仿人机器人步态平衡泛化模型的建立与仿真

通过人体示教计算零力矩点(zero moment point, ZMP)，并通过补偿关节角度对其矫正的方法可以解决机器人步行不稳定的问题，但仍存在算法复杂度过高等问题。本文提出一种人体示教与机器学习相结合的方法，基于支持向量回归算法建立机器人的步态平衡泛化模型，通过该模型可以实现对模型输入人体示教的关节角度和ZMP信息后直接得到经稳定性补偿的关节角度，并以此驱动机器人完成步行动作。引入鲸鱼优化算法(whale optimization algorithm, WOA)优化模型的参数以使模型得到最优的泛化效果，完善步态平衡模型的性能。WEBOTS仿真平台下，使用模型输出的补偿后的关节角度驱动NAO机器人，其动作自然、稳定且算法复杂度较低，验证了本文方法的可行性。

Modeling and simulation of humanoid robot gait balance generalization

The problem of robot walking instability can be solved by calculating the zero-moment point (ZMP) through human body teaching and correction by the compensation of joint angles; however, problems such as high algorithm complexity still exist. This paper proposes a method that combines human teaching with machine learning. The gait balance generalization model of a robot is established based on the support vector regression algorithm. The joint angle of human teaching and ZMP information are inputted into the model; then, we get the joint angle compensated by stability, and the robot is driven to complete the walking action. The parameters of the whale optimization algorithm (WOA) model are introduced to make the model obtain the optimal generalization effect and improve the performance of the gait balance model. Under the Webots simulation platform, the NAO robot is driven by the compensated joint angle of the model output. The action is natural and stable, and the algorithm complexity is low, which verifies the feasibility of the method.