A Novel DMPs Framework for Robot Skill Generalizing With Obstacle Avoidance: Taking Volume and Orientation Into Consideration

The method of dynamic movement primitives (DMPs) is a widely used tool for robots to learn and generalize human skills in different situations. In previous studies, researchers have proposed several methods to generalize the learned human skills when avoiding obstacles. However, the assumption of treating the end-effector of robot as a point is not advisable in actual use. In fact, in all conditions, ignoring the volume of the end-effector can easily lead to unexpected collisions, especially when robots are using volumetric tools or carrying items. Moreover, the orientation of the robot end-effector should be also considered when avoiding obstacles. This article proposes a novel DMPs-based framework to generalize the learned skills with shorter extra distances when avoiding obstacles by adding coupling terms in both the position space and the quaternion space. First, the Minkowski sum of obstacle and end-effector is calculated to represent the collision space, which is then utilized to construct Minkowski sum-based coupling terms in DMPs to avoid collisions. After that, quaternion-based DMPs are used to represent the orientation in Cartesian space, which is used to update the appropriate Minkowski sum at each moment. Furthermore, a quaternion coupling term is proposed to regulate the orientation of the robot end-effector, helping to avoid obstacles in different scenarios. With the introduction of two novel coupling terms in DMPs and quaternion-based DMPs, an advanced skill learning and generalization framework based on DMPs is developed. By integrating both position and orientation, this framework costs only nearly half the additional distance in generalization for obstacle avoidance and demonstrates significantly improved performance across various scenarios. Simulation and experiments have been performed using a KUKA LBR IIWA robot and the results have confirmed the validity of the proposed methods.