🤖 AI Summary
This work addresses the challenges of kinematic redundancy and task-space decoupling in serial manipulators performing low-degree-of-freedom tasks. To overcome these issues, the authors propose a geometrically defined screw projector that directly decomposes the end-effector twist into task-relevant and redundant components, thereby establishing a compact inverse kinematics framework. Unlike conventional approaches relying on Jacobian null-space projection, this method leverages geometric screw decomposition to intuitively separate motions inside and outside the task space, offering a unified treatment of both kinematic and task redundancy. Experimental results demonstrate that the proposed approach enables efficient, intuitive, and natural motion control while effectively managing redundancy.
📝 Abstract
This paper introduces a twist decomposition framework for serial manipulators performing lower mobility tasks. Rather than relying on Jacobian null-space projections, the method separates the end-effector twist into task and redundant components using geometrically defined twist projectors. This formulation provides a direct and intuitive distinction between task-relevant and task-irrelevant motions in operational space, enabling a compact inverse kinematics scheme that naturally handles both manipulator and task redundancy.