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.

Automation involves having a machine perform simple, repetitive operations that follow instructions or workflows set by humans. Automation tasks are very repetitive, predictable tasks. Think of a machine in a factory that makes the same part the same way over and over again. For many people, artificial intelligence (AI) means robots that perform complex human tasks in science fiction movies. Actually, this is partially true.