Abstract Walking control is the key issue to motion control of humanoid robots. To achieve fast and stable gait, a humanoid robot spiral model algorithm based on the covariance matrix adaptation evolution strategy(CMA-ES) is proposed in this paper. In the walking optimization process, the optimization task is divided into three sub tasks, the parameters are selected according to the optimization goal to join the corresponding optimization group, and the CMA-ES optimizer is constructed. Each CMA-ES algorithm optimizer is designed according to different learning objectives. Based on the optimization results of the previous optimization group, the spiral iterative optimization is combined with new requirements, and finally the established learning objectives are achieved to obtain the optimal parameter values. The proposed algorithm is applied in the HfutEngine simulation 3D team. The relevant gait test data of the robot show the validity of the proposed algorithm.
Fund:Supported by Natural Science Foundation of Anhui Province(No.1708085MF146), Science and Technology Support Project of Sichuan Province(No.2016GZ0389), Project of Innovation Team of Ministry of Education of China(No.IRT17R32)
Corresponding Authors:
FANG Baofu, Ph.D., associate professor. His research inte-rests include multi-robot systems, emotional robots and machine learning.
About author:: LI Xiaoyu, master student. His research interests include intelligent computing and knowledge engineering;WANG Hao, Ph.D., professor. His research interests include intelligent computing theory and software, data mining, distributed intelligent systems and robot scomplex system theory and modeling.
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2018, Vol. 31 
