Cooperative Control for Dual-Arm Robots Based on Improved Dynamic Movement Primitives

Abstract

In this article, a cooperative control scheme for a dual-arm robotic system is proposed to manipulate various objects, where a vision system is utilized to compensate for the uncertainties of manipulated objects. In the proposed strategy, improved dynamic movement primitives (DMPs) are designed to achieve dual-arm robot trajectory learning and generalization. This method solves the problem of excessive initial acceleration in classic DMPs methods while ensuring fast convergence of motion trajectories. Furthermore, the impedance control is employed to adjust the contact force between the robotic manipulator and the object, aiming to achieve compliant control. To verify the feasibility and effectiveness of the proposed scheme, a humanoid dual-arm robotic system is set up with two Kinova MICO manipulators. Experimental results demonstrate that the proposed method successfully completes transportation tasks, highlighting its potential for enhancing robotic manipulation capabilities.