Adaptive neurofuzzy control of a robotic gripper with on-line machine learning

Robotics and Autonomous Systems

Volumn 48, Issue 2-3, 2004, Pages 93-110

  Domínguez López, Jorge A ^a   Damper, Robert I ^a   Crowder, Richard M ^a   Harris, Chris J ^a  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

Grasping; Intelligent control; Neurofuzzy systems; On line learning; Robotics

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; AEROSPACE APPLICATIONS; COMPUTER SIMULATION; DATA ACQUISITION; END EFFECTORS; FUZZY CONTROL; INTELLIGENT CONTROL; LEARNING SYSTEMS; MANIPULATORS; ONLINE SYSTEMS; ROBOTS; SENSORS;

GRASPING; NEUROFUZZY SYSTEMS; ON-LINE LEARNING; REINFORCEMENT LEARNING;

GRIPPERS;

EID: 4344647280     PISSN: 09218890     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.robot.2004.06.001     Document Type: Article

References (29)

1. A. Bicchi, Hands for dextrous manipulation and robust grasping: a difficult road toward simplicity, IEEE Trans. Robot. Autom. 16 (6) (2000) 652-662.
2. A.M. Okamura, N. Smaby, M.R. Cutkosky, An overview of dextrous manipulators, in: Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, CA, 2000, pp. 171-180.
3. M. Brown, C.J. Harris, Neurofuzzy Adaptive Modelling and Control, Prentice Hall, Hemel Hempstead, UK, 1994.
4. J-S.R. Jang, C-T. .Sun, E. Mizutani, Neuro-Fuzzy Modeling and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice-Hall, Englewood Cliffs, NJ, 1997.
5. M.M. Gupta, N.K. Sinha, Soft Computing and Intelligent Systems: Theory and Applications, Elsevier, Amsterdam, The Netherlands, 1999.
6. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, Upper Saddle River, NJ, 1999.
7. C. Harris, X. Hong, Q. Gan, Adaptive Modelling, Estimation and Fusion from Data: A Neurofuzzy Approach, Berlin and Heidelberg, Germany, Springer-Verlag, 2002.
8. V. Kecman, Learning and Soft Computing, MIT Press/Bradford Books, Cambridge, MA, 2001.
9. J.A. Domínguez-López, R.I. Damper, R.M. Crowder, C.J. Harris, Optimal object grasping using fuzzy logic, in: Proceedings of the IEEE International Conference on Robotics Vision Information and Signal Processing (ROVISP2003), Penang, Malaysia, 2003, pp. 367-372.
10. D.E. Rumelhart, G.E. Hinton, R. Williams, Learning representations by back-propagating errors, Nature 323 (9) (1986) 533-536.
11. Y. Chauvin, D. Rumelhart (Eds.), Backpropagation: Theories, Architectures and Applications, Lawrence Erlbaum Associates, Hillsdale, NJ, 1995.
12. R.S. Sutton, A.C. Barto, Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA, 2000.
13. C.J.C.H. Watkins, Learning from Delayed Rewards. Ph.D. thesis, University of Cambridge, UK, 1989.
14. L.P. Kaelbling, Foundations of learning in autonomous systems, Robot. Autonom. Syst. 8 (1991) 131-144.
15. R.S. Sutton, Reinforcement learning architectures for animals, in: J.-A. Meyer, S.W. Wilson (Eds.), From Animals to Animals: Proceedings of the 1st International Conference on Simulation of Adaptive Behavior, Bradford Books/MIT Press, Cambridge, MA, 1991, pp. 288-296.
16. C.J.C.H. Watkins, P. Dayan, Q-learning, Machine Learn. 8 (1992) 279-292.
17. R.M. Crowder, Sensors: Touch, force, and torque, in: R.L. Shell, E.L. Hall (Eds.), Handbook of Industrial Automation, New York, NY, Marcel Dekker, 2000, pp. 377-392 (Chapter 5.1).
18. C.W. de Suva, Applications of fuzzy logic in the control of robotic manipulators, Fuzzy Sets Systems 70 (2-3) (1995) 223-234.
19. W. Pedrycz, Computational Intelligence: An Introduction, Boca Raton, FL, CRC Press, 1998.
20. W.S. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophys. 5 (1943) 115-133.
21. D. Driankov, H. Hellendoorn, M. Reinfrank, An Introduction to Fuzzy Control, Springer-Verlag, London, UK, 1993.
22. J. Hertz, A. Krogh, R.G. Palmer, Introduction to the Theory of Neural Computation, Addison-Wesley, Reading, MA, 1991.
23. R.S. Sutton, Learning to predict by the methods of temporal differences, Machine Learn. 3 (1) (1988) 9-44.
24. H. Berenji, P. Khedkar, Learning and tuning fuzzy logic controllers through reinforcements, IEEE Trans. Neural Networks 3 (5) (1992) 724-740.
25. R.M. Kandadai, J.M. Tien, A knowledge-base generating hierarchical fuzzy-neural controller, IEEE Trans. Neural Networks 8 (6) (1997) 1531-1541.
26. A. Chatovich, S. Oktug, G. Dundar, Hierarchical neuro-fuzzy call admission controller for ATM networks, Comput. Commun. 24 (11) (2001) 1031-1044.
27. S. Tzafestas, E. Tzafestas, Computational intelligence techniques for short-term electric load forecasting, J. Intell. Robot. Syst. 31 (1-3) (2001) 7-68.
28. E. Bingham, Reinforcement learning in neurofuzzy traffic signal control, Eur. J. Operat. Res. 131 (2) (2001) 232-241.
29. V.N. Dubey, R.M. Crowder, P.H. Chappell, Optimal object grasp using tactile sensors and fuzzy logic, Robotica 17 (6) (1999) 685-693.