Title |
Application of chaotic dynamics in a recurrent neural network to control: hardware implementation into a novel autonomous roving robot
|
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Published in |
Biological Cybernetics, September 2008
|
DOI | 10.1007/s00422-008-0249-6 |
Pubmed ID | |
Authors |
Yongtao Li, Shuhei Kurata, Shogo Morita, So Shimizu, Daigo Munetaka, Shigetoshi Nara |
Abstract |
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. |
Mendeley readers
Geographical breakdown
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France | 1 | 9% |
Brazil | 1 | 9% |
Unknown | 8 | 73% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 2 | 18% |
Professor | 1 | 9% |
Student > Doctoral Student | 1 | 9% |
Student > Master | 1 | 9% |
Other | 1 | 9% |
Unknown | 1 | 9% |
Readers by discipline | Count | As % |
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Business, Management and Accounting | 1 | 9% |
Agricultural and Biological Sciences | 1 | 9% |
Medicine and Dentistry | 1 | 9% |
Other | 1 | 9% |
Unknown | 1 | 9% |