Automated biped gait generetion for robot nao in webots simulator using a bio-inspired approach.

Abstract

This thesis presents a solution to the biped locomotion problem. The robot used for the experiments is robot Nao by Aldebaran Robotics and it is simulated in Webots mobile robot simulator. We provide a methodology for automated biped gait generation for a robot. The robot generates motions to move in its environment. The motions learned include a frontal walk and lateral left and right movement. Our method of solution does not requires the dynamic model of the robot, thus making this approach usable to other biped robots. For faster results the number of degrees of freedom is kept low, only six are used for generating a frontal locomotion. For lateral walk only four degrees of freedom are used. The walking gait is generated using Central Pattern Generators with limit-cycle oscillators. We suggest the use of Central Pattern Generators (CPGs) based on limit-cycle oscillators, together with Genetic Algorithms (GA). The CP G generates trajectories that are followed by the robot's actuators, and it is optimized by the use of GAs to find the best synchronization. Our solution is generated automatically and the best results for the frontal walk allow the robot to walk twice as fast as the Aldebaran's webots walk and four times faster than the default walk in Robotstadium.