Top > JACIII > Model Reference Control for Collision Avoidance of a Human-Ope ...

single-jc.php

JACIII Vol.15 No.5 pp. 617-623
doi: 10.20965/jaciii.2011.p0617
(2011)

Paper:

Views over last 60 days: 484

Model Reference Control for Collision Avoidance of a Human-Operated Quadrotor Helicopter

Busara Piriyanont*, Naoki Uchiyama**, and Shigenori Sano**

*School of Electrical Engineering and Computer Science, The University of Newcastle, University Drive, Callaghan, NSW 2308, Australia

**Toyohashi University of Technology, Japan, 1-1 Hibarigaoka, Tempaku, Toyohashi, Aichi 441-8580, Japan

Received:
November 22, 2010
Accepted:
March 22, 2011
Published:
July 20, 2011
Creative Commons License
Keywords:
quadrotor helicopter, collision avoidance, social force model, model reference control
Abstract
Because quadrotor helicopter has four fixed-pitch rotors, its control system can be much simpler than that of conventional helicopters; thus, it is expected to find wide application in many remote control situations, such as in hazardous environments. This paper proposes a collision avoidance control for a quadrotor helicopter based on the concept of a social force model. The proposed control incorporates commands given from human operators and compensates for operator mistakes in real time to achieve collision avoidance of a quadrotor helicopter. The proposed method uses distance sensors to achieve real-time collision avoidance. Its effectiveness is shown by experimental results, in which the algorithm successfully drives the helicopter along the desired trajectory without a collision.
Cite this article as:
B. Piriyanont, N. Uchiyama, and S. Sano, “Model Reference Control for Collision Avoidance of a Human-Operated Quadrotor Helicopter,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.5, pp. 617-623, 2011.
Data files:
References
  1. [1] V. Mistler, A. Benallegue, and N. K. M’Sirdi, “Exact Linearization and Noninteracting Control of a 4 Rotors Helicopter via Dynamic Feedback,” Proc. 10th IEEE Int. Workshop on Robot-Human Interactive Communication, pp. 586-593, 2001.
  2. [2] E. Altug, J. P. Ostrowski, and R. Mahony, “Control of a Quadrotor Helicopter Using Visual Feedback,” Int. Conf. on Robotics and Automation, pp. 72-77, 2002.
  3. [3] A. Mokhtari and A. Benallegue, “Dynamic Feedback Controller of Euler Angles and Wind Parameters Estimation for Quadrotor Unmanned Aerial Vehicle,” IEEE Int. Conf. on Robotics and Automation, pp. 2359-2366, 2004.
  4. [4] R. Lozano, P. Castillo, P. Garcia, and A. Dzul, “Robust Prediction-Based Control for Unstable Delay Systems: Application to the Yaw Control of a Mini-Helicopter,” Automatica, Vol.40, No.4, pp. 603-612, 2004.
  5. [5] S. Bouabdallah and R. Siegwart, “Backstepping and Sliding Mode Techniques Applied to an Indoor Micro Quadrotor,” Proc. IEEE Int. Conf. on Robotics and Automation, pp. 2259-2264, 2005.
  6. [6] S. L. Waslander, G. M. Hoffmann, J. S. Jang, and C. J. Tomlin, “Multi-Agent Quadrotor Testbed Control Design: Integral Sliding Mode vs. Reinforcement Learning,” IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 468-473, 2005.
  7. [7] L. C. Lai, C. C. Yang, and C. J. Wu, “Time-Optimal Control of a Hovering Quad-Rotor Helicopter,” J. of Intelligent and Robotic Systems, Vol.45, No.2, pp. 115-135, 2006.
  8. [8] J. Borenstein and Y. Koren, “The Vector Field Histogram - Fast Obstacle Avoidance for Mobile Robots,” IEEE Trans. Robotics and Automation, Vol.7, No.3, pp. 278-288, 1991.
  9. [9] D. Fox, W. Burgard, and S. Thrun, “The Dynamic Window Approach to Collision Avoidance,” IEEE Robotics and Automation Magazine, Vol.4, No.1, pp. 23-33, 1997.
  10. [10] P. Orgen and N. E. Leonard, “A Convergent Dynamic Window Approach to Obstacle Avoidance,” IEEE Trans. Robotics and Automation, Vol.21, No.2, pp. 188-195, 2005.
  11. [11] F. Lamiraux, D. Bonnafous, and O. Lefebvre, “Reaction Path Deformation for Nonholonomic Mobile Robots,” IEEE Trans. Robotics and Automation, Vol.20, No.6, pp. 967-977, 2004.
  12. [12] Y. Wang, I. P. W. Silliyoe, and D. J. Mulvanney, “Mobile Robot Path Planning in Dynamic Environments,” Proc. IEEE Int. Conf. Robotics and Automation, pp. 71-76, 2007.
  13. [13] M. Seer and I. Petrovic, “DynamicWindow Based Approach to Mobile Robot Motion Control in the Presence of Moving Obstacle,” Proc. IEEE Int. Conf. Robotics and Automation, pp. 1986-1999, 2007.
  14. [14] S. G. Vougioukas, “Reactive Trajectory Tracking for Mobile Robots Based on Non Linear Model Predictive Control,” Proc. IEEE Int. Conf. Robotics and Automation, pp. 3074-3079, 2007.
  15. [15] O. Khatib, “Real-Time Obstacle Avoidance for Manipulators and Mobile Robots,” Int. J. Robotics Research, Vol.5, No.1, pp. 90-98, 1986.
  16. [16] D. Helbing and P. Molnar, “Social Force Model for Pedestrian Dynamics,” Physical Review, E51, pp. 4282-4286, 1995.
  17. [17] T. I. Lakoba and D. J. Kaup, “Modification of the Helbing-Molnar-Farkas-Vicsek Social Force Model for Pedestrian Evolution,” The Society for Modeling and Simulation Int., Vol.81, No.5, pp. 339-352, 2005.
  18. [18] D. Helbing, I. Farkas, and T. Vicsek, “Simulating Dynamical Features of Escape Panic,” Nature, Vol.407, pp. 487-490, 2000.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Oct. 11, 2024