Szczegóły publikacji
Opis bibliograficzny
Development of a UR5 cobot vision system with MLP neural network for object classification and sorting / Szymon Kluziak, Piotr KOHUT // Information [Dokument elektroniczny]. - Czasopismo elektroniczne ; ISSN 2078-2489. — 2025 — vol. 16 iss. 7 art. no. 550, s. 1-30. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 28-30, Abstr. — Publikacja dostępna online od: 2025-06-27
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 161152 |
|---|---|
| Data dodania do BaDAP | 2025-07-16 |
| Tekst źródłowy | URL |
| DOI | 10.3390/info16070550 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Information |
Abstract
This paper presents the implementation of a vision system for a collaborative robot equipped with a web camera and a Python-based control algorithm for automated object-sorting tasks. The vision system aims to detect, classify, and manipulate objects within the robot’s workspace using only 2D camera images. The vision system was integrated with the Universal Robots UR5 cobot and designed for object sorting based on shape recognition. The software stack includes OpenCV for image processing, NumPy for numerical operations, and scikit-learn for multilayer perceptron (MLP) models. The paper outlines the calibration process, including lens distortion correction and camera-to-robot calibration in a hand-in-eye configuration to establish the spatial relationship between the camera and the cobot. Object localization relied on a virtual plane aligned with the robot’s workspace. Object classification was conducted using contour similarity with Hu moments, SIFT-based descriptors with FLANN matching, and MLP-based neural models trained on preprocessed images. Conducted performance evaluations encompassed accuracy metrics for used identification methods (MLP classifier, contour similarity, and feature descriptor matching) and the effectiveness of the vision system in controlling the cobot for sorting tasks. The evaluation focused on classification accuracy and sorting effectiveness, using sensitivity, specificity, precision, accuracy, and F1-score metrics. Results showed that neural network-based methods outperformed traditional methods in all categories, concurrently offering more straightforward implementation.
