Kicking motion planning of Nao robots based on CMA-ES

Abstract

A kicking design motion of humanoid robots is presented in this paper. This kicking design motion uses a gradual accumulation learning method which is based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). By planning the best kicking point and the foot space motion trajectory, the first layer of learning optimization can be realized using the linear distance after kicking and the time cost about kicking point as the target. Then, the optimization of the next layer was fulfilled by employing the double balancing mechanism of the robot's center of the gravity and the gyroscope sensor feedback. The learning goal was that the football contact point selection, the weighted penalty of the ankle joint and the performance of kicking were overall considered. The effectiveness of the proposed design method has been revealed in this paper through experimental results.