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Applied Sciences
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Application of Computer Science in Mobile Robots II
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Application of Computer Science in Mobile Robots II

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: 20 August 2024 | Viewed by 1502

Special Issue Editors

Dr. Mónica Ballesta Galdeano

E-Mail Website
Guest Editor
Systems Engineering and Automation Department, Universidad Miguel Hernández de Elche (Alicante), 03202 Elche, Spain
Interests: mobile robots; deep learning; localization; mapping; scene recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Marina Paolanti

E-Mail Website
Guest Editor
Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, 60131 Ancona, Italy
Interests: deep learning; machine learning; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advancements in artificial intelligence (AI) techniques have attracted research into how they can be used in robotic systems. This Special Issue seeks to provide readers with an overview and applications of computer science and its related technologies such as machine and deep learning and their potential applications in mobile robots. The Issue is devoted to original research papers on techniques, applications, and industrial case studies of the design and deployment based on formal methods of robotic systems. The focus includes all aspects of modelling, simulation, testing, and implementation for the validation and verification of robotic systems. We seek high quality contributions of articles that advance AI along with its related technologies such as natural language processing, robotics, and machine and deep learning. We also welcome papers about incorporation of these technologies into actual products and services. Visionary papers describing futuristic applications and domain advancements are also encouraged.

Potential topics of interest include, but are not limited to, the following:

  • Artificial intelligence
  • Machine learning
  • Deep learning
  • Neural networks
  • Expert systems
  • Pattern recognition
  • Humanoid robots
  • Space and underwater robots
  • Assistive robots
  • Mobile robots
  • Autonomous robots
  • Human–robot interaction
  • Robotic automation

Dr. Mónica Ballesta Galdeano
Dr. Marina Paolanti
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. Applied Sciences 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 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

  • deep learning
  • machine learning
  • computer science
  • artificial intelligence
  • mobile robots
  • intelligent systems

Published Papers (2 papers)

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Research

24 pages, 7931 KiB  
Article
Fuzzy Logic-Based Driving Decision for an Omnidirectional Mobile Robot Using a Simulink Dynamic Model
by Mihai Crenganiș, Radu-Eugen Breaz, Sever-Gabriel Racz, Claudia-Emilia Gîrjob, Cristina-Maria Biriș, Adrian Maroșan and Alexandru Bârsan
Appl. Sci. 2024, 14(7), 3058; https://doi.org/10.3390/app14073058 - 5 Apr 2024
Viewed by 483
Abstract
This scientific paper presents the development and validation process of a dynamic model in Simulink used for decision-making regarding the locomotion and driving type of autonomous omnidirectional mobile platforms. Unlike traditional approaches relying on differential equations, this study uses Simulink’s block-based diagrams, offering [...] Read more.
This scientific paper presents the development and validation process of a dynamic model in Simulink used for decision-making regarding the locomotion and driving type of autonomous omnidirectional mobile platforms. Unlike traditional approaches relying on differential equations, this study uses Simulink’s block-based diagrams, offering a simpler and efficient development process. Importantly, the dynamic model accounts for friction forces, a critical factor for energy monitoring. The model’s validation is conducted experimentally, ensuring its accuracy and reliability. This paper formulates mathematical models for both conventional and Mecanum wheel configurations, facilitating energy-efficient driving strategies. By decomposing resistive forces into inertial and frictional components using the Jacobian matrix, this study accurately simulates electrical current consumption during robot motion. Through fuzzy decision algorithms utilizing parameters such as energy consumption, travel time, precision, and desired maneuverability, this paper proposes a method for determining the optimal locomotion mode for mobile platforms with Mecanum wheels. Overall, this research brings a new contribution to the field of mobile robotics by providing a comprehensive framework for dynamic modeling and it offers the possibility to drive omnidirectional robots in an energy-efficient manner. Full article
(This article belongs to the Special Issue Application of Computer Science in Mobile Robots II)
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15 pages, 867 KiB  
Article
Tolerance Synthesis of Delta-like Parallel Robots Using a Nonlinear Optimisation Method
by Allaoua Brahmia, Adlen Kerboua, Ridha Kelaiaia and Ameur Latreche
Appl. Sci. 2023, 13(19), 10703; https://doi.org/10.3390/app131910703 - 26 Sep 2023
Viewed by 700
Abstract
Robotic systems require high accuracy in manipulating objects. Positioning errors are influenced by geometric tolerances and various sources. This paper introduces a new technique based on the interior-point algorithm optimisation method to allocate tolerances to the geometric parameters of a robot. This method [...] Read more.
Robotic systems require high accuracy in manipulating objects. Positioning errors are influenced by geometric tolerances and various sources. This paper introduces a new technique based on the interior-point algorithm optimisation method to allocate tolerances to the geometric parameters of a robot. This method consists of three steps. First, a method for modelling the kinematic problem as well as the geometric errors must be used. The Denavit–Hartenberg rule is the most suitable method for this modelling case. Then, a mathematical model for tolerance allocation is developed and used as a nonlinear multivariable optimisation problem. Finally, the “interior-point” algorithm is used to solve this optimisation problem. The accuracy and efficiency of the proposed method, in determining the tolerance allocations for a Delta parallel robot, is illustrated via calculation and simulation results. The values of the dimensional tolerances found are optimal. As a result, these values always keep the accuracy less than or equal to the imposed value. Full article
(This article belongs to the Special Issue Application of Computer Science in Mobile Robots II)
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