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Gyroscopy and Navigation

, Volume 4, Issue 1, pp 14–25 | Cite as

Reactive navigation of a mobile robot using elliptic trajectories and effective online obstacle detection

  • J. Vilca
  • L. Adouane
  • Y. Mezouar
Article

Abstract

This paper deals with the problem of mobile robot navigation in cluttered environment. Adaptive elliptic trajectories are exploited for reactive obstacle avoidance using only position information and uncertain range data. The obstacle avoidance strategy used is based on the elliptic limit-cycle principle where each obstacle is surrounded by an ellipse. The ellipse parameters are computed online using a sequence of uncertain range data. An online heuristic method combined with the extended Kalman filter (EKF) is used to compute the ellipse parameters. It is demonstrated that this process ensures that all range data are surrounded by a computed ellipse. Moreover, this paper proposes a single control law to the multicontroller architecture where a reactive obstacle avoidance algorithm is embedded. The proposed control law is based on the Kanayama control law; it is designed to improve the performance of the controllers. The stability of this control architecture is proved according to the Lyapunov synthesis. Simulations and experiments in different environments have been performed to demonstrate the efficiency and reliability of the proposed online navigation in cluttered environment.

Keywords

Mobile Robot Obstacle Avoidance Control Architecture Mobile Robot Navigation Robot Trajectory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Latombe, J.C., Robot Motion Planning, Kluwer Academic Publishers, Boston, MA, 1991.CrossRefGoogle Scholar
  2. 2.
    Rimon, E. and Koditschek, D., Exact Robot Navigation Using Artificial Potential Fields, IEEE Transactions on Robotics and Automation, 1992, vol. 8, no. 5, pp. 501–518.CrossRefGoogle Scholar
  3. 3.
    Fraichard, T., Trajectory Planning in a Dynamic Workspace: a “State-Time Space” Approach, Advanced Robotics, 1999, vol. 13, no. 1, pp. 75–94.CrossRefGoogle Scholar
  4. 4.
    Jur-Van-Den, B., and Overmars, M., Roadmap-Based Motion Planning in Dynamic Environments, IEEE Transactions on Robotics, 2005, vol. 21(5), pp. 885–897.CrossRefGoogle Scholar
  5. 5.
    Egerstedt, M. and Hu, X., A Hybrid Control Approach to Action Coordination for Mobile Robots, Automatica, 2002, vol. 38(1), pp. 125–130.zbMATHCrossRefGoogle Scholar
  6. 6.
    Toibero, J., Carelli, R., and Kuchen, B., Switching Control of Mobile Robots for Autonomous Navigation in Unknown Environments, IEEE International Conference on Robotics and Automation, 2007, pp. 1974–1979.Google Scholar
  7. 7.
    Adouane, L., Hybrid and Safe Control Architecture for Mobile Robot Navigation, 9th Conference on Autonomous Robot Systems and Competitions, Portugal, May 2009.Google Scholar
  8. 8.
    Khatib, O., Real-Time Obstacle Avoidance for Manipulators and Mobile Robots, International Journal of Robotics Research, 1986, vol. 5, pp. 90–99.Google Scholar
  9. 9.
    Arkin, R. C., Motor Schema-based Mobile Robot Navigation, International Journal of Robotics Research, 1989, vol. 8, no. 4, pp. 92–112.CrossRefGoogle Scholar
  10. 10.
    Zapata, R., Cacitti, A., and Lepinay, P., DVZ-Based Collision Avoidance Control of Non-holonomic Mobile Manipulators, JESA, European Journal of Automated Systems, 2004, vol. 38(5), pp. 559–588.Google Scholar
  11. 11.
    Arkin, R.C., Behavior-Based Robotics, MIT Press, 1998.Google Scholar
  12. 12.
    De Luca, A. and Oriolo, G., Local Incremental Planning for Nonholonomic Mobile Robots, IEEE International Conference on Robotics and Automation, May 1994, vol. 1, pp. 104–110.Google Scholar
  13. 13.
    Kim D.-H. and Kim, J.-H., A Real-Time Limit-Cycle Navigation Method for Fast Mobile Robots and its Application to Robot Soccer, Robotics and Autonomous Systems, 2003, vol. 42(1), pp. 17–30.zbMATHCrossRefGoogle Scholar
  14. 14.
    Jie, M.S., Baek, J.H., Hong, Y.S., and Lee, K.W., Real Time Obstacle Avoidance for Mobile Robot Using Limit-Cycle and Vector Field Method, Knowledge-Based Intelligent Information and Engineering Systems, October 2006.Google Scholar
  15. 15.
    Adouane, L., Orbital Obstacle Avoidance Algorithm for Reliable and On-Line Mobile Robot Navigation, 9th Conference on Autonomous Robot Systems and Competitions, May 2009, Portugal.Google Scholar
  16. 16.
    Adouane, L., Benzerrouk, A., and Martinet, P., Mobile Robot Navigation in Cluttered Environment Using Reactive Elliptic Trajectories, 18th IFAC World Congress, August 2011.Google Scholar
  17. 17.
    Benzerrouk, A., Adouane, L., and Martinet, P., Lyapunov Global Stability for a Reactive Mobile Robot Navigation in Presence of Obstacles,” ICRA’10 International Workshop on Robotics and Intelligent Transportation System, 2010.Google Scholar
  18. 18.
    Kanayama, Y., Kimura, Y., Miyazaki, F., and Noguchi, T., A Stable Tracking Control Method for an autonomous mobile robot, Proceedings of the IEEE International Conference on Robotics and Automation, May 1990, pp. 384–389.Google Scholar
  19. 19.
    Welzl, E., Smallest Enclosing Disks (Balls and Ellipsoids), Results and New Trends in Computer Science, Springer-Verlag, 1991, pp. 359–370.Google Scholar
  20. 20.
    Zhang, Z., Parameter Estimation Techniques: A Tutorial with Application to Conic Fitting, Image and Vision Computing, 1997, vol. 15, pp. 59–76.CrossRefGoogle Scholar
  21. 21.
    Vilca, J., Adouane, L., and Mezouar, Y., On-Line Obstacle Detection Using Data Range for Reactive Obstacle Avoidance, 12th International Conference on Intelligent Autonomous Systems, Korea, June 2012.Google Scholar
  22. 22.
    Xiong, K., Wei, C., and Liu, L., Robust Kalman Filtering for Discrete-Time Nonlinear Systems with Parameter Uncertainties, Aerospace Science and Technology, 2011.Google Scholar
  23. 23.
    Fouque, C., Bonnifait, P., and Betaille, D., Enhancement of Global Vehicle Localization Using Navigable Road Maps and Dead-Reckoning, IEEE Position Location and Navigation Symposium, 2008.Google Scholar
  24. 24.
    Rigatos, G.G., Extended Kalman and Particle Filtering for Sensor Fusion in Motion Control of Mobile Robots, Mathematics and Computers in Simulation, November 2010, vol. 81, no. 3, pp. 590–607.MathSciNetzbMATHCrossRefGoogle Scholar
  25. 25.
    Levinson, J. and Thrun, S., Robust Vehicle Localization in Urban Environments Using Probabilistic Maps, IEEE International Conference on Robotics and Automation, Alaska, USA, May 2010.Google Scholar
  26. 26.
    Porrill, J., Fitting Ellipses and Predicting Confidence Envelopes Using a Bias Corrected Kalman Filter, Image and Vision Computing, February 1990, vol. 8, no. 1, pp. 37–41.CrossRefGoogle Scholar
  27. 27.
    Vilca, J., Adouane, L., and Mezouar, Y., Robust Online Obstacle Detection Using Range Data for Reactive Navigation, 10th International IFAC Symposium on Robot Control, Croatia, September 2012.Google Scholar
  28. 28.
    Brooks, R.A., A Robust Layered Control System for a Mobile Robot, IEEE Journal of Robotics and Automation, vol. RA-2, March 1986, pp. 14–23.MathSciNetCrossRefGoogle Scholar
  29. 29.
    Adouane, L. and Le Fort-Piat, N., Behavioral and Distributed Control Architecture of Control for Minimalist Mobile Robots, Journal Europen des Systèmes Automatisés, 2006, vol. 40, no. 2, pp. 177–196.CrossRefGoogle Scholar
  30. 30.
    Maalouf, E., Saad, M., and Saliah, H., A Higher Level Path Tracking Controller for a Four-Wheel Differentially Steered Mobile Robot, Robotics and Autonomous Systems, 2006, vol. 54, pp. 23–33.CrossRefGoogle Scholar
  31. 31.
    De Maesschalck, R., Jouan-Rimbaud, D., and Massart, D., The Mahalanobis Distance, Chemometrics and Intelligent Laboratory Systems, 2000, vol. 50, no. 1, pp. 1–18.CrossRefGoogle Scholar
  32. 32.
    Barshan, B. and Kuc, R., Active Sonar for Obstacle Localization Using Envelope Shape Information, International Conference on Acoustics, Speech, and Signal Processing, April 1991, vol. 2, pp. 1273–1276.Google Scholar
  33. 33.
    Burguera, A., Gonzlez, Y., and Oliver, G., Sonar Sensor Models and Their Application to Mobile Robot Localization, Sensors, December 2009, vol. 9, no. 12, pp. 10217–10243.CrossRefGoogle Scholar
  34. 34.
    Khalil, H.K., Nonlinear Systems, 3rd ed., P. Hall, Ed., 2002.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2013

Authors and Affiliations

  1. 1.Institut Pascal, UBP — UMR CNRS 6602Clermont-FerrandFrance

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