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.
|Number of pages||11|
|Journal||International Journal of Control, Automation and Systems|
|Publication status||Published - 2014 Feb|
Bibliographical noteFunding Information:
The work in this paper is in part supported by Natural Sciences and Engineering Research Council (NSERC) of Canada (granted to the first author) and by the National Science Foundation through CAREER Award CMMI-0846547 (granted to the third author). These supports are gratefully acknowledged. The second author also acknowledges the financial support from the MSIP (Ministry of Science, ICT & Future Planning), Korea, under the Convergence-ITRC (Convergence Information Technology Research Center) support program (NIPA-2013-H0401-13-1004) supervised by the NIPA.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computer Science Applications