Nullspace Optimization of Redundant Robots for Dynamics Decoupling in Motion Force Control
Wenbo Tang, Weiming Wang, Shiquan Wang, Wenhai Liu
Abstract
The dynamics coupling between motion and force subspaces in robotic control poses significant challenges to ensuring force control robustness, particularly under large external disturbances. While actively shaping the system inertia can eliminate this coupling, it introduces additional disturbances due to modeling uncertainties and force sensing errors. Inspired by how humans naturally adjust their elbow postures to facilitate motion force operations, we propose a quadratic programming-based nullspace optimization method that minimizes dynamics coupling for redundant torque-controlled robots. Integrated into an impedance motion force control framework, our approach minimizes an objective function defined by the Frobenius norm of the projection matrix representing inertia coupling in Cartesian space, yielding human-like postures that passively decouple task dynamics. Experimental results demonstrate that the proposed nullspace optimization significantly improves force control stability and tracking performance under conditions of high friction and external disturbances, outperforming conventional motion force control combined with traditional nullspace tracking approaches.