocular camera for indoor autonomous navigation of micro aerial vehicle. In: International Conference on Augmented Reality, Virtual Reality and Computer Graphics, pp. 547–561 (2018)

    Google Scholar 

  7. Rahman, M.F.A., Radzuan, S.M., Hussain, Z., Khyasudeen, M.F., Ahmad, K.A., Ahmad, F., et al.: Performance of loiter and auto navigation for quadcopter in mission planning application using open source platform. In: Proceedings of 7th IEEE IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2017, Nov 2017. pp. 342–347 (2018)

    Google Scholar 

  8. Aguilar, W.G., Cobeña, B., Rodriguez, G., Salcedo, V.S., Collaguazo, B.: SVM and RGB-D sensor based gesture recognition for UAV control. In: International Conference on Augmented Reality, Virtual Reality and Computer Graphics, pp. 713–719 (2018)

    Google Scholar 

  9. Shang, J, Shi, Z.: Vision-based runway recognition for UAV autonomous landing. IJCSNS Int J Comput Sci Netw Secur 7(3), 112 (2007). Available from: http://paper.ijcsns.org/07_book/200703/20070317.pdf

  10. Aguilar, W.G., Luna, M.A., Ruiz, H., Moya, J.F., Luna, M.P., Abad, V., Parra, H.: Statistical abnormal crowd behavior detection and simulation for real-time applications. In: International Conference on Intelligent Robotics and Applications, pp. 671–682 (2017)

    Google Scholar 

  11. Brockers, R., Hummenberger, M., Weiss, S., Matthies, L.: Towards autonomous navigation of miniature UAV. IEEE Comput Soc Conf Comput Vis Pattern Recognit Work 645–51 (2014)

    Google Scholar 

  12. Aguilar, W.G., Salcedo, V.S., Sandoval, D.S., Cobeña, B.: Developing of a video-based model for UAV autonomous navigation. Latin American Workshop on Computational Neuroscience, pp. 94–105 (2017)

    Google Scholar 

  13. Imanberdiyev, N., Fu, C., Kayacan, E., Chen, I.M.: Autonomous navigation of UAV by using real-time model-based reinforcement learning. In: 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), Nov 2016, pp. 13–5 (2017)

    Google Scholar 

  14. Aguilar, W.G., Casaliglla, V.P., Polit, J.L.: Obstacle avoidance based-visual navigation for micro aerial vehicles. Electronics 6(1), 10 (2017)

    Article  Google Scholar 

  15. Aguilar, W.G., Angulo, C.: Real-time video stabilization without phantom movements for micro aerial vehicles. EURASIP J. Image Video Process. 2014(1), 46 (2014)

    Article  Google Scholar 

  16. Aguilar, W.G., Angulo, C.: Real-time model-based video stabilization for microaerial vehicles. Neural Process. Lett. 43(2), 459–477 (2016)

    Article  Google Scholar 

  17. Parrot. Dron Parrot Bebop 2 FPV|Sitio Web Official de Parrot [Internet]. 2019 [cited 2020 Jan 8]. Available from: https://www.parrot.com/es/drones/parrot-bebop-2-fpv

  18. Roberts, C.: GPS guided autonomous drone, 32 (2016). Available from: https://www.evansville.edu/majors/eecs/downloads/projects2016/CameronRobertsReport.pdf

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wilbert G. Aguilar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Merizalde, D., Aguilar, W.G., Calderón, M. (2020). Autonomous Navigation Based on Proportional Controller with GPS Setpoint for UAV in External Environments. In: Rocha, Á., Paredes-Calderón, M., Guarda, T. (eds) Developments and Advances in Defense and Security. MICRADS 2020. Smart Innovation, Systems and Technologies, vol 181. Springer, Singapore. https://doi.org/10.1007/978-981-15-4875-8_8

Download citation

Publish with us

Policies and ethics