Scalar sensor-based adaptive manipulation for source seeking
- 161 Downloads
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.
KeywordsAdaptive sensing autonomous manipulation scalar field source seeking
Unable to display preview. Download preview PDF.
- L. E. Parker and J. V. Draper, “Robotics applications in maintenance and repair,” in Handbook of Industrial Robotics, 2nd edition, 1998.Google Scholar
- 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
- 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
- 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
- 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
- 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
- M. W. Spong, S. Hutchinson, and M. Vidyasagar, Robot Modeling and Control, John Wiley & Sons, Inc, 2006.Google Scholar
- R. V. Patel and F. Shadpey, Control of Redundant Robot Manipulators: Theory and Experiments, Springer-Verlag GmbH, 2005.Google Scholar
- M. R. Beychok, Fundamentals of Stack Gas Dispersion, 4th edition, author-published (http://www.air-dispersion.com/).
- K. J. Åtröm and B. Wittenmark, Adaptive Control, 2nd edition, Addison-Wesley, 1995.Google Scholar