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Head Motion Stabilization During Quadruped Robot Locomotion: Combining CPGs and Stochastic Optimization Methods
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Author(s): Miguel Oliveira (University of Minho, Portugal), Cristina P. Santos (University of Minho, Portugal), Lino Costa (University of Minho, Portugal), Ana Maria A. C. Rocha (University of Minho, Portugal)and Manuel Ferreira (University of Minho, Portugal)
Copyright: 2014
Pages: 25
Source title:
Natural Computing for Simulation and Knowledge Discovery
Source Author(s)/Editor(s): Leandro Nunes de Castro (Mackenzie University, Brazil)
DOI: 10.4018/978-1-4666-4253-9.ch003
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Abstract
In this work, the authors propose a combined approach based on a controller architecture that is able to generate locomotion for a quadruped robot and a global optimization algorithm to generate head movement stabilization. The movement controllers are biologically inspired in the concept of Central Pattern Generators (CPGs) that are modelled based on nonlinear dynamical systems, coupled Hopf oscillators. This approach allows for explicitly specified parameters such as amplitude, offset and frequency of movement and to smoothly modulate the generated oscillations according to changes in these parameters. The overall idea is to generate head movement opposed to the one induced by locomotion, such that the head remains stabilized. Thus, in order to achieve this desired head movement, it is necessary to appropriately tune the CPG parameters. Three different global optimization algorithms search for this best set of parameters. In order to evaluate the resulting head movement, a fitness function based on the Euclidean norm is investigated. Moreover, a constraint-handling technique based on tournament selection was implemented.
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