Nullspace Optimization of Redundant Robots for Dynamics Decoupling in Motion Force Control
Wenbo Tang, Weiming Wang, Shiquan Wang, Wenhai Liu
AI summary
Problem
Dynamics coupling between motion and force subspaces undermines force control robustness in redundant robots, while existing decoupling methods that actively reshape inertia amplify modeling uncertainties and sensor noise.
Approach
A quadratic programming framework passively shapes the robot's nullspace posture to minimize the Frobenius norm of inertia coupling, generating human-like configurations that naturally decouple motion and force dynamics without active inertia reshaping.
Key results
- Derivation of three distinct objective functions to quantify and minimize motion-force coupling
- Integration of QP-based nullspace optimization into an impedance motion-force control framework
- Experimental validation showing improved force stability and tracking under high friction and disturbances
- Demonstration of superior performance over conventional nullspace tracking in simulation and physical tasks
Why it matters
Enables safer, more robust human-robot interaction and precision contact tasks in manufacturing and service industries by eliminating the need for error-prone active inertia shaping.
Abstract
The dynamics coupling between motion and force subspaces in robotic control poses significant challenges to en- suring 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 stabil- ity and tracking performance under conditions of high friction and external disturbances, outperforming conventional motion force control with traditional nullspace tracking approaches.