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

TL;DR

FastOrient introduces a lightweight computer vision algorithm that accurately determines object orientation for assistive robotic grasping, reducing user control complexity and improving grasp success rates.

Contribution

The paper presents a novel, fast vision-based method for automatic end-effector orientation control in assistive robotics, enhancing grasping efficiency and user experience.

Findings

Object detection rate of 94.8% across diverse backgrounds

Grasp success rate of 91.1% in simulated tests

Effective orientation estimation in real-time scenarios

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.