FastOrient: Lightweight Computer Vision for Wrist Control in Assistive Robotic Grasping

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Title: FastOrient: Lightweight Computer Vision for Wrist Control in Assistive Robotic Grasping
Authors: Maymo, MR
Shafti, A
Faisal, AA
Item Type: Working Paper
Abstract: 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.
Issue Date: 9-Oct-2018
Copyright Statement: © 2018 The Author(s).
Keywords: cs.RO
Notes: 6 pages. Accepted for publication at IEEE BioRob 2018
Appears in Collections:Faculty of Engineering

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