  Wearable and Assistive robotics for human grasp support are broadly either
tele-operated robotic arms or act through orthotic control of a paralyzed
user's hand. Such devices require correct orientation for successful and
efficient grasping. In many human-robot assistive settings, the end-user is
required to explicitly control the many degrees of freedom making effective or
efficient control problematic. Here we are demonstrating the off-loading of
low-level control of assistive robotics and active orthotics, through automatic
end-effector orientation control for grasping. This paper describes a compact
algorithm implementing fast computer vision techniques to obtain the
orientation of the target object to be grasped, by segmenting the images
acquired with a camera positioned on top of the end-effector of the robotic
device. The rotation needed that optimises grasping is directly computed from
the object's orientation. The algorithm has been evaluated in 6 different scene
backgrounds and end-effector approaches to 26 different objects. 94.8% of the
objects were detected in all backgrounds. Grasping of the object was achieved
in 91.1% of the cases and has been evaluated with a robot simulator confirming
the performance of the algorithm.
