T-S Fuzzy Contact State Recognition for Compliant Motion Robotic Tasks Using Gravitational Search-Based Clustering Algorithm

Jasim, Ibrahim; Plapper, Peter

Paper published in a book (Scientific congresses, symposiums and conference proceedings)

Jasim, Ibrahim; Plapper, Peter

2013 • In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013)

Peer reviewed

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https://hdl.handle.net/10993/7635

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Keywords :

Compliant motion robots; Contact state; Fuzzy clustering; Gravitational search

Abstract :

[en] In this paper, we address the problem of contact state recognition for compliant motion robotic systems. The wrench (Cartesian forces and torques) and pose (position and orientation) of the manipulated object in different Contact Formations (CFs) are firstly captured during a certain task execution. Then for each CF, we develop an efficient Takagi- Sugeno (T-S) fuzzy inference system that can model that specific CF using the available input (wrench and pose) - output (the desired model output for each CF) data. The antecedent part parameters are computed using the Gravitational Search- based Fuzzy Clustering Algorithm (GS- FCA) and the consequent parts parameters are tuned by the Least Mean Square (LMS). Excellent mapping and hence recognition capabilities can be expected from the suggested scheme. In order to validate the approach; experimental test stand is built which is composed of a KUKA Light Weight Robot (LWR) manipulating a cube rigid object that interacts with an environment composed of three orthogonal planes. The manipulated object is rigidly attached to the robot arm. The robot is programmed, by a human operator, to move in different CFs and for each CF, the wrench and pose readings are captured via the Fast Research Interface (FRI) available at the KUKA LWR. Using the suggested approach, excellent modeling is obtained for different CFs during the robot task execution. A comparison with the available CF recognition approaches is also performed and the superiority of the suggested scheme is shown.

Disciplines :

Mechanical engineering

Author, co-author :

Jasim, Ibrahim ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit

Plapper, Peter ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit

External co-authors :

Language :

English

Title :

T-S Fuzzy Contact State Recognition for Compliant Motion Robotic Tasks Using Gravitational Search-Based Clustering Algorithm

Publication date :

July 2013

Event name :

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013)

Event place :

Hyderabad, India

Event date :

07-07-2013 to 10-07-2013

Audience :

International

Main work title :

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013)

Peer reviewed :

Peer reviewed

FnR Project :

FNR2955286 - Self-adaptive Fuzzy Control For Robotic Peg-in-hole Assembly Process, 2011 (01/05/2012-30/04/2016) - Ibrahim Fahad Jasim Ghalyan

Name of the research project :

R-AGR-0071 - IRP13 - PROBE (20130101-20151231) - PLAPPER Peter

Funders :

FNR - Fonds National de la Recherche [LU]

Available on ORBilu :

since 04 October 2013

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Bibliography

R. S. Desai and R. A. Volz, "Identification and verification of termination conditions in fine motion in presence of sensor errors and geometric uncertainties," in Proc. 1989 IEEE Int. Conf. Robotics and Automation, Scottsdale, AZ, May 1989, pp. 800-807.
B. J. McCarragher, "Petri net modeling for robotic assembly and trajectory planning," IEEE Trans. Indust. Elect., vol. 41, no. 6, pp. 631-640, December, 1994.
D. J. Austin, and B. J. McCarragher, "Model-adaptive hybrid dynamic control for robotic assembly tasks," Int. J. Robot. Res., vol. 18, pp. 998-1012, October, 1999.
Y. Fei, and X. Zhao, "An assembly process modeling and analysis for robotic multiple peg-in-hole," Jour. Intell. Robot. Syst., vol. 36 pp. 175-189, 2003.
G. E. Hovland, and B. J. McCarragher, "Hidden Markov models as a process monitor in robotic assembly," Int. J. Robot. Res., vol. 17, no. 2, pp. 153-168, February, 1998.
H. Y. Lau, "A hidden Markov model-based assembly contact recognition," Mechatronics, vol. 13, pp. 1001-1023, 2003.
T. J. Debus, P. E. Dupot, and R. D. Howe, "Contact state estimation using multiple model estimation and Markov models," Int. J. Robot. Res., vol. 23, no. 4-5, pp. 399-413, April-May, 2004.
J. De Schutter, H. Bruyninckx, S. Dutré, J. De Geeter, J. Katupitiya, S. Demey, and T. Lefenvre, "Estimating first-order geometric parameters and monitoring contact transitions during force controlled compliant motions," Int. J. Robot. Res., vol. 18, no. 12, pp. 1161-1184, December, 1999.
W. Meeussen, J. Rutgeerts, K. Gadeyne, H. Bruyninckx, and J. De Schutter, "Contact-state segmentation using particle filters for programming by human demonstration in compliant-motion tasks," IEEE Trans. Robot., vol. 23, no. 2, pp. 218-231, April, 2007.
H. Okuda, H. Takeuchi, S. Inagaki, and T. Suzuki, "Modeling and analysis of peg-in-hole task based on mode segmentation," 2008 SICE Annual Conf., Tokyo, Japan, 20-22 August, 2008, pp. 1595-1600.
S. Cabras, M. E. Castellanos, and E. Staffetti, "Contact-state classification in human-demonstrated robot compliant motion tasks using the boosting algorithm," IEEE Trans. Syst. Man, Cybern. B, vol. 40, no. 5, pp. 1372-1386, October, 2010.
K. Hertkorn, M. A. Rao, C. Preusche, C. Borst, and G. Hirzinger, "Identification of contact formations: resolving ambiguous force torque information," in Proc. of the 2012 IEEE Int. Conf. Robot. and Autom., Saint Paul, Minnesota, USA, 14-18 May, 2012, pp. 3278-3284.
M. Skubic and R. A. Volz, "Learning force sensory patterns and skills from human demonstration," in Proc. of the 1997 IEEE Int. Conf. Robot. and Autom., New Mexico, 20-25 April, 1997, pp. 284-290.
L. J. Everett, R. Ravuri, R. A. Volz, and M. Skubic, "Generalized recognition of single - ended contact formations," IEEE Trans. Robot. and Autom., vol. 15, no. 5, pp. 829-836, October, 1999.
M. Skubic, S. P. Castrianni, and R. A. Volz, "Identifying contact formations from force signals: a comparison of fuzzy and neural network classifiers," in Proc. of the 1997 Int. Conf. on Neural Networks, Houston, TX, 9-12 June, 1997, pp. 1623-1628.
M. Skubic and R. A. Volz, "Identifying Single-Ended Contact Formations from Force Sensor Patterns," IEEE Trans. Robot. and Autom., vol. 16, no. 5, pp. 597-603, October, 2000.
L. Breiman, "Bagging predictors," Mach. Learn., vol. 26, issue 2, pp. 123-140, August, 1996.
A. Hatamlou, S. Abdullah, and H. Nesamabadi-pour, "Application of gravitational search algorithm on data clustering," Rough Sets and Knowledge Technology-Lect. Notes on Comput. Sci., vol. 6954, pp. 337-346, 2011.
C. Li, J. Zhou, B. Fu, P. Kou, and J. Xiao, "T-S fuzzy model identification with a gravitational search based hyperplane clustering algorithm," IEEE Trans. Fuzzy Syst., vol. 20, no. 2, pp. 305-317, April, 2012.
J. T. Spooner, M. Maggiore, R. Ordonez, and K. M. Passino. Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques. John Wiley & Sons, 2002.
S. Miyamoto, H. Ichihashi, and K. Honda. Algorithms for Fuzzy Clustering:Methods in c-Means Clustering with Applications. Springer, 2008.
T. A. Runkler, and C. Katz, "Fuzzy clustering by particle swarm optimization," in Proc. of the 2006 IEEE Int. Conf. on Fuzzy Syst., Vancouver, BC, Canada, 16-21 July, 2006, pp. 601-608.
E. Rashedi, H. Nesamabadi-pour, and S. Saryazdi, "GSA: A Gravitational Search Algorithm," Inf. Sci., vol. 179, no. 13, pp. 2232-2248, 2009.
E. Kim, M. Park, S. Ji, and M. Park, "A new approach to fuzzy modeling," IEEE Trans. Fuzzy Syst., vol. 5, no. 3, pp. 328-337, August, 1997.
J. Abonyi, R. Babuška, and F. Szeifert, "Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno Fuzzy Models," IEEE Trans. Syst. Man. Cyb. Part B:Cyb., vol. 32, no. 5, pp. 612-621, Oct., 2002.