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Cooperative UAV Formation Flying With Obstacle/Collision Avoidance | IEEE Journals & Magazine | IEEE Xplore

Cooperative UAV Formation Flying With Obstacle/Collision Avoidance


Abstract:

Navigation problems of unmanned air vehicles (UAVs) flying in a formation in a free and an obstacle-laden environment are investigated in this brief. When static obstacle...Show More

Abstract:

Navigation problems of unmanned air vehicles (UAVs) flying in a formation in a free and an obstacle-laden environment are investigated in this brief. When static obstacles popup during the flight, the UAVs are required to steer around them and also avoid collisions between each other. In order to achieve these goals, a new dual-mode control strategy is proposed: a "safe mode" is defined as an operation in an obstacle-free environment and a "danger mode" is activated when there is a chance of collision or when there are obstacles in the path. Safe mode achieves global optimization because the dynamics of all the UAVs participating in the formation are taken into account in the controller formulation. In the danger mode, a novel algorithm using a modified Grossberg neural network (GNN) is proposed for obstacle/collision avoidance. This decentralized algorithm in 2-D uses the geometry of the flight space to generate optimal/suboptimal trajectories. Extension of the proposed scheme for obstacle avoidance in a 3-D environment is shown. In order to handle practical vehicle constraints, a model predictive control-based tracking controller is used to track the references generated. Numerical results are provided to motivate this approach and to demonstrate its potential.
Published in: IEEE Transactions on Control Systems Technology ( Volume: 15, Issue: 4, July 2007)
Page(s): 672 - 679
Date of Publication: 25 June 2007

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Select All
1.
D. W. Casbeer, S.-M. Li, R. W. Beard, T. W. McLain and R. K. Mehra, "Forest fire monitoring using multiple small UAVs", Proc. ACC, pp. 3531-3535, 2005.
2.
D. H. A. Maithripala and S. Jayasuriya, "Radar deception through phantom track generation", Proc. ACC, pp. 4102-4106, 2005.
3.
J. Kim and J. P. Hespanha, "Cooperative radar jamming for groups of unmanned air vehicles", Proc. CDC, pp. 632-637, 2004.
4.
J. B. Saunders, B. Call, A. Curtis and R. W. Beard, "Static and dynamic obstacle avoidance inminiature air vehicles", AIAA Infotech@Aerosp. Conf., 2005.
5.
Y. Kuwata and J. How, Real-time trajectory design for unmanned aerial vehicles using receding horizon control, 2003.
6.
S. M. LaValle and J. J. Kuffner, "Rapidly-exploring random trees: Progress and prospects" in Algorithmic and Computational Robotics: New Directions, MA, Wellesley:A. K. Peters, pp. 293-308, 2001.
7.
D. B. Edwards, T. A. Bean, D. L. Odell and M. J. Anderson, "Leader-follower algorithm for multiple AUVformations", Proc. IEEE/OES Auton. Underwater Vehicles, pp. 40-46, 2004.
8.
S. Grossberg, "Nonlinear neural networks: Principles mechanismsarchitecture", Neural Netw., vol. 1, pp. 17-61, 1988.
9.
S. X. Yang, M. Meng and X. Yuan, "A biological inspired neural network approach to real-timecollision-free motion planning of a nonholonomic car-like robot", Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 239-244, 2000.
10.
S. M. Lavalle, Planning Algorithms, U.K., Cambridge:Cambridge Univ. Press, 2006.
11.
X. Wang and S. N. Balakrishnan, "Cooperative formation flyingwith hierarchical control", GNC, 2004.
12.
X. Wang and S. N. Balakrishnan, "Optimal and hierarchical formation control for UAVs (I)", Proc. ACC, pp. 4685-4689, 2005.
13.
M. Morari and J. H. Lee, "Model predictive control: Past presentand future", Comput. Chem. Eng., vol. 23, pp. 667-682, 1999.
14.
M. A. Henson, "Nonlinear model predictive control: Currentstatus and future directions", Comput. Chem. Eng., vol. 23, pp. 187-202, 1998.
15.
E. F. Camacho and C. Bordons, Model Predictive Control, New York:Springer, 1999.
16.
G. C. Goodwin, M. M. Seron and J. A. De Don, Constrained Control and Estimation: An Optimisation Approach, New York:Springer, 2004.
17.
A. E. Bryson and Y.-C. Ho, Applied Optimal Control: Optimization Estimation and Control, New York:Taylor Francis, 1975.
18.
T. Schouwenaars, M. Valenti, E. Feron and J. How, "Implementation and flight test results of MILP-based UAVguidance", Proc. IEEE Conf. Aerosp., pp. 1-13, 2005.
19.
M. de Berg, M. Van Kreveld and M. de Berg, Computational Geometry: Algorithms and Applications, New York:Springer-Verlag, 2000.
20.
M. A. Cohen and S. Grossberg, "Absolute stability of global pattern formationand parallel memory storage by competitive neural networks", IEEE Trans. Syst. Man Cybern., vol. SMC-13, pp. 815-826, 1983.
21.
J. Canny and J. Reif, "New lower bound techniques for robot motion planning problems", Proc. IEEE 28th Annu. Symp. Foundations Comput. Sci., pp. 49-60, 1987.
22.
L. P. Gewali and S. Ntafos, "Path planning in the presence of verticalobstacles", IEEE Trans. Robot. Autom., vol. 6, no. 3, pp. 331-341, Jun. 1990.
23.
Y. Kuwata and J. How, "Three dimensional receding horizon controlfor UAVs", AIAA Guid. Nav. Control Conf. Exhibit, 2004.
24.
V. Yadav, X. Wang and S. N. Balakrishnan, "Neural network approach for obstacle avoidance in threedemensional enviroments for UAVs", Proc. ACC, pp. 3667-3672, 2006.

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