Autonomous Robots

, Volume 33, Issue 1–2, pp 143–156 | Cite as

Trajectory design and control for aggressive formation flight with quadrotors

Article

Abstract

In this work we consider the problem of controlling a team of micro-aerial vehicles moving quickly through a three-dimensional environment while maintaining a tight formation. The formation is specified by shape vectors which prescribe the relative separations and bearings between the robots. To maintain the desired shape, each robot plans its trajectory independently based on its local information of other robot plans and estimates of states of other robots in the team. We explore the interaction between nonlinear decentralized controllers, the fourth-order dynamics of the individual robots, time delays in the network, and the effects of communication failures on system performance. Simulations as well as an experimental evaluation of our approach on a team of quadrotors suggests that suitable performance is maintained as the formation motions become increasingly aggressive and as communication degrades.

Keywords

Micro-aerial vehicles Formation control Finite horizon control 

References

  1. Beard, R. W., Lawton, J., & Hadaegh, F. Y. (2001). A coordination architecture for spacecraft formation control. IEEE Transactions on Control Systems Technology, 9(6), 777–790. CrossRefGoogle Scholar
  2. Desai, J. P., Ostrowski, J. P., & Kumar, V. (2001). Modeling and control of formations of nonholonomic mobile robots. IEEE Transactions on Robotics, 17(6), 905–908. CrossRefGoogle Scholar
  3. Egerstedt, M., & Hu, X. (2001). Formation constrained multi-agent control. IEEE Transactions on Robotics and Automation, 17(6), 947–951. CrossRefGoogle Scholar
  4. Fax, J. A., & Murray, R. M. (2004). Information flow and cooperative control of vehicle formations. IEEE Transactions on Automatic Control, 49(9), 1465–1476. MathSciNetCrossRefGoogle Scholar
  5. Franchi, A., Giordano, P., Secchi, C., Son, H., & Bülthoff, H. (2011). A passivity-based decentralized approach for the bilateral teleoperation of a group of uavs with switching topology. In Proc. IEEE intl conf. on robotics & automation. Google Scholar
  6. Gu, Y., Seanor, B., Campa, G., Napolitano, M. R., Rowe, L., Gururajan, S., & Wan, S. (2006). Design and flight testing evaluation of formation control laws. IEEE Transactions on Control Systems Technology, 14(6), 1105–1112. CrossRefGoogle Scholar
  7. Jadbabaie, A., Lin, J., & Morse, A. S. (2003). Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Transactions on Automatic Control, 48(6), 988–1001. MathSciNetCrossRefGoogle Scholar
  8. Jadbabaie, A., Yu, J., & Hauser, J. (2001). Unconstrained receding-horizon control of nonlinear systems. IEEE Transactions on Automatic Control, 46(5), 776–783. MathSciNetMATHCrossRefGoogle Scholar
  9. Keviczky, T., & Johansson, K. (2008). A study on distributed model predictive consensus. arXiv:0802.4450.
  10. Latouche, G., & Ramaswami, V. (1999). Introduction to matrix analytic methods in stochastic modeling. Philadelphia: ASA-SIAM. MATHCrossRefGoogle Scholar
  11. Lee, T. (2011). Geometric tracking control of the attitude dynamics of a rigid body on SO(3). In Proc. of the Amer. control conf., San Francisco, CA. Google Scholar
  12. Lee, T., Leok, M., & McClamroch, N. H. (2010). Geometric tracking control of a quadrotor UAV on SE(3). In Proc. of the IEEE conf. on decision and control, Atlanta, GA. Google Scholar
  13. Mellinger, D., & Kumar, V. (2011). Minimum snap trajectory generation and control for quadrotors. In Proc. of the IEEE intl. conf. on robot. and autom., Shanghai, China. Google Scholar
  14. Mellinger, D., Michael, N., & Kumar, V. (2010). Trajectory generation and control for precise aggressive maneuvers with quadrotors. In Proc. of the intl. sym. on exp. robot., Delhi, India. Google Scholar
  15. Mesbahi, M. (2005). On state-dependent dynamic graphs and their controllability properties. IEEE Transactions on Automatic Control, 50(3), 387–392. MathSciNetCrossRefGoogle Scholar
  16. Michael, N., Mellinger, D., Lindsey, Q., & Kumar, V. (2010). The GRASP multiple micro UAV testbed. IEEE Robotics & Automation Magazine, 17(3), 56–65. CrossRefGoogle Scholar
  17. Nieuwstadt, M. J. V., & Murray, R. M. (1998). Real-time trajectory generation for differentially flat systems. International Journal of Robust and Nonlinear Control, 8(11), 995–1020. MathSciNetMATHCrossRefGoogle Scholar
  18. Ogren, P., Fiorelli, E., & Leonard, N. (2002). Formations with a mission: stable coordination of vehicle group maneuvers. In Proc. of intl. sym. on mathematical theory networks and syst., Notre Dame, IN. Google Scholar
  19. Olfati-Saber, R., & Murray, R. M. (2002). Distributed cooperative control of multiple vehicle formations using structural potential functions. In Proc. of the IFAC world congress, Barcelona, Spain. Google Scholar
  20. Olfati-Saber, R., & Murray, R. M. (2004). Consensus problems in networks of agents with switching topology and time-delays. IEEE Transactions on Automatic Control, 49(9), 1520–1533. MathSciNetCrossRefGoogle Scholar
  21. Shim, D., Kim, H., & Sastry, S. (2003). Decentralized nonlinear model predictive control of multiple flying robots. In Decision and control, 2003. Proceedings. 42nd IEEE conference on (Vol. 4, pp. 3621–3626). New York: IEEE. Google Scholar
  22. Tabuada, P., Pappas, G. J., & Lima, P. (2001). Feasible formations of multi-agent systems. In Proc. of the Amer. control conf., Arlington, VA (pp. 56–61). Google Scholar
  23. Tanner, H., Pappas, G. J., & Kumar, V. (2002). Input-to-state stability on formation graphs. In Proc. of the IEEE intl. conf. on robot. and autom., Las Vegas, NV (pp. 2439–2444). Google Scholar
  24. Turpin, M., Michael, N., & Kumar, V. (2011). Trajectory design and control for aggressive formation flight with quadrotors. In Proc. of the intl. sym. of robotics research, Flagstaff, AZ. Google Scholar
  25. Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I., & Shochet, O. (1995). Novel type of phase transition in a system of self-driven particles. Physical Review Letters, 75(6), 1226–1229. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.GRASP LaboratoryUniversity of PennsylvaniaPhiladelphiaUSA

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