基于动态运动基元的轨迹学习方法

Trajectory Learning Based on Dynamic Movement Primitives

针对动态运动基元轨迹学习方法得到的学习轨迹终点值存在较大位置误差的问题,提出一种通过增大动态运动基元积分步数来减小位置误差的方法.通过以正弦轨迹、斜坡轨迹为示教轨迹的仿真实验验证了该方法的有效性.针对动态运动基元学习轨迹起始值与目标值相同时得到的学习轨迹恒为直线的问题,提出一种分段式轨迹学习方法,以轨迹极值点为分界点将学习轨迹分割为多段初始值与目标值不同的轨迹,通过仿真实验验证了该方法的有效性.

In the process of dynamic movement primitives(DMPs) based trajectory learning, the position error between the end point of the learned trajectory and that of the real trajectory is always large. For this problem, a new method is developed to reduce the position error by increasing the number of integral steps in DMPs. In simulation, both sine function and ramp function are used to construct teaching trajectories, respectively, to verify the effectiveness of the new method. When the start point of the learned trajectory is same as the target point, the learned trajectory is always a line trajectory based on DMP. In order to solve the problem, a segmented trajectory learning method is proposed. Specifically, the proposed method treats the extreme points of the trajectory as cut-off points, and then segments the learned trajectory into several new trajectories whose start points depart from the target points. Simulation results also verify the effectiveness of the proposed method.