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.
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References
Sabattini, L.: Nonlinear Control Strategies for Cooperative Control of Multi-Robot Systems. Ph.D. thesis, Universitá di Bologna (2012)
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)
Dong, W., Guo, Y., Farrell, J.: Formation control of nonholonomic mobile robots. In: American Control Conference, pp. 5602–5607. IEEE (2006)
Speranzon, A.: On Control Under Communicaiton Constraints in Autonomous Multi-Robot Systems. KTH, Signals, Sensors and Systems, Stockholm (2004)
Arranz, L.B.: Cooperative control design for a fleet of AUVs under communication constraints. Ph.D. thesis, Université de Grenoble (2011)
Clark, J.D.: Cooperative hybrid control of robotic sensors for perimeter detection and tracking. Master’s thesis, Oklahoma State University (2005)
Olfati-Saber, R.: Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans. Autom. Control 51, 401–420 (2006)
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)
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)
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)
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)
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)
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)
Liu, H., Li, D., Xi, J., Zhong, Y.: Robust attitude controller design for miniature quadrotors. Int. J. Robust Nonlinear Control (2015)
Bresciani, T.: Modelling, Identification and Control of a Quadrotor Helicopter. Lund University, Department of Automatic Control, Lund (2008)
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Jafari, M., Sengupta, S., La, H.M. (2015). Adaptive Flocking Control of Multiple Unmanned Ground Vehicles by Using a UAV. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_58
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DOI: https://doi.org/10.1007/978-3-319-27863-6_58
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