Abstract—Controlling a biped robot with a high degree of
freedom to achieve stable, straight and fast movement patterns
is one of the most complex problems. With growing
computational power of computer hardware, simulation of such
robots in high resolution real time environment has become
more applicable. This paper introduces a novel approach to
Generate Bipedal gait for humanoid locomotion. In this scene,
first we have used a modified Truncated Fourier Series (TFS) to
generate angular trajectories, then to find the best angular
trajectory we built an improved Genetic Algorithm (GA). One
of the major difficulties of GAs is choosing appropriate values
for mutation and crossover parameters. Hence, we present
GALA (Genetic Algorithm parameters adaption using
Learning Automata) to adjust these parameters by recruiting
Learning Automata. As results show, my approach could
generate better values for angular trajectories for biped
walking, hence in my approach the robot could walk with high
stability and faster than other approaches. Evaluations
performed on Simulated NAO robot in RoboCup 3D soccer
simulation environment.
Index Terms—Bipedal locomotion, learning automata,
genetic algorithm, truncated Fourier series.
O. Mohamad Nezami is with the Bijar Branch, Islamic Azad University,
Bijar, Iran (e-mail: omid.mnezami@gmail.com).
M. R. Meybodi is with the Computer Engineering Department of
Amirkabir University of Technology, Tehran, Iran (e-mail:
meybodi@ce.aut.ac.ir).
Cite:Omid Mohamad Nezami and Mohammad Reza Meybodi, "Biped Robot Walking using a Combination of Truncated Fourier Series and GALA (Genetic Algorithm parameters adaption using Learning Automata)," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 598-603, 2012.