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.
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Recommended by Associate Editor Youngjin Choi under the direction of Editor Hyouk Ryeol Choi.
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.
Soo Jeon received his Ph.D. degree in Mechanical Engineering from the University of California at Berkeley in 2007. He also received his B.S. and M.S. degrees from Seoul National University in 1998 and 2001, respectively. He worked as a Mechanical and Systems Engineer at Applied Materials, Inc., San Jose before he joined the University of Waterloo, Canada in 2009 as an assistant professor in the Department of Mechanical and Mechatronics Engineering. His research interests include sensor fusion, friction-induced nonlinear dynamics of controlled mechanical systems and the biological-inspired control for dexterous manipulation. He is a member of American Society of Mechanical Engineers (ASME) and received the Rudolf Kalman Best Paper Award from Dynamic Systems and Control Division (DSCD) of ASME in 2010.
Hyeong-Joon Ahn received his B.S., M.S. and Ph.D. degrees from Seoul national university in 1995, 1997 and 2001, respectively. Dr. Ahn is currently an associate professor at the Department of Mechanical Engineering, Soongsil University. Dr. Ahn’s research interests are in the area of mechatronics including sensors, actuators, control and precision machine design.
Jongeun Choi received his Ph.D. and M.S. degrees in Mechanical Engineering from the University of California at Berkeley, in 2006 and 2002, respectively. He also received a B.S. degree in Mechanical Design and Production Engineering from Yonsei University at Seoul, Korea in 1998. He is currently an Associate Professor with the Departments of Mechanical Engineering and Electrical and Computer Engineering at the Michigan State University. His current research interests include systems and control, system identification, and Bayesian methods, with applications to mobile robotic sensors, environmental adaptive sampling, engine control, neuromusculoskeletal systems, and biomedical problems. He is an Associate Editor for Journal of Dynamic Systems, Measurement and Control. His papers were finalists for the Best Student Paper Award at the 24th American Control Conference (ACC) 2005 and the Dynamic System and Control Conference (DSCC) 2011 and 2012. He is a recipient of an NSF CAREER Award in 2009. Dr. Choi is a member of ASME.
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Jeon, S., Ahn, HJ. & Choi, J. Scalar sensor-based adaptive manipulation for source seeking. Int. J. Control Autom. Syst. 12, 126–136 (2014). https://doi.org/10.1007/s12555-012-0429-y
- Adaptive sensing
- autonomous manipulation
- scalar field
- source seeking