Image-Processing Based Methods to Improve the Robustness of Robotic Gripping

TL;DR

This paper demonstrates how optical flow image processing enhances robotic gripping by enabling slip detection and secure handling of soft tissues, improving automation in industries like food processing and surgery.

Contribution

It introduces a novel optical flow based algorithm for slip detection in robotic grippers, specifically tailored for handling soft, slippery tissues in industrial applications.

Findings

Optical flow methods effectively detect slip in robotic gripping.

The proposed system improves grip security on soft tissues.

Application in meat industry automation shows practical benefits.

Abstract

Image processing techniques have huge impact on most fields of robotics and industrial automation. Real time methods are usually employed in complex automation tasks, assisting with decision making or directly guiding robots and machinery, while post-processing is usually used for retrospective assessment of systems and processes. While artificial intelligence based image processing algorithms (usually neural networks) are more common nowadays, classical methods can also be used effectively even in modern applications. This paper focuses on optical flow based image processing, proving its efficiency by presenting optical flow based solutions for modern challenges in different fields of robotics such as robotic surgery and food industry automation. The main subject of the paper is a smart robotic gripper designed for automated robot cells in the meat industry, that is capable of slip detection and secure gripping of soft, slippery tissues with the help of the implemented optical flow based algorithm.