Abstract
This paper presents numerical and analytical methods for synthesis of a CPG network to acquire desired locomotor patterns. The CPG network is modeled as a chain of coupled Hopf oscillators with a coupling scheme that eliminates the influence of afferent signals on amplitude of the oscillator. The numerical method converts the related CPG parameters into dynamical systems that evolve as part of the CPG network dynamics. The frequency, amplitude and phase relations of teaching signals can be encoded by the CPG network with the proposed learning rules. For direct specification of the phase relations, the expression that defines the dependence of phase difference on coupling weights is analytically derived. The ability of the numerical methods to learn instructed locomotor pattern is proved with simulations. The effectiveness of the analytical method is also validated by the numerical results.
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Hu, Y., Zhao, W., Liang, J., Wang, T. (2012). Numerical and Analytical Methods for Synthesis of Central Pattern Generators. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7508. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33503-7_34
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DOI: https://doi.org/10.1007/978-3-642-33503-7_34
Publisher Name: Springer, Berlin, Heidelberg
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