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.