Efficient Robot Localization Through Deep Learning-Based Natural Fiduciary Pattern Recognition
This paper introduces an efficient localization algorithm for robotic systems, utilizing deep learning to identify and exploit natural fiduciary patterns within the environment. Diverging from conventional localization techniques that depend on artificial markers, this method capitalizes on the inhe...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/3/467 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850068287956713472 |
|---|---|
| author | Ramón Alberto Mena-Almonte Ekaitz Zulueta Ismael Etxeberria-Agiriano Unai Fernandez-Gamiz |
| author_facet | Ramón Alberto Mena-Almonte Ekaitz Zulueta Ismael Etxeberria-Agiriano Unai Fernandez-Gamiz |
| author_sort | Ramón Alberto Mena-Almonte |
| collection | DOAJ |
| description | This paper introduces an efficient localization algorithm for robotic systems, utilizing deep learning to identify and exploit natural fiduciary patterns within the environment. Diverging from conventional localization techniques that depend on artificial markers, this method capitalizes on the inherent environmental features to enhance both accuracy and computational efficiency. By integrating advanced deep learning frameworks with natural scene analysis, the proposed algorithm facilitates robust, real-time localization in dynamic and unstructured settings. The resulting approach offers significant improvements in adaptability, precision, and operational efficiency, representing a substantial contribution to the field of autonomous robotics. We are aiming at analyzing an automotive manufacturing scenario to achieve robotic localization related to a moving target. To work with a simpler and more accessible scenario we have chosen a demonstrative context consisting of a laboratory wall containing some elements. This paper will focus on the first part of the case study, with a continuation planned for future work. It will demonstrate a scenario in which a camera is mounted on a robot, capturing images of the underside of a car (which we assume to be represented by a gray painted surface with specific elements to be described in Materials and Methods). These images are processed by a convolutional neural network (CNN), designed to detect the most distinctive features of the environment. The extracted information is crucial, as the identified characteristic areas will serve as reference points for the real-time localization of the industrial robot. In this work, we have demonstrated the potential of leveraging natural fiduciary patterns for efficient and accurate robot localization. By utilizing deep learning, specifically convolutional neural networks. The experimental results suggest that this approach is not only feasible but also scalable across a wide range of applications, including industrial automation autonomous vehicles, and aerospace navigation. As robots increasingly operate in environments where computational efficiency and adaptability are paramount, our methodology offers a viable solution to enhance localization without compromising accuracy or speed. The proposal of an algorithm that enables the application of the proposed method for natural fiduciary patterns based on neural networks to more complex scenarios is highlighted, along with the efficiency of the method for robot localization compared to others. |
| format | Article |
| id | doaj-art-7607fbecbfda4fe8818f888ba8f99400 |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-7607fbecbfda4fe8818f888ba8f994002025-08-20T02:48:06ZengMDPI AGMathematics2227-73902025-01-0113346710.3390/math13030467Efficient Robot Localization Through Deep Learning-Based Natural Fiduciary Pattern RecognitionRamón Alberto Mena-Almonte0Ekaitz Zulueta1Ismael Etxeberria-Agiriano2Unai Fernandez-Gamiz3Instituto Tecnológico de Las Américas (ITLA), La Caleta, Boca Chica 11606, Dominican RepublicSystem Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, SpainDepartment of Computer Languages and Systems, University College of Engineering, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, SpainDepartment Energy Engineering, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, SpainThis paper introduces an efficient localization algorithm for robotic systems, utilizing deep learning to identify and exploit natural fiduciary patterns within the environment. Diverging from conventional localization techniques that depend on artificial markers, this method capitalizes on the inherent environmental features to enhance both accuracy and computational efficiency. By integrating advanced deep learning frameworks with natural scene analysis, the proposed algorithm facilitates robust, real-time localization in dynamic and unstructured settings. The resulting approach offers significant improvements in adaptability, precision, and operational efficiency, representing a substantial contribution to the field of autonomous robotics. We are aiming at analyzing an automotive manufacturing scenario to achieve robotic localization related to a moving target. To work with a simpler and more accessible scenario we have chosen a demonstrative context consisting of a laboratory wall containing some elements. This paper will focus on the first part of the case study, with a continuation planned for future work. It will demonstrate a scenario in which a camera is mounted on a robot, capturing images of the underside of a car (which we assume to be represented by a gray painted surface with specific elements to be described in Materials and Methods). These images are processed by a convolutional neural network (CNN), designed to detect the most distinctive features of the environment. The extracted information is crucial, as the identified characteristic areas will serve as reference points for the real-time localization of the industrial robot. In this work, we have demonstrated the potential of leveraging natural fiduciary patterns for efficient and accurate robot localization. By utilizing deep learning, specifically convolutional neural networks. The experimental results suggest that this approach is not only feasible but also scalable across a wide range of applications, including industrial automation autonomous vehicles, and aerospace navigation. As robots increasingly operate in environments where computational efficiency and adaptability are paramount, our methodology offers a viable solution to enhance localization without compromising accuracy or speed. The proposal of an algorithm that enables the application of the proposed method for natural fiduciary patterns based on neural networks to more complex scenarios is highlighted, along with the efficiency of the method for robot localization compared to others.https://www.mdpi.com/2227-7390/13/3/467localization algorithmnatural fiduciary patternrobot localizationautonomous robotics |
| spellingShingle | Ramón Alberto Mena-Almonte Ekaitz Zulueta Ismael Etxeberria-Agiriano Unai Fernandez-Gamiz Efficient Robot Localization Through Deep Learning-Based Natural Fiduciary Pattern Recognition Mathematics localization algorithm natural fiduciary pattern robot localization autonomous robotics |
| title | Efficient Robot Localization Through Deep Learning-Based Natural Fiduciary Pattern Recognition |
| title_full | Efficient Robot Localization Through Deep Learning-Based Natural Fiduciary Pattern Recognition |
| title_fullStr | Efficient Robot Localization Through Deep Learning-Based Natural Fiduciary Pattern Recognition |
| title_full_unstemmed | Efficient Robot Localization Through Deep Learning-Based Natural Fiduciary Pattern Recognition |
| title_short | Efficient Robot Localization Through Deep Learning-Based Natural Fiduciary Pattern Recognition |
| title_sort | efficient robot localization through deep learning based natural fiduciary pattern recognition |
| topic | localization algorithm natural fiduciary pattern robot localization autonomous robotics |
| url | https://www.mdpi.com/2227-7390/13/3/467 |
| work_keys_str_mv | AT ramonalbertomenaalmonte efficientrobotlocalizationthroughdeeplearningbasednaturalfiduciarypatternrecognition AT ekaitzzulueta efficientrobotlocalizationthroughdeeplearningbasednaturalfiduciarypatternrecognition AT ismaeletxeberriaagiriano efficientrobotlocalizationthroughdeeplearningbasednaturalfiduciarypatternrecognition AT unaifernandezgamiz efficientrobotlocalizationthroughdeeplearningbasednaturalfiduciarypatternrecognition |