Robust graph-based spatial coupling of dynamic movement primitives for multi-robot manipulation

Abstract

Dynamic Movement Primitives (DMPs) provide a flexible framework for robotic trajectory generation, offering adaptability, robustness to disturbances, and modulation of predefined motions. Yet achieving reliable spatial coupling among multiple DMPs in cooperative manipulation tasks remains a challenge. This paper introduces a graph-based trajectory planning framework that designs dynamic controllers to couple multiple DMPs while preserving formation. The proposed method is validated in both simulation and real-world experiments on a dual-arm UR5 robot performing tasks such as soft cloth folding and object transportation. Results show faster convergence and improved noise resilience compared to conventional approaches. These findings demonstrate the potential of the proposed framework for rapid deployment and effective trajectory planning in multi-robot manipulation.