Scalar sensor-based adaptive manipulation for source seeking

Robotics and Automation
First Online:
Received:
Revised:
Accepted:

Abstract

This paper considers a class of robotic manipulation that can automatically trace an unknown source of a scalar field by sensors attached to links of a robotic manipulator. To achieve this, one approach is to model the field map by a radial basis function (RBF) network and to update its weights in a recursive way so that the gradient estimate can be available in realtime to command the end-effector toward the target source. In this paper, we investigate the practical implementation of this autonomous manipulation scheme and demonstrate its performance through experimental tests. Firstly, we provide a selection guideline for the Gaussian-type RBF network. Secondly, the field estimation algorithm is simplified to a suboptimal estimator instead of the original recursive least square (RLS) filter previously used. Thirdly, a cross-coupled parameter estimator is newly introduced for global convergence of the combined control law. The overall control scheme is experimentally demonstrated using a two link planar robot. A smooth gray scale map is devised to represent the unknown physical potential field with its scalar values measured by color sensors on robot links. The effect of under fitting of the field model is also investigated through the experimental results.

Keywords

Adaptive sensing autonomous manipulation scalar field source seeking 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    J. P. Laumond, “Kineo CAM: a success story of motion planning algorithms,” IEEE Robotics & Automation Magazine, vol. 13, no. 2, pp. 90–93, June 2006.CrossRefGoogle Scholar
  2. [2]
    L. E. Parker and J. V. Draper, “Robotics applications in maintenance and repair,” in Handbook of Industrial Robotics, 2nd edition, 1998.Google Scholar
  3. [3]
    K. Erik and L. Pål, and T. A. Andreas, “A robotic concept for remote inspection and maintenance on oil platforms,” Proc. of the ASME 28th International Conference on Ocean, Offshore and Arctic Engineering, Honolulu, Hawaii, USA, pp. 667–674, 2009.Google Scholar
  4. [4]
    J. Vertut and P. Coiffet, Teleoperation and Robotics: Applications and Technology, Prentice-Hall, Englewood Cliffs, NJ, 1985.CrossRefGoogle Scholar
  5. [5]
    J. Choi, S. Oh, and R. Horowitz, “Distributed learning and cooperative control for multi-agent systems,” Automatica, vol. 45, no. 12, pp. 2802–2814, 2009.CrossRefMATHMathSciNetGoogle Scholar
  6. [6]
    S. Martinez, “Distributed interpolation schemes for field estimation by mobile sensor networks,” IEEE Trans. on Control Systems Technology, vol. 18, no. 2, pp. 491–500, 2010.CrossRefGoogle Scholar
  7. [7]
    N. Leonard, D. A. Paley, F. Lekien, R. Sepulchre, D. M. Fratantoni, and R. E. Davis,, “Collective motion, sensor networks, and ocean sampling,” Proceedings of the IEEE, vol. 95, no. 1, pp. 48–74, 2007.CrossRefGoogle Scholar
  8. [8]
    M. Jadaliha, J. Lee, and J. Choi, “Adaptive control of multiagent systems for finding peaks of uncertain static fields,” ASME Journal of Dynamic Systems, Measurement, and Control, vol. 134, no. 5, 2012.Google Scholar
  9. [9]
    Y. Xu and J. Choi, “Stochastic adaptive sampling for mobile sensor networks using kernel regression,” International Journal of Control, Automation and Systems, vol. 10, no. 4, pp. 778–786, 2012.CrossRefGoogle Scholar
  10. [10]
    S. S. Sahyoun, S. M. Djouadi, H. Qi, and A. Drira, “Source localization using stochastic approximation and least squares methods,” Proc. of the 2nd Mediterranean Conference on Intelligent Systems and Automation, vol. 1107, pp. 59–64, 2009.Google Scholar
  11. [11]
    G. Ferri, M. V. Jakuba, E. Caselli, V. Mattoli, B. Mazzolai, D. R. Yoerger, and P. Dario, “Localizing multiple gas/odor sources in an indoor environment using Bayesian occupancy grid mapping,” Proc. of IEEE/RSJ International Conference on Intelligent Robotsand Systems, pp. 566–571, 2007.Google Scholar
  12. [12]
    S. Jeon, “Recursive field estimation and tracking for autonomous manipulation,” Robotica, vol. 30, no. 5, pp. 743–753, 2012.CrossRefGoogle Scholar
  13. [13]
    J. C. Latombe, Robot Motion Planning, Kluwer Academic Publishers, 1991.CrossRefGoogle Scholar
  14. [14]
    H. Choset, K. M. Lynch, S. Hutchinson, W. Burgard, L. E. Kavraki, and S. Thrun, Principles of Robot Motion, Addison-Wesley, England, 1999.Google Scholar
  15. [15]
    O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” The International Journal of Robotics Research, vol. 5, no. 1, pp. 90–98, 1986.CrossRefMathSciNetGoogle Scholar
  16. [16]
    C. Zhang, D. Arnold, N. Ghods, A. Siranosian, and M. Krstic, “Source seeking with nonholonomic unicycle without position measurement and with tuning of forward velocity,” Systems and Control Letters, vol. 56, pp. 245–252, 2007.CrossRefMATHMathSciNetGoogle Scholar
  17. [17]
    M. W. Spong, S. Hutchinson, and M. Vidyasagar, Robot Modeling and Control, John Wiley & Sons, Inc, 2006.Google Scholar
  18. [18]
    R. V. Patel and F. Shadpey, Control of Redundant Robot Manipulators: Theory and Experiments, Springer-Verlag GmbH, 2005.Google Scholar
  19. [19]
    M. R. Beychok, Fundamentals of Stack Gas Dispersion, 4th edition, author-published (http://www.air-dispersion.com/).
  20. [20]
    B. E. A. Saleh and M. C. Teich, Fundamentals of Photonics, John Wiley & Sons, 1991.CrossRefGoogle Scholar
  21. [21]
    R. M. Sanner and J. E. Slotine, “Gaussian networks for direct adaptive control,” IEEE Trans. on Neural Networks, vol. 3, no. 6, pp. 837–863, 1992.CrossRefGoogle Scholar
  22. [22]
    M. D. Buhmann, Radial Basis Functions, Cambridge University Press, 2003.CrossRefMATHGoogle Scholar
  23. [23]
    J. I. Mulero-Martínez, “Best approximation of Gaussian neural networks with nodes uniformly spaced,” IEEE Trans. on Neural Networks, vol. 19, no. 2, pp. 284–298, 2008.CrossRefGoogle Scholar
  24. [24]
    K. J. Åtröm and B. Wittenmark, Adaptive Control, 2nd edition, Addison-Wesley, 1995.Google Scholar
  25. [25]
    J. J. Slotine and W. Li, “On the Adaptive Control of Robot Manipulators,” International Journal of Robotics Research, vol. 6, no. 3, pp. 49–59, 1987.CrossRefGoogle Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Mechanical and Mechatronics EngineeringUniversity of WaterlooWaterlooCanada
  2. 2.Department of Mechanical EngineeringSoongsil UniversitySeoulKorea
  3. 3.Departments of Mechanical Engineering and Electrical and Computer EngineeringMichigan State UniversityEast LansingUSA

Personalised recommendations