RoboCup3D仿真机器人步态优化研究

Gait Optimization Research on RoboCup3D Simulation

在RoboCup3D比赛中,拥有一个快速灵活、稳定的步态模式是赢得机器人足球比赛的关键之一。为了获得这样的步态模式,提出一种双足机器人垂直质心高度可变的机器学习训练方法。首先,通过规划双足机器人垂直质心高度的轨迹、利用倒立摆模型和数值化方法控制零力矩点,实现双足机器人的类人行走。然后,采用自适应协方差矩阵进化算法对步态参数优化,为了获得快速稳定的步行,采用累积分层的学习方法在之前优化的基础上进一步优化。最后,采用蜂拥编队壁障算法验证多机器人环境下优化步态的稳定性、灵活性。实验和竞赛结果均表明本文提出算法的有效性。

In the RoboCup3D competition,a flexible and stable gait pattern is one of the key for the humanoid robot to win the match,to achieve such walk gait,a machine learning method of optimizing the vertical Center of Mass(Com)trajectory is presented.Firstly,to generate bipedal walk slowly and unsteadily,the vertical Com trajectory is planed by multiple polynomial,the inverted pendulum model(IPM)and a numerical method are utilized to control the Zero Moment Point(ZMP).Secondly,to get a fast and stable bipedal walk,a overlapping layered learning method is proposed to optimize the walking parameters which is based on Covariance Matrix Adaptation Evolution Strategy(CMA-ES).Finally,flocking control is applied to verify the flexibilty of the optimized gait in multi-robot environment.The experimental and competition results show that the proposed method is effective.