基于质心校正补偿的仿人机器人模仿学习

由于仿人机器人自由度多、结构冗余,因此面对不同环境下的运动规划十分复杂.利用人体运动信息作为示教数据,实现仿人机器人对人体姿态的模仿学习,简化了仿人机器人的运动规划.为满足机器人在运动过程中的平衡性,提出了一种机器人质心补偿的方法:通过示教数据预估机器人的质心偏移,经质心-角度雅可比矩阵计算角度补偿量,并引入二次规划进行优化处理.基于Nao机器人的模仿学习系统实验研究结果表明:提出的质心补偿方法可以有效地保证机器人在模仿学习过程中的姿态平衡,引入的权值可调的二次规划有效地保证了姿态模仿的相似性.

Humanoid Robot Imitation Learning Based on COM Correction and Compensation

Since humanoid robot has high degrees of freedom and redundant structure, so the motion planning is complicated when facing different environments. In this paper, kinect was used to collect human motion information as teaching data to conduct the imitation of human motion. The motion planning of humanoid robot was simplified. To maintain the balance of robot in the process of motion,a method was proposed to compensate the robot' s center of mass ( COM): estimate the deviation of mass center through teaching dat was estimated, and the calculation of angle compensation through Jacobian was conducted and quadratic programming for optimization was optimized. The experiment of imitation system based on robot Nao indicates that the method of COM compensation ensures the balance in the process of imitation, the quadratic programming can effectively ensure the similarity of motion imitation.