Acquisition of humanoid walking motion using genetic algorithm-Considering characteristics of servo modules

Abstract

This paper presents a method for humanoid walking acquisition with less energy consumption based on a two-stage genetic algorithm. In the first phase of genetic algorithm, in order to acquire the continuous walking motion, the fitness function consists of a walking distance (longer is better). In the second phase, the fitness function consists of a walking distance (longer) and energy consumption (less) for acquisition of highly energy-efficient walking. Further, we restrain the relationship among some joints and keep knee joint straight on supporting leg in order to ensure the less energy consumption. We apply the method to our platform PINO which has low-torque actuators owing to the servo modules. In order to realize a genetic process, we encode the scaling parameter of the joint movements and the phase difference between the joints into the computational simulation which considers the characteristics of the servo module used in our platform, PINO. The evolved results are applied to a real PINO and its smooth and stable walking with less energy consumption is verified.