Research Article
Gait Optimization for Multiple Humanoid Robots Based on Parallel Multi-swarm Particle Swarm Algorithm
@INPROCEEDINGS{10.4108/eai.7-11-2017.2274577, author={Chunguang li and Rongyi He and Lina Yao and Chongben Tao}, title={Gait Optimization for Multiple Humanoid Robots Based on Parallel Multi-swarm Particle Swarm Algorithm}, proceedings={14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services}, publisher={ACM}, proceedings_a={MOBIQUITOUS}, year={2018}, month={4}, keywords={humaniod robot multi-swarm particle swarm optimization gait optimization distributed system robocup}, doi={10.4108/eai.7-11-2017.2274577} }- Chunguang li
Rongyi He
Lina Yao
Chongben Tao
Year: 2018
Gait Optimization for Multiple Humanoid Robots Based on Parallel Multi-swarm Particle Swarm Algorithm
MOBIQUITOUS
ACM
DOI: 10.4108/eai.7-11-2017.2274577
Abstract
In the RoboCup 3D simulation competition, how to find a flexible and stable gait pattern is one of the keys to win the match. To achieve such walking gait, a machine learning method of optimizing the vertical Center of Mass(CoM) trajectory is presented. The vertical CoM trajectory is planned by multiple polynomial function. Inverted Pendulum Model(IPM) and a numerical method are utilized to control the Zero Moment Point(ZMP). Then the key parameters are extracted from the gait pattern, a distributed multi-robot training environment based on RoboCup 3D simulated platform is constructed, and the parallel multi-swarm particle swarm algorithm is applied to optimize the parameters. The results of experiment and competition demonstrate that the effectiveness of the proposed method.
