New Localization Methods and Motion Tracking Algorithms for Mechatronic Systems, Robots and Unmanned Vehicles

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 5145

Special Issue Editors

Department of Mechatronics and Automation, Faculty of Engineering, University of Szeged, 6275 Szeged, Hungary
Interests: intelligent control; sensor fusion; robotics; kalman filtering; industrial robotics; soft computing; localization; SLAM
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Mechatronics and Automation, Faculty of Engineering, University of Szeged, 6725 Szeged, Hungary
Interests: intelligent sensor systems; wireless sensor networks; sensor calibration; inertial and magnetic sensors; sensor applications; human-machine interfaces; wearable sensors; sensor fusion; localization; intelligent transportation systems; vehicle detection and classification systems; robotics; mobile robots; multi-robot systems; pattern recognition; signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Mechatronics and Automation, Faculty of Engineering, University of Szeged, 6275 Szeged, Hungary
Interests: pneumatics systems; soft actuators; static and dynamic modelling; nonlinear controls

Special Issue Information

Dear Colleagues,

The localization problem of mobile robots/mechatronic systems is the first critical task that needs to be addressed in robot control applications. It outputs the pose estimate; the reliability of this result directly influences the success of control algorithms. The problem is solved in a sensor fusion framework, where generally relative and absolute poses are fused with different approaches, from probabilistic methods, over sophisticated mathematical applications, to deep learning models. The dynamic motion is described by relative sensor measurements along with their uncertainties; these sensors are encoders, magnetic, angular rate and gravity sensors (MARG), control speed signals, etc. Then, the absolute measurements, based on lidar, camera, GPS, or radio communication, are incorporated in uncertainty-driven observation models with the aim of correcting the previously obtained results. The combination of these models results in synergy in a recursive pose-estimation framework, which is the basis for nowadays state-of-the-art algorithms in motion tracking applications.

This Special Issue aims to invite high-quality research papers and up-to-date reviews that address new, challenging and interesting localization algorithms, sensor fusion solutions and motion tracking approaches in robotics/mechatronics applications. Topics of interest include, but are not limited to, the following:

  • Low-cost embedded system-based solutions;
  • Real-time and online sensor fusion algorithms;
  • Machine-learning-/deep-learning-aided localization approaches;
  • Artificial-intelligence-based sensor fusion solutions;
  • Adaptive algorithms in localization;
  • New sensor calibration techniques and multi sensor approaches;
  • Pattern-recognition-based intelligent sensory solutions;
  • Intelligent filtering algorithms and signal processing approaches;
  • New dynamical model implementations in filtration;
  • Novel sensor combinations and filter structures in localization solutions;
  • Human–machine interface-based applications in motion tracking.

Dr. Akos Odry
Dr. Peter Sarcevic
Prof. Dr. Jozsef Sarosi
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Machines is an international peer-reviewed open access monthly 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 2400 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

  • sensor fusion
  • motion tracking
  • localization
  • state estimation
  • sensor calibration
  • applied robotics
  • robot modeling
  • model validation
  • machine learning
  • intelligent sensor systems
  • pattern recognition

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

18 pages, 12611 KiB  
Article
Fuzzy Control of Self-Balancing, Two-Wheel-Driven, SLAM-Based, Unmanned System for Agriculture 4.0 Applications
by János Simon
Machines 2023, 11(4), 467; https://doi.org/10.3390/machines11040467 - 10 Apr 2023
Cited by 6 | Viewed by 1883
Abstract
This article presents a study on the fuzzy control of self-balancing, two-wheel-driven, simultaneous localization and mapping (SLAM)-based, unmanned systems for Agriculture 4.0 applications. The background highlights the need for precise and efficient navigation of unmanned vehicles in the field of agriculture. The purpose [...] Read more.
This article presents a study on the fuzzy control of self-balancing, two-wheel-driven, simultaneous localization and mapping (SLAM)-based, unmanned systems for Agriculture 4.0 applications. The background highlights the need for precise and efficient navigation of unmanned vehicles in the field of agriculture. The purpose of this study is to develop a fuzzy control system that can enable self-balancing and accurate movement of unmanned vehicles in various terrains. The methods employed in this study include the design of a fuzzy control system and its implementation in a self-balancing, two-wheel-driven, SLAM-based, unmanned system. The main findings of the study show that the proposed fuzzy control system is effective in achieving accurate and stable movement of the unmanned system. The conclusions drawn from the study indicate that the use of fuzzy control systems can enhance the performance of unmanned systems in Agriculture 4.0 applications by enabling precise and efficient navigation. This study has significant implications for the development of autonomous agricultural systems, which can greatly improve efficiency and productivity in the agricultural sector. Fuzzy control was chosen due to its ability to handle uncertainty and imprecision in real-world applications. Full article
Show Figures

Figure 1

Review

Jump to: Research

48 pages, 925 KiB  
Review
Localization and Mapping for Self-Driving Vehicles: A Survey
by Anas Charroud, Karim El Moutaouakil, Vasile Palade, Ali Yahyaouy, Uche Onyekpe and Eyo U. Eyo
Machines 2024, 12(2), 118; https://doi.org/10.3390/machines12020118 - 7 Feb 2024
Viewed by 2549
Abstract
The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of [...] Read more.
The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicle localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains. Full article
Show Figures

Figure 1

Back to TopTop