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Collision-free navigation of an autonomous unmanned helicopter in unknown urban environments: sliding mode and MPC approaches

Published online by Cambridge University Press:  25 July 2011

Michael Hoy ,
Alexey S. Matveev ,
Matt Garratt  and
Andrey V. Savkin
Michael Hoy*
Affiliation:
School of Electrical Engineering and Telecommunication, University of New South Wales,Sydney, Australia. E-mail: a.savkin@unsw.edu.au
Alexey S. Matveev
Affiliation:
Department of Mathematics and Mechanics, Saint Petersburg University, St. Petersburg, Russia. E-mail: almat1712@yahoo.com
Matt Garratt
Affiliation:
School of Aerospace, Civil and Mechanical Engineering, University of New South Wales at the Australian Defense Force Academy, Canberra, Australia. E-mail: m.garratt@adfa.edu.au
Andrey V. Savkin
Affiliation:
School of Electrical Engineering and Telecommunication, University of New South Wales,Sydney, Australia. E-mail: a.savkin@unsw.edu.au
*
*Corresponding author. E-mail: mch.hoy@gmail.com
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Summary

When employing autonomous helicopters, it is desirable to use navigation approaches, which firmly ensure safety. In this paper, we propose and compare two approaches to navigation through environments containing obstacles. The first uses sliding mode boundary following to maintain a prespecified distance to obstacles, and the second uses a model predictive control approach to plan short horizon trajectories around detected objects, while ensuring that the helicopter is brought to a halt within the sensor visibility radius. The navigation approaches are subjected to analysis for robustness, and simulations are carried out with a realistic helicopter model for verification. Additional real-world experiments were performed with a wheeled robot to demonstrate potential for real-time application.

Keywords

Path planningSliding mode controlRobot navigationUAV's, Collision avoidance
Type
Articles
Information
Robotica , Volume 30 , Issue 4 , July 2012 , pp. 537 - 550
Copyright
Copyright © Cambridge University Press 2011

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References

1.Aicardi, M., Casalino, G., Bicchi, A. and Balestrino, A., “Closed loop steering of unicycle like vehicles via lyapunov techniques,” IEEE Robot. Autom. Mag. 2 (1), 27–35 (1995).CrossRefGoogle Scholar
2.Fox, D., Burgard, W. and Thrun, S., “The dynamic window approach to collision avoidance,” IEEE Robot. Autom. Mag. 4 (1), 23–33 (1997).CrossRefGoogle Scholar
3.Garratt, M., “Biologically Inspired Vision and Control for an Autonomous Flying Vehicle,” Ph.D. dissertation (Australia: Australian National University, 2007).Google Scholar
4.Heffley, R. K., “Minimum-Complexity Helicopter Simulation Math Model, USAAVSCOM,” Technical Report, 87-a-7. Prepared for US Army Research and Technology Activity under contract NAS2-11665 (1988).Google Scholar
5.Adept Technology, Inc., Mobilerobots pioneer p3-dx (2011).Google Scholar
6.Kamon, I. and Rivlin, E., “Sensory-based motion planning with global proofs,” IEEE Trans. Robot. Autom. 13 (6), 814–822 (1997).CrossRefGoogle Scholar
7.Kozlowski, K., Robot Motion and Control (Springer-Verlag, Berlin, Heidelberg, 2009).Google Scholar
8.Kuwata, Y., Richards, A., Schouwenaars, T. and How, J. P., “Distributed robust receding horizon control for multivehicle guidance,” IEEE Trans. Control Syst. Technol. 15 (4), 627–641 (2007).CrossRefGoogle Scholar
9.LaValle, S. M. and Kuffner, J. J., “Randomized kinodynamic planning,” Int. J. Robot. Res. 20 (5), 378–400 (May 2001).CrossRefGoogle Scholar
10.Ahmed, B.Garratt, M. A. and Pota, H. R.. “Platform Enhancements and System Identification for Control of an Unmanned Helicopter,” Proceedings of the International Conference on Control, Automation, Robotics and Vision, Singapore (2006) pp. 1981–1986.Google Scholar
11.Manchester, I. R. and Savkin, A. V., “Circular navigation missile guidance with incomplete information and uncertainautopilot model,” J. Guid. Control Dyn. 27 (6), 1076–1083 (2004).CrossRefGoogle Scholar
12.Manchester, I. R. and Savkin, A. V., “Circular navigation guidance law for precision missile/target engagement,” J. Guid. Control Dyn. 29 (2), 1287–1292 (2006).CrossRefGoogle Scholar
13.Masoud, A., “Kinodynamic motion planning,” IEEE Robot. Autom. Mag. 17 (1), 85–99 (2010).CrossRefGoogle Scholar
14.Matveev, A. S., Teimoori, H. and Savkin, A. V., “A method for guidance and control of an autonomous vehicle in problems of border patrolling and obstacle avoidance,” Automatica 47 (3), 515–514 (2011).CrossRefGoogle Scholar
15.Mayne, D. Q., Rawlings, J. B., Rao, C. V. and Scokaert, P. O. M., “Constrained model predictive control: Stability and optimality,” Automatica 36, 789–814 (2000).CrossRefGoogle Scholar
16.Ogren, P. and Leonard, N. E., “A convergent dynamic window approach to obstacle avoidance,” IEEE Trans. Robot. 21 (2), 188–195 (2005).CrossRefGoogle Scholar
17.Park, J. M., Kim, D. W., Yoon, Y. S., Kim, H. J. and Yi, K. S., “Obstacle avoidance of autonomous vehicles based on model predictive control,” Proc. Inst. Mech. Eng. 223 (12), 1499–1516 (2009).CrossRefGoogle Scholar
18.Petti, S. and Fraichard, T., “Partial Motion Planning Framework for Reactive Planning within Dynamic Environments,” Proceedings of the IFAC/AAAI International Conference on Informatics in Control, Automation and Robotics, Barcelona (SP), September 2005.Google Scholar
19.Pozna, C., Troester, F., Precup, R. E., Tar, J. K. and Preitl, S., “On the design of an obstacle avoiding trajectory: Method and simulation,” Math. Comput. Simul. 79 (7), 2211–2226 (2009).CrossRefGoogle Scholar
20.Rakovic, S. V., Kerrigan, E. C., Kouramas, K. I. and Mayne, D. Q., “Invariant approximations of the minimal robust positively invariant set,” IEEE Trans. Autom. Control 50 (3), 406–410 (2005).CrossRefGoogle Scholar
21.Richards, A. and How, J., “Robust variable horizon model predictive control for vehicle maneuvering,” Int. J. Robust Nonlinear Control 16 (7), 333–351 (2006).CrossRefGoogle Scholar
22.Sasiadek, J. Z. and Duleba, I., “3D local trajectory planner for UAV,” J. Intell. Robot. Syst. 29 (2), 191–210 (2000).CrossRefGoogle Scholar
23.Shim, D. H., Chung, H. and Sastry, S. S., “Conflict-free navigation in unknown urban environments,” IEEE Robot. Autom. Mag. 13 (3), 27–33 (2006).CrossRefGoogle Scholar
24.Teimoori, H. and Savkin, A. V., “A biologically inspired method for robot navigation in a cluttered environment,” Robotica 28 (5), 637–648 (2010).CrossRefGoogle Scholar
25.Ulrich, I. and Borenstein, J., VFH*: Local Obstacle Avoidance with Look-ahead Verification,” Proceedings of the IEEE International Conference on Robotics and Automation (2000) San Francisco, CA, USA, vol. 3, pp. 2505–2511.Google Scholar
26.Yang, K., Gan, S. and Sukkarieh, S., “An efficient path planning and control algorithm for ruav's in unknown and cluttered environments,” J. Intell. Robot. Syst. 57 (1), 101–122 (2010).CrossRefGoogle Scholar