Work

Vision-based control of small educational parallel selective compliance assembly robot arm robot

Year: 2025

Type: article

Abstract: Vision-based control in robotics offers versatile automation; however, accessible educational platforms for exploring its integration with AI are still limited.This paper addresses this gap by presenting a small, 3D-printed parallel SCARA robot designed specifically for educational purposes.We provide details on its construction and demonstrate its application in laboratory exercises, which cover inverse and forward kinematics, vision-based tip positioning, and object detection.Notably, we investigate both supervised (using convolutional neural networks) and unsupervised (through autoencoder latent space exploration) approaches for classifying faulty parts.The unsupervised method achieved high performance, with a precision of 1.00, recall of 0.96, and an F1-measure of 0.98, which is comparable to the supervised approach that yielded a precision of 0.98, recall of 0.97, and an F1-measure of 0.97.This work contributes to the development of a low-cost platform and demonstrates the effectiveness of unsupervised AI techniques for vision-based robotic fault detection in educational settings, paving the way for more advanced AI-integrated robotics curricula.

Source: Advances in Science and Technology – Research Journal

DOI: 10.12913/22998624/204413

Authors: Michał Batsch, Dariusz Nowak, Dawid Wydrzyński, Łukasz Kochmański

Institution: Rzeszów University of Technology

Language: English

Topic: Educational Robotics and Engineering

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $330