Brain-like functor control machine for general humanoid biodynamics

International Journal of Mathematics and Mathematical Sciences

Volumn 2005, Issue 11, 2005, Pages 1759-1779

  Ivancevic, Vladimir ^a   Beagley, Nicholas ^a  

^a DEFENCE SCIENCE AND TECHNOLOGY ORGANISATION   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 27944506497     PISSN: 01611712     EISSN: None     Source Type: Journal    
DOI: 10.1155/IJMMS.2005.1759     Document Type: Article

References (35)

1. O. A. Bauchau and L. Trainelli, The vectorial parameterization of rotation, Nonlinear Dynam. 32 (2003), no. 1, 71-92.
2. A. Billard and M. Mataric, Learning human arm movements by imitation: Evaluation of a biologically-inspired connectionist architecture, Rob. Auton. Syst. 941 (2001), 1-16.
3. F. Crepel, N. Hemart, D. Jaillard, and H. Daniel, Long-term depression in the cerebellum, The Handbook of Brain Theory and Neural Networks (M. Arbib, ed.), MIT Press, Massachusetts, 2nd ed., 1998, pp. 560-563.
4. A. D'Souza, S. Vijayakumar, and S. Schaal, Learning inverse kinematics, Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'01) (Hawaii), vol. 1, October-November 2001, pp. 298-303.
5. O. Friberg, A set of parameters for finite rotations and translations, Comput. Methods Appl. Mech. Engrg. 66 (1988), 163-171.
6. H. Hatze, A general myocybernetic control model of skeletal muscle, Biol. Cyber. 28 (1978), no. 3, 143-157.
7. J. Hertzberg, How to plan under uncertainty, and use a logical action model, Modelling and planning for sensor based intelligent robot systems (Proc. of Dagstuhl Workshop, October, 1994) (H. Bunke, T. Kanade, and H. Noltemeier, eds.), Machine Perception and Artificial Intelligence, vol. 21, World Scientific, Singapore, 1995, pp. 160-175.
8. A. V. Hill, The heat of shortening and the dynamic constants of muscle, Proc. Roy. Soc. Ser. B 126 (1938), 136-195.
9. J. C. Houk, Regulation of stiffness by skeletomotor reflexes, Annu. Rev. Physiol. 41 (1979), 99-114.
10. J. C. Houk, J. T. Buckingham, and A. G. Barto, Models of the cerebellum and motor learning, Behav. Brain Sci. 19 (1996), no. 3, 368-383.
11. A. Isidori, Nonlinear Control Systems: An Introduction, 2nd ed., Springer, Berlin, 1989.
12. T. Ivancevic, L. C. Jain, and M. Bottema, A new two-feature FAM-matrix classifier for breast cancer diagnosis, Proc. 3rd International Conference on Knowledge-Based Intelligent Information Engineering Systems (KES '99), IEEE Press, Adelaide, September 1999, pp. 305-308.
13. T. Ivancevic, L. C. Jain, and M. Bottema, A new two-feature GBAM-neurodynamical classifier for breast cancer diagnosis, Proc. 3rd International Conference on Knowledge-Based Intelligent Information Engineering Systems (KES '99), IEEE Press, Adelaide, September 1999, pp. 296-299.
14. V. Ivancevic, Introduction to Biomechanical Systems: Modeling, Control and Learning, Scientific Book, Belgrade, 1991.
15. V. Ivancevic, Generalized Hamiltonian biodynamics and topology invariants of humanoid robots, Int. J. Math. Math. Sci. 31 (2002), no. 9, 555-565.
16. V. Ivancevic, Symplectic rotational geometry in human biomechanics, SIAM Rev. 46 (2004), no. 3, 455-474.
17. V. Ivancevic and C. E. M. Pearce, Poisson manifolds in generalized Hamiltonian biomechanics, Bull. Austral. Math. Soc. 64 (2001), 515-526.
18. V. Ivancevic, and C. E. M. Pearce, Topological duality in humanoid robot dynamics, ANZIAM J. 43 (2001), no. 2, 183-194.
19. V. Ivancevic and M. Snoswell, Fuzzy-stochastic functor machine for general humanoid-robot dynamics, IEEE Trans. Syst., Man, Cybern. B 31 (2001), no. 3, 319-330.
20. D. Katic and M. Vukobratovic, A neural network-based classification of environment dynamics models for compliant control of manipulation robots, IEEE Trans. Syst., Man, Cybern. B 28 (1998), no. 1, 58-69.
21. B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice-Hall, New Jersey, 1992.
22. C. Mladenova, Mathematical modelling and control of manipulator systems, Int. J. Robotics and Computer-Integrated Manufacturing 8 (1991), no. 4, 233-242.
23. C. D. Mladenova, Applications of Lie group theory to the modeling and control of multibody systems, Multibody Syst. Dyn. 3 (1999), no. 4, 367-380.
24. H. Nijmeijer and A. van der Schaft, Nonlinear Dynamical Control Systems, Springer, New York, 1990.
25. S. Schaal, Is imitation learning the route to humanoid robots?, Trends Cogn. Sci. 3 (1999), no. 6, 233-242.
26. S. Schaal and C. G. Atkeson, Constructive incremental learning from only local information, Neural Comput. 10 (1998), no. 8, 2047-2084.
27. H. Schaub, P. Tsiotras, and J. L. Junkins, Principal rotation representations of proper N × N orthogonal matrices, Internat. J. Engrg. Sci. 33 (1995), no. 15, 2277-2295.
28. H. Seraji, Configuration control of redundant manipulators: theory and implementation, IEEE Trans. Robot. Automat. 5 (1989), no. 4, 472-490.
29. M. D. Shuster, A survey of attitude representations, J. Astronaut. Sci. 41 (1993), no. 4, 439-517.
30. P. van der Smagt (ed.), Self-Learning Robots III Brainstyle Robotics: The Cerebellum Beyond Function Approximation, IEE, London, 1999.
31. M. Vukobratovic, B. Borovac, D. Surla, and D. Stokic, Biped Locomotion: Dynamics, Stability, Control, and Applications, Springer, Berlin, 1990.
32. D. E. Whitney, Historical perspective and state of the art in robot force control, Int. J. Robot. Res. 6 (1987), no. 1, 3-14.
33. D. R. Wilkie, The mechanical properties of muscle, Br. Med. Bull. 12 (1956), no. 3, 177-182.
34. T. Yoshikawa, Analysis and control of robot manipulators with redundancy, Robotics Research: The First International Symposium (M. Brady and R. Paul, eds.), MIT Press, Massachusetts, 1984, pp. 735-748.
35. E. Young, Mathematical twist reveals the agony of back pain, New Scientist (2003), August.