Advertisement

SMART: Implementing a New Flight System Based on Multi-agent and Embedded on a Real-Time Platform

  • Firdaous MarzoukEmail author
  • Mohamed Ennaji
  • Hicham Medromi
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 366)

Abstract

Current work in the field related to the drones mainly aimed at improving their autonomy related to the environment. The purpose of this paper focuses on the areas of improvement of autonomy, flexibility and adaptability of these aircraft during a flight. The developing of flying machines characterized by a pseudo-autonomy, better flexibility and adaptability to situations are mainly blocked by order constraints equipment, such as for architecture requires more complexity. To meet this need autonomous control architecture has been developed; it supports various aspects of the development process of the agent, from the design of the agent architecture, to the implementation on the hardware. An original architecture has been developed that allows the real-time control and manage different decisions by evaluating the path to follow and the speed of the UAV (unmanned aerial vehicle). It is deployed on an embedded system platform that provides good computing power for this kind of tasks in addition to managing communication with the ground operator.

Keywords

Control UAV Drone Autonomy Multi-agents SMA Embedded system Intelligent systems Autopilot 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chen, B., Zhang, A., Cao, L.: Autonomous intelligent decision-making system based on Bayesian SOM neural network for robot soccer. Neurocomputing 128, 447–458 (2014)CrossRefGoogle Scholar
  2. 2.
    Li, X.: A Software Scheme for UAV’s Safe Landing Area Discovery. AASRI Procedia 4, 230–235 (2013)CrossRefGoogle Scholar
  3. 3.
    Toratani, D.: Research and development of double tetrahedron hexa-rotorcraft (Dot-HR). In: Proc. 28th Int. Congr. Aeronaut. Sci., pp. 1–8 (2012)Google Scholar
  4. 4.
    Rullán-Lara, J.L., Salazar, S., Lozano, R.: Real-time localization of an UAV using Kalman filter and a Wireless Sensor Network. J. Intell. Robot. Syst. Theory Appl. 65, 283–293 (2012)CrossRefGoogle Scholar
  5. 5.
    Leong, B.T.M., Low, S.M., Ooi, M.P.-L.: Low-Cost Microcontroller-based Hover Control Design of a Quadcopter. Procedia Eng. 41(Iris), 458–464 (2012)CrossRefGoogle Scholar
  6. 6.
    Selecký, M., Meiser, T.: Integration of autonomous UAVs into multi-agent simulation. Acta Polytech. 52, 93–99 (2012)Google Scholar
  7. 7.
    Chrobocinski, P., Makri, E., Zotos, N., Stergiopoulos, C., Bogdos, G.: DARIUS project: deployable SAR integrated chain with unmanned systems. In: 2012 Int. Conf. Telecommun. Multimedia, TEMU 2012, pp. 220–226 (2012)Google Scholar
  8. 8.
    Tahar, K.N., Ahmad, A., Abdul, W., Wan, A., Akib, M.: Unmanned aerial vehicle technology for low cost. In: 11th South East Asian Surv. Congr. (11th SEASC 2011) (2011)Google Scholar
  9. 9.
    Bristeau, P.-J., Callou, F., Vissière, D., Petit, N.: The navigation and control technology inside the AR. drone micro UAV. In: Proc. 18th IFAC World Congr. 2011, vol. 18, pp. 1477–1484 (2011)CrossRefGoogle Scholar
  10. 10.
    Vokřínek, J., Novák, P., Komenda, A.: Ground tactical mission support by multi-agent control of UAV operations. In: Lect. Notes Comput. Sci., (LNAI, LNBI), vol. 6867, pp. 225–234 (2011)Google Scholar
  11. 11.
    Bhattacharya, S., Likhachev, M., Kumar, V.: Multi-agent path planning with multiple tasks and distance constraints. In: Proc. - IEEE Int. Conf. Robot. Autom., pp. 953–959 (2010)Google Scholar
  12. 12.
    Bertuccelli, L.F., Choi, H., Cho, P., How, J.P.: Real-time multi-UAV task assignment in dynamic and uncertain environments. Construction, 1–16 (2009)Google Scholar
  13. 13.
    Albusac, J., Castro-Schez, J.J., Lopez-Lopez, L.M., Vallejo, D., Jimenez-Linares, L.: A supervised learning approach to automate the acquisition of knowledge in surveillance systems. Signal Processing 89(12), 2400–2414 (2009)CrossRefGoogle Scholar
  14. 14.
    Jang, M.W., Agha, G.: Scalable agent distribution mechanisms for large-scale UAV simulations. In: 2005 Int. Conf. Integr. Knowl. Intensive Multi-Agent Syst. KIMAS 2005 Model. Explor. Eng., vol. 2005, pp. 85–90 (2005)Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  • Firdaous Marzouk
    • 1
    Email author
  • Mohamed Ennaji
    • 1
  • Hicham Medromi
    • 1
  1. 1.EAS Team, LISER LaboratoryENSEM, Hassan II UniversityCasablancaMorocco

Personalised recommendations