Dynamic movement primitives are widely used for learning skills which can be
demonstrated to a robot by a skilled human or controller. While their
generalization capabilities and simple formulation make them very appealing to
use, they possess no strong guarantees to satisfy operational safety
constraints for a task. In this paper, we present constrained dynamic movement
primitives (CDMP) which can allow for constraint satisfaction in the robot
workspace. We present a formulation of a non-linear optimization to perturb the
DMP forcing weights regressed by locally-weighted regression to admit a Zeroing
Barrier Function (ZBF), which certifies workspace constraint satisfaction. We
demonstrate the proposed CDMP under different constraints on the end-effector
movement such as obstacle avoidance and workspace constraints on a physical
robot. A video showing the implementation of the proposed algorithm using
different manipulators in different environments could be found here
this https URL.

本文提出了一种基于动态动作原理的运动控制方法 CDMP，通过非线性优化来实现约束条件（如障碍物避让、工作空间限制）的满足，并在不同机器人以及环境下验证了该方法的有效性。