On evolving a dynamic bipedal walk using Partial Fourier Series

Abstract

The paper presents a Partial Fourier Series (PFS) based bipedal gait in sagittal and transverse planes. The parameters of the Fourier series are optimized through Evolutionary Algorithms (EA). In addition to evolving the two walks (forward and turn) separately, the paper demonstrates how the combination of the two enables a dynamic and adjustable walk. The stability of the walk is ensured through an effective use of the built-in gyroscope sensor. The evolved walk has been tested on the simulated version of the humanoid Nao robot and is being used within the RoboCup Soccer 3D Simulation competition.