Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning to Move in Modular Robots using Central Pattern Generators and Online Optimization
International Journal of Robotics Research
A brainstem-like modulation approach for gait transition in a quadruped robot
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Gait transition and modulation in a quadruped robot: A brainstem-like modulation approach
Robotics and Autonomous Systems
CPG modulation for navigation and omnidirectional quadruped locomotion
Robotics and Autonomous Systems
| Hi-index | 0.00 |
The head shaking that results from robot locomotion is important because it difficults stable image acquisition and the possibility to rely on that information to act accordingly, for instance, to achieve visually-guided locomotion. In this article, we focus on the development of a head controller able to minimize the head motion induced by locomotion itself. Specifically, we propose a combined approach to generate head movement stabilization on a quadruped robot, using Central Pattern Generators (CPGs) and a genetic algorithm. Head movement is generated by CPGs which are modelled as autonomous differential equations. This approach allows to explicitly specify parameters such as amplitude, offset and frequency of movement and to smoothly modulate the generated trajectories according to changes in these parameters. It is therefore easy to combine the CPG with an optimization method. A genetic algorithm determines the best set of parameters that generates the head movement that reduces the head shaking caused by locomotion. Experimental results on a simulated AIBO robot demonstrate that the proposed approach generates head movement that does not eliminate but reduces the one induced by locomotion.