FastOrient: Lightweight Computer Vision for Wrist Control in Assistive Robotic Grasping
This addresses the challenge of efficient control in assistive robotics for users with mobility impairments, though it is incremental as it builds on existing computer vision techniques.
The paper tackles the problem of automating end-effector orientation for assistive robotic grasping by using a fast computer vision algorithm to detect object orientation from a camera on the robot, achieving 94.8% object detection and 91.1% successful grasping in tests.
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.