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New Solutions in Pattern Recognition and Intelligent Sensors for Mobile Robots

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1419

Special Issue Editors

Dr. Peter Sarcevic

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Guest Editor
Department of Mechatronics and Automation, Faculty of Engineering, University of Szeged, 6725 Szeged, Hungary
Interests: intelligent systems; sensor networks; pattern recognition; signal analysis; robotics; sensor fusion; mobile robots; localization; sensor calibration; sensor applications
Special Issues, Collections and Topics in MDPI journals
Dr. Akos Odry

E-Mail Website
Guest Editor
Department of Mechatronics and Automation, Faculty of Engineering, University of Szeged, 6275 Szeged, Hungary
Interests: inertial sensors; sensor fusion; localization; intelligent control; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Seyed Jalaleddin Mousavirad

E-Mail Website
Guest Editor
Department of Computer and Electrical Engineering (DET), Mid Sweden University, Holmgatan, 852 30 Sundsvall, Sweden
Interests: machine and deep learning; image processing and computer vision; evolutionary computation; measurement systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in technology have led to the application of various sensors in mobile robots. The effective use of the information provided by these sensors requires sophisticated algorithms.

Intelligent sensor systems in mobile robotics can be used in various areas, such as localization, the detection and classification of objects, control, etc.

Developing real-time algorithms for mobile robots is a challenging task, especially today, as the focus has shifted toward distributed systems where algorithms run on robots' embedded hardware.

The aim of this Special Issue is to invite high-quality research papers and up-to-date reviews that address challenging topics on intelligent sensor systems for mobile robots. Topics of interest include, but are not limited to, the following:

  • Robot sensors and sensor networks;
  • Pattern-recognition-based solutions;
  • Sensor fusion and perception;
  • Sensor calibration and pre-processing of signals;
  • Signal and image analysis, feature extraction, and feature selection methods;
  • Machine learning and decision-making and classification methods;
  • Sensory-based robot control and learning;
  • Navigation, localization, and SLAM;
  • Human–robot interactions;
  • Robotic vision, recognition, and reconstruction;
  • Implementation of algorithms on embedded systems.

Dr. Peter Sarcevic
Dr. Akos Odry
Dr. Seyed Jalaleddin Mousavirad
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent sensor systems
  • mobile robots
  • sensor networks
  • pattern recognition
  • control
  • sensor fusion
  • localization
  • machine learning
  • decision-making

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Published Papers (3 papers)

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Research

21 pages, 10300 KB  
Article
Cross-Detector Visual Localization with Coplanarity Constraints for Indoor Environments
by Jose-Luis Matez-Bandera, Alberto Jaenal, Clara Gomez, Alejandra C. Hernandez, Javier Monroy, José Araújo and Javier Gonzalez-Jimenez
Sensors 2025, 25(24), 7593; https://doi.org/10.3390/s25247593 - 15 Dec 2025
Viewed by 140
Abstract
Most visual localization (VL) methods typically assume that keypoints in the query image are detected with the same algorithm as those stored in the reference map. This poses a serious limitation, as new and better detectors may progressively appear, and we would like [...] Read more.
Most visual localization (VL) methods typically assume that keypoints in the query image are detected with the same algorithm as those stored in the reference map. This poses a serious limitation, as new and better detectors may progressively appear, and we would like to ensure the interoperability and coexistence of cameras with heterogeneous detectors in a single map representation. While rebuilding the map with new detectors might seem a solution, it is often impractical, as original images may be unavailable or restricted due to data privacy constraints. In this paper, we address this challenge with two main contributions. First, we introduce and formalize the problem of cross-detector VL, in which the inherent spatial discrepancies between keypoints from different detectors hinder the process of establishing correct correspondences when relying strictly on the similarity of descriptors for matching. Second, we propose CoplaMatch, the first approach to solve this problem by relaxing strict descriptor similarity and imposing geometric coplanarity constraints. The latter is achieved by leveraging 2D homographies between groups of query and map keypoints. This process involves segmenting planar patches, which is performed offline once for the map, and also in the query image, which adds an extra computational overhead to the VL process, although we demonstrated in our experiments that this does not hinder the online applicability. We extensively validate our proposal through experiments in indoor environments using real-world datasets, demonstrating its effectiveness against two state-of-the-art methods by enabling accurate localization in cross-detector scenarios. Additionally, our work validates the feasibility of cross-detector VL and opens a new direction for the long-term usability of feature-based maps. Full article
(This article belongs to the Special Issue New Solutions in Pattern Recognition and Intelligent Sensors for Mobile Robots)
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22 pages, 4777 KB  
Article
A Mechanical Structure Design and Simulation-Based Validation of a Novel Compact and Low-Cost 3-DOF Robotic Arm
by Jiahe Chen, Bojun Jiang, Shu Zhu and Jun Wang
Sensors 2025, 25(23), 7356; https://doi.org/10.3390/s25237356 - 3 Dec 2025
Viewed by 402
Abstract
This paper presents the mechanical design and simulation-based validation of a novel compact and low-cost 3-DOF dual-arm robotic system tailored for space-constrained applications such as rescue robotics. The proposed design achieves a fully folded footprint of 366 × 226.3 × 100 mm through [...] Read more.
This paper presents the mechanical design and simulation-based validation of a novel compact and low-cost 3-DOF dual-arm robotic system tailored for space-constrained applications such as rescue robotics. The proposed design achieves a fully folded footprint of 366 × 226.3 × 100 mm through an orthogonal joint configuration and modular structure, while maintaining a hemispherical workspace for each arm. Key innovations include the following: (1) A cost-optimized architecture with only 3 motors per arm (total system cost ~£2000), enabled by hybrid manufacturing (laser-cut acrylic hull and 3D-printed ASA-CF reinforced links with 3740 MPa flexural modulus); (2) a custom Python-based skeleton modeling tool that automates D-H parameter generation and kinematic analysis, supporting rapid design iteration; (3) verified collision-free operation via point cloud analysis, demonstrating successful target grasping (50 mm objects) and dual-arm coordination despite a 5–20 mm deflection tolerance. The system addresses critical limitations in affordable, deployable manipulators, with future work focusing on 3D printing and part manufacturing in industry applications. Full article
(This article belongs to the Special Issue New Solutions in Pattern Recognition and Intelligent Sensors for Mobile Robots)
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30 pages, 6687 KB  
Article
A Novel Shallow Neural Network-Augmented Pose Estimator Based on Magneto-Inertial Sensors for Reference-Denied Environments
by Akos Odry, Peter Sarcevic, Giuseppe Carbone, Peter Odry and Istvan Kecskes
Sensors 2025, 25(22), 6864; https://doi.org/10.3390/s25226864 - 10 Nov 2025
Viewed by 636
Abstract
Magnetic, angular rate, and gravity (MARG) sensor-based inference is the de facto standard for mobile robot pose estimation, yet its sensor limitations necessitate fusion with absolute references. In environments where such references are unavailable, the system must rely solely on the uncertain MARG-based [...] Read more.
Magnetic, angular rate, and gravity (MARG) sensor-based inference is the de facto standard for mobile robot pose estimation, yet its sensor limitations necessitate fusion with absolute references. In environments where such references are unavailable, the system must rely solely on the uncertain MARG-based inference, posing significant challenges due to the resulting estimation uncertainties. This paper addresses the challenge of enhancing the accuracy of position/velocity estimations based on the fusion of MARG sensor data with shallow neural network (NN) models. The proposed methodology develops and trains a feasible cascade-forward NN to reliably estimate the true acceleration of dynamical systems. Three types of NNs are developed for acceleration estimation. The effectiveness of each topology is comprehensively evaluated in terms of input combinations of MARG measurements and signal features, number of hidden layers, and number of neurons. The proposed approach also incorporates extended Kalman and gradient descent orientation filters during the training process to further improve estimation effectiveness. Experimental validation is conducted through a case study on position/velocity estimation for a low-cost flying quadcopter. This process utilizes a comprehensive database of random dynamic flight maneuvers captured and processed in an experimental test environment with six degrees of freedom (6DOF), where both raw MARG measurements and ground truth data (three positions and three orientations) of system states are recorded. The proposed approach significantly enhances the accuracy in calculating the rotation matrix-based acceleration vector. The Pearson correlation coefficient reaches 0.88 compared to the reference acceleration, surpassing 0.73 for the baseline method. This enhancement ensures reliable position/velocity estimations even during typical quadcopter maneuvers within 10-s timeframes (flying 50 m), with a position error margin ranging between 2 to 4 m when evaluated across a diverse set of representative quadcopter maneuvers. The findings validate the engineering feasibility and effectiveness of the proposed approach for pose estimation in GPS-denied or landmark-deficient environments, while its application in unknown environments constitutes the main future research direction. Full article
(This article belongs to the Special Issue New Solutions in Pattern Recognition and Intelligent Sensors for Mobile Robots)
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