Walking speed control in human behavior inspired gait generation system for biped robot

This paper proposes a gait generation system based on human behavior using biological approach. Humans have different gaits with different levels of speed or step. The proposed gait generator is able to generate the walking transition when the speed and the step length are changing dynamically. Neuron inter-connection structures as the locomotion model are formed. We apply evolutionary computation for each level of walking optimization. In locomotion generator, one joint angle is represented by two coupled neurons. Synaptic weights connected between ten motor neurons represent five joint angles and their gain values required to be optimized during several levels of speed. The optimized walking patterns are combined for acquiring dynamic relationship in one gait generator by using supervised multilayer perceptron (MLP) learning system. This gait generator uses optimized MLP weight parameters to generate synaptic weights transferred to locomotion generator depending on the desired walking speed. In order to prove the effectiveness of the model, we implemented it in computer simulation and in simple humanoid robot. The walking transitions depending on the changes in the walking speed are also shown. The smoothness of walking transition increased compared to previous researches.