An evolutionary gait generator with online parameter adjustment for humanoid robots

This article proposes a new hybrid methodology, together with an associated series of experiments employing this methodology, for an evolutionary gait generator that uses trigonometric truncated Fourier series formulations with coefficients optimized by a Genetic Algorithm. The Fourier series is used to model joint angle trajectories of a simulated humanoid robot with 25 degrees of freedom. The humanoid robot in this study learns to imitate the human walking behavior on flat terrains in a dynamically simulated environment. The simulation result shows the robustness of the developed walking behaviors even in extremely high and low speeds providing appropriate frequency. Number of range limitations were applied to the genetic algorithm used in this research to improve the learning period to less than 48 hours. The research seeks to improve upon the previous works on evolutionary gait generation, in robots with lower degrees of freedom. In addition, the proposed solution adapts a hybrid approach, thereby avoiding the long learning curves and unstable and slow gaits associated with evolutionary approaches.