Advertisement

An Autonomous Airship Swarm for Maritime Patrol

  • Constantino G. RibeiroEmail author
  • Luciano Santos Constantin Raptopoulos
  • Max Suell Dutra
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 152)

Abstract

The studies of swarm of unmanned aerial vehicles (UAVs) have been increasing, and swarm of UAVs can become part of many daily tasks. But, as matter of fact, even the use of a UAV does not mean the decreasing of operational complexities and, consequently, the costs of performing its tasks, because of high costs of trained operators and remote control facilities to operate state-of-the-art UAVs. So, in order to support the operation of swarm, this work proposes a parallel/distributed framework to control mission of each UAV. These unmanned crafts will less dependent on remote control facilities. Embedding the mission control running in an embedded parallel/distributed computer system will be able to carry on basic mission control tasks. In order to prove this concept, the following items were developed: (i) a prototype of an embedded parallel/distributed computer cluster using low-cost components; (ii) new procedures to resolve navigation and collision evasion issues; and (iii) a parallel/distributed path discover program. The tests carried out in the embedded parallel/distributed computer cluster prototype and with new evasion procedures proved the viability of proposed framework.

Keywords

Autonomous airship Swarm of robots Embedded computer cluster Parallel/distributed programs 

References

  1. 1.
    Defense Industry Daily staff: JLENS: Coordinating Cruise Missile Defense – And More, 30 December 2012. http://www.defenseindustrydaily.com/jlens-coordinating-cruise-missile-defense-and-more-02921/ (2012)
  2. 2.
    Corporation, A.P.: Aerial Products, 28 December 2012. http://www.aerialproducts.com/surveillance-systems/aerial-surveillance.html (2008)
  3. 3.
    Dadkhah, N., Korukanti, V.R., Kong, Z., Mettler, B.: Experimental demonstration of an online trajectory optimization scheme using approximate spatial value functions. In: Proceedings of the 48th IEEE Conference on Decision and Control, pp. 2978–2983 (2009)Google Scholar
  4. 4.
    Saiki, H., Fukao, T., Urakubo, T., Khno, T.: Hovering control of outdoor blimp robots based on path following. In: 2010 IEEE International Conference on Control Applications (CCA), pp. 2124–2129 (2010)Google Scholar
  5. 5.
    Mettler, B., Kong, Z.: Receding horizon trajectory optimization with a finite-state value function approximation. In: Proceeding of 2008 American Control Conference, Seattle, Washington, pp. 3810–3816 (2008)Google Scholar
  6. 6.
    González, P., Burgard, W., Sanz, R., Fernadez, J.L.: Developing a low-cost autonomous indoor blimp. J. Phys. Agents 3(1), 43–51 (2009)CrossRefGoogle Scholar
  7. 7.
    Fukao, T., et al.: Inverse optimal velocity field control of an outdoor blimp robot. In: Proceedings of the 17th World Congress the International Federation of Automatic Control, Seoul, Korea, pp. 4374–4379 (2008)Google Scholar
  8. 8.
    Rottmann, A., Plagemann, C., Hilgers, P., Burgard, W.: Autonomous blimp control using model-free reinforcement learning in a continuous state and action space. In: Proceeding of International Conference on Intelligent Robots and Systems, IROS 2007, pp. 1895–1900 (2007)Google Scholar
  9. 9.
    Gammon, S.M., Fryr, M.T., Qian, C.: The mathematical model of the tri-turbofan airship for autonomous formation control research. In: IEEE Region 5 Technical, Professional, and Student Conference, San Antonio, Texas (2006)Google Scholar
  10. 10.
    Azinheira, J.R., et al.: Visual servo control for the hovering of an outdoor robotic airship. In: Proceedings of the 2002 IEEE International Conference on Robotics & Automation, Washington, DC, pp. 2787–2792 (2002)Google Scholar
  11. 11.
    Machry, C.R.: As Potencialidades de Emprego dos Dirigíveis no Transporte de Carga. Reista da UNIFA (2003)Google Scholar
  12. 12.
    Navarro, I., Matía, F.: An introduction to swarm robotics. ISRN Robot. 2013, Article ID 608164. http://dx.doi.org/10.5402/2013/608164 (2013)
  13. 13.
    Fossen, T.I.: Guidance and Control of Ocean Vehicles. Wiley, West Susses, England (1994)Google Scholar
  14. 14.
    Hardkernel Co., Ltd.: Products, 24 January 2013. http://www.hardkernel.com/renewal_2011/products/prdt_info.php (2013)
  15. 15.
    Ribeiro, C.G., et al.: A platform for autonomous path control of unmanned airship. J. Braz. Soc. Mech. Sci. Eng. (2017).  https://doi.org/10.1007/s40430-017-0891-9. ISSN 1678-5878, Rio de Janeiro, BrazilCrossRefGoogle Scholar
  16. 16.
    Becker, D.J., Sterling, T.: Beowulf: a parallel workstation for scientific computation. In: Proceedings of the International Conference on Parallel Processing (1995)Google Scholar
  17. 17.
    ARDUINO: Arduino, 25 March 2013. http://www.arduino.cc/ (2013)
  18. 18.
    Fossen, T.I.: Marine control systems, guidance, navigation, and control of ships, rigs and underwater vehicles. In: Marine Cybernetics, Trondheim, Norway (2002). ISBN 82-92356-00-2Google Scholar
  19. 19.
    Reschke, C., Sterling, T., Ridge, D.: A design study of alternative network topologies for the Beowulf parallel workstations. In: Proceedings of the IEEE International Symposium on High Performance Distributed Computing (1996)Google Scholar
  20. 20.
    Ralph, N.: Odroid-x development board brings quad-core Exynos 4 Quad processor to budding Android hackers for $129, 24 January 2013. http://www.theverge.com/2012/7/13/3156032/odroid-x-development-board-exynos-4412-quad (2012)
  21. 21.
    Larabel, M.: Quad-Core ODROID-X Battles NVIDIA Tegra 3, 24 January 2013. http://www.phoronix.com/scan.php?page=article&item=samsung_odroidx&num=1 (2012)
  22. 22.
    redOrbit Staff & Wire Reports - Your Universe Online: Miniature Quad-Core Computer For Under $130, 24 January 2013. http://www.redorbit.com/news/technology/1112656695/miniature-quad-core-computer-for-under-130/ (2012)
  23. 23.
    Lidar USA 3D Documentation and Beyond: Lidar USA 3D Documentation and Beyond. http://www.lidarusa.com/page.php?cat=3, 7 July 2013 (15 de December de 2013)
  24. 24.
    Texas Instruments: LIDAR System Design for Automotive/Industrial/Military Applications, SIGNAL PATH designer SM. Tips, tricks, and techniques from the analog signal-path experts Texas Instruments, Literature Number: SNAA 123 (2013)Google Scholar
  25. 25.
    i Badia, S.B., Pyk, P., Verschure, P.F.: A biologically inspired flight control system for a blimp-based UAV. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 3053–3059 (2005)Google Scholar
  26. 26.
    U.S. Department of Homeland Security: NAVIGATION RULES ONLINE, 24 January 2013. http://www.navcen.uscg.gov/?pageName=navRulesContent (2013)

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Constantino G. Ribeiro
    • 1
    Email author
  • Luciano Santos Constantin Raptopoulos
    • 2
  • Max Suell Dutra
    • 3
  1. 1.CEFET-RJ, UnED Itaguaí, Rodovia Mário CovasRio de JaneiroBrazil
  2. 2.UnED Nova Iguaçu, Estrada de AdrianópolisRio de JaneiroBrazil
  3. 3.COPPE - UFRJ, Centro de Tecnologia, bloco G, salas 202/203/204, Universidade Federal do Rio de Janeiro, Cidade Universitária - Ilha do FundãoRio de JaneiroBrazil

Personalised recommendations