Application of chaotic dynamics in a recurrent neural network to control: hardware implementation into a novel autonomous roving robot
- 105 Downloads
Originating from a viewpoint that complex/chaotic dynamics would play an important role in biological system including brains, chaotic dynamics introduced in a recurrent neural network was applied to control. The results of computer experiment was successfully implemented into a novel autonomous roving robot, which can only catch rough target information with uncertainty by a few sensors. It was employed to solve practical two-dimensional mazes using adaptive neural dynamics generated by the recurrent neural network in which four prototype simple motions are embedded. Adaptive switching of a system parameter in the neural network results in stationary motion or chaotic motion depending on dynamical situations. The results of hardware implementation and practical experiment using it show that, in given two-dimensional mazes, the robot can successfully avoid obstacles and reach the target. Therefore, we believe that chaotic dynamics has novel potential capability in controlling, and could be utilized to practical engineering application.
KeywordsChaotic dynamics Autonomous robot Recurrent neural network Hardware implementation
Unable to display preview. Download preview PDF.
ESM 1 (MPG 2,275 kb)
ESM 2 (MPG 4,542 kb)
ESM 3 (MPG 4,324 kb)
- Blum L (1994) A theory of computation and complexity over the real numbers. In: Yamaguti M (ed) Towards the harnessing of chaos. Proceedings of the 7th Toyota Conference. Elsevier Science B.V., Amsterdam, pp 11–27Google Scholar
- Haken H (1996) Principles of brain functioning. Springer, BerlinGoogle Scholar
- Huber F, Thorson H (1985) Cricket auditory communication. Sci Am 253: 60–68Google Scholar
- Kuroiwa J, Nara S, Aihara K (1999) Functional possibility of chaotic behaviour in a single chaotic neuron model for dynamical signal processing elements. In: 1999 IEEE international conference on systems, man, and cybernetics (SMC’99), Tokyo, vol 1, p 290Google Scholar
- Manoonpong P, Pasemann F, Fischer J, Roth H (2005) Neural processing of auditory signals and modular neural control for sound tropism of walking machines. Int J Adv Robot Syst 2(3): 223–234Google Scholar
- Skarda CA, Freeman WJ (1987) How brains make chaos in order to make sense of the world. Behav Brain Sci 10: 161–195Google Scholar