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Head Motion Stabilization During Quadruped Robot Locomotion: Combining CPGs and Stochastic Optimization Methods

Head Motion Stabilization During Quadruped Robot Locomotion: Combining CPGs and Stochastic Optimization Methods

Miguel Oliveira, Cristina P. Santos, Lino Costa, Ana Maria A. C. Rocha, Manuel Ferreira
Copyright: © 2014 |Pages: 25
ISBN13: 9781466642539|ISBN10: 146664253X|EISBN13: 9781466642546
DOI: 10.4018/978-1-4666-4253-9.ch003
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MLA

Oliveira, Miguel, et al. "Head Motion Stabilization During Quadruped Robot Locomotion: Combining CPGs and Stochastic Optimization Methods." Natural Computing for Simulation and Knowledge Discovery, edited by Leandro Nunes de Castro, IGI Global, 2014, pp. 41-65. https://doi.org/10.4018/978-1-4666-4253-9.ch003

APA

Oliveira, M., Santos, C. P., Costa, L., Rocha, A. M., & Ferreira, M. (2014). Head Motion Stabilization During Quadruped Robot Locomotion: Combining CPGs and Stochastic Optimization Methods. In L. Nunes de Castro (Ed.), Natural Computing for Simulation and Knowledge Discovery (pp. 41-65). IGI Global. https://doi.org/10.4018/978-1-4666-4253-9.ch003

Chicago

Oliveira, Miguel, et al. "Head Motion Stabilization During Quadruped Robot Locomotion: Combining CPGs and Stochastic Optimization Methods." In Natural Computing for Simulation and Knowledge Discovery, edited by Leandro Nunes de Castro, 41-65. Hershey, PA: IGI Global, 2014. https://doi.org/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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