Version 1
: Received: 2 October 2024 / Approved: 2 October 2024 / Online: 2 October 2024 (15:05:04 CEST)
How to cite:
Vasilev, R.; Chivarov, N.; Ivanova, V. Integration of Object Recognition, Color Classification and QR Decoding for the Purposes of Intelligent Robots. Preprints2024, 2024100195. https://doi.org/10.20944/preprints202410.0195.v1
Vasilev, R.; Chivarov, N.; Ivanova, V. Integration of Object Recognition, Color Classification and QR Decoding for the Purposes of Intelligent Robots. Preprints 2024, 2024100195. https://doi.org/10.20944/preprints202410.0195.v1
Vasilev, R.; Chivarov, N.; Ivanova, V. Integration of Object Recognition, Color Classification and QR Decoding for the Purposes of Intelligent Robots. Preprints2024, 2024100195. https://doi.org/10.20944/preprints202410.0195.v1
APA Style
Vasilev, R., Chivarov, N., & Ivanova, V. (2024). Integration of Object Recognition, Color Classification and QR Decoding for the Purposes of Intelligent Robots. Preprints. https://doi.org/10.20944/preprints202410.0195.v1
Chicago/Turabian Style
Vasilev, R., Nayden Chivarov and Valentina Ivanova. 2024 "Integration of Object Recognition, Color Classification and QR Decoding for the Purposes of Intelligent Robots" Preprints. https://doi.org/10.20944/preprints202410.0195.v1
Abstract
This article presents a conceptual program model that integrates three main methods: object recognition, color classification, and QR decoding of information. These methods are well-established in the fields of Robotics and Artificial Intelligence, yet they remain subjects of ongoing research interest. The program model organizes the execution of the methods in a sequence directed toward recognized objects. In this way, color classification and QR decoding are performed only on already recognized objects. Colors and QR codes that do not belong to recognized objects are not analyzed, which can provide intelligent robots with reliable and accurate information about the objects they can recognize. For research purposes, image sensors (cameras), images saved to disk, clipboard, or shared memory between applications are used. The article shows that the program model can be augmented with various sensors and algorithms for the comprehensive construction of program control of autonomous robots. Future research that builds on current studies will focus on measuring the distance to recognized objects and structuring the collected information about these objects, which will create opportunities for perceptual anchoring and logical reasoning in the program control of intelligent robots.
Keywords
object recognition; color classification; QR decoding; mobile robots; dynamic environment; perceptual anchoring; artificial intelligence.
Subject
Computer Science and Mathematics, Robotics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
