  This paper presents a new dual quaternion-based formulation for pose-based
visual servoing. Extending our previous work on local contact moment (LoCoMo)
based grasp planning, we demonstrate grasping of arbitrarily moving objects in
3D space. Instead of using the conventional axis-angle parameterization, dual
quaternions allow designing the visual servoing task in a more compact manner
and provide robustness to manipulator singularities. Given an object point
cloud, LoCoMo generates a ranked list of grasp and pre-grasp poses, which are
used as desired poses for visual servoing. Whenever the object moves (tracked
by visual marker tracking), the desired pose updates automatically. For this,
capitalising on the dual quaternion spatial distance error, we propose a
dynamic grasp re-ranking metric to select the best feasible grasp for the
moving object. This allows the robot to readily track and grasp arbitrarily
moving objects. In addition, we also explore the robot null-space with our
controller to avoid joint limits so as to achieve smooth trajectories while
following moving objects. We evaluate the performance of the proposed visual
servoing by conducting simulation experiments of grasping various objects using
a 7-axis robot fitted with a 2-finger gripper. Obtained results demonstrate the
efficiency of our proposed visual servoing.
