International Symposium on Visual Computing

Advances in Visual Computing pp 628-637 | Cite as

Adaptive Flocking Control of Multiple Unmanned Ground Vehicles by Using a UAV

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9475)

Abstract

In this paper we aim to discuss adaptive flocking control of multiple Unmanned Ground Vehicles (UGVs) by using an Unmanned Aerial Vehicle (UAV). We utilize a Quadrotor to provide the positions of all agents and also to manage the shrinking or expanding of the agents with respect to the environmental changes. The proposed method adaptively causes changing in the sensing range of the ground robots as the quadrotor attitude changes. The simulation results show the effectiveness of proposed method.

Keywords

Hybrid system Multi-agent Flocking control UAVs UGVs 

References

  1. 1.
    Sabattini, L.: Nonlinear Control Strategies for Cooperative Control of Multi-Robot Systems. Ph.D. thesis, Universitá di Bologna (2012)Google Scholar
  2. 2.
    Garg, D.P., Fricke, G.K.: Potential function based formation control of mobile multiple-agent systems. In: 1st International and 16th National Conference on Machines and Mechanisms (iNaCoMM2013) (2013)Google Scholar
  3. 3.
    Dong, W., Guo, Y., Farrell, J.: Formation control of nonholonomic mobile robots. In: American Control Conference, pp. 5602–5607. IEEE (2006)Google Scholar
  4. 4.
    Speranzon, A.: On Control Under Communicaiton Constraints in Autonomous Multi-Robot Systems. KTH, Signals, Sensors and Systems, Stockholm (2004)Google Scholar
  5. 5.
    Arranz, L.B.: Cooperative control design for a fleet of AUVs under communication constraints. Ph.D. thesis, Université de Grenoble (2011)Google Scholar
  6. 6.
    Clark, J.D.: Cooperative hybrid control of robotic sensors for perimeter detection and tracking. Master’s thesis, Oklahoma State University (2005)Google Scholar
  7. 7.
    Olfati-Saber, R.: Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans. Autom. Control 51, 401–420 (2006)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Coza, C., Nicol, C., Macnab, C., Ramirez-Serrano, A.: Adaptive fuzzy control for a quadrotor helicopter robust to wind buffeting. J. Intell. Fuzzy Syst.: Appl. Eng. Technol. 22, 267–283 (2011)MATHMathSciNetGoogle Scholar
  9. 9.
    Zhang, Y., Xu, B., Li, H.: Adaptive neural control of a quadrotor helicopter with extreme learning machine. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, K.-A. (eds.) Proceedings of ELM-2014 Volume 2, PALO, vol. 4, pp. 125–134. Springer, Heidelberg (2014) Google Scholar
  10. 10.
    Islam, S., Faraz, M., Ashour, R., Cai, G., Dias, J., Seneviratne, L.: Adaptive sliding mode control design for quadrotor unmanned aerial vehicle. In: 2015 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 34–39. IEEE (2015)Google Scholar
  11. 11.
    Jafari, M., Shahri, A.M., Shouraki, S.B.: Attitude control of a quadrotor using brain emotional learning based intelligent controller. In: 2013 13th Iranian Conference on Fuzzy Systems (IFSC), pp. 1–5. IEEE (2013)Google Scholar
  12. 12.
    Bou-Ammar, H., Voos, H., Ertel, W.: Controller design for quadrotor uavs using reinforcement learning. In: 2010 IEEE International Conference on Control Applications (CCA), pp. 2130–2135. IEEE (2010)Google Scholar
  13. 13.
    Xian, B., Diao, C., Zhao, B., Zhang, Y.: Nonlinear robust output feedback tracking control of a quadrotor UAV using quaternion representation. Nonlinear Dyn. 79, 2735–2752 (2015)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Liu, H., Li, D., Xi, J., Zhong, Y.: Robust attitude controller design for miniature quadrotors. Int. J. Robust Nonlinear Control (2015)Google Scholar
  15. 15.
    Bresciani, T.: Modelling, Identification and Control of a Quadrotor Helicopter. Lund University, Department of Automatic Control, Lund (2008) Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mohammad Jafari
    • 1
  • Shamik Sengupta
    • 1
  • Hung Manh La
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of NevadaRenoUSA

Personalised recommendations