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Advanced Technologies in AI Mobile Robots
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Advanced Technologies in AI Mobile Robots

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1992

Special Issue Editors

Dr. Antonio H.J. Moreira

E-Mail Website
Guest Editor
1. 2Ai – School of Technology, IPCA, Barcelos, Portugal
2. LASI – Associate Laboratory of Intelligent Systems, Guimarães, Portugal
Interests: artificial intelligence; Industry 4.0; robotics; computer vision; IoT & EdgeAI
Dr. João Borges

E-Mail Website
Guest Editor
2Ai – School of Technology, IPCA, Barcelos, Portugal
Interests: artificial intelligence; automotive; computer vision; automation and robotics
Prof. Dr. José Lima

E-Mail Website
Guest Editor
1. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal
2. Centre for Robotics in Industry and Intelligent Systems (CRIIS), Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), 4200-465 Porto, Portugal
Interests: mobile robot localization; collaborative robots; IoT; path planning; simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue provides a comprehensive exploration of the relationship between cutting-edge advancements in artificial intelligence (AI) and the evolution of mobile robots. It focuses on the specific ways these powerful technologies are being integrated, highlighting how sophisticated algorithms work in an integrated way with advanced sensors like lidar, cameras, and other technologies. Additionally, this Special Issue explores the role of machine learning in enabling robots to continuously learn and adapt to their environments, further enhancing their capabilities.

The focus for this Special Issue extends beyond outlining the latest advancements to encompass real-world applications and the results of this technological integration, showcasing how mobile robots are achieving increased autonomy, adaptability, and efficiency. This translates to revolutionary applications across various industries, with this Special Issue specifically addressing the impact on sectors like industrial processes, logistic operations, healthcare applications, and automated agriculture.

By exploring these advancements and their real-world applications, this Special Issue aims to provide a comprehensive overview of the future that lies ahead for AI-powered mobile robots and their potential to revolutionize various aspects of our lives.

Dr. Antonio H.J. Moreira
Dr. João Borges
Prof. Dr. José Lima
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

  • AI-powered mobile robots
  • artificial intelligence and machine learning
  • computer vision
  • advanced sensors and data fusion
  • autonomous navigation
  • scene context and interaction
  • real-world deployment (industrial, healthcare, agriculture)
  • robotic operating system (ROS)
  • 5G technology integration
  • human cooperation
  • advanced technologies
  • innovation

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Published Papers (2 papers)

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Research

19 pages, 4449 KiB  
Article
Development of an Adaptive Force Control Strategy for Soft Robotic Gripping
by Ian MacDonald and Rickey Dubay
Appl. Sci. 2024, 14(16), 7354; https://doi.org/10.3390/app14167354 - 20 Aug 2024
Viewed by 875
Abstract
Using soft materials in robotic mechanisms has become a common solution to overcome many challenges associated with the rigid bodies frequently used in robotics. Compliant mechanisms allow the robot to adapt to objects and perform a broader range of tasks, unlike rigid bodies [...] Read more.
Using soft materials in robotic mechanisms has become a common solution to overcome many challenges associated with the rigid bodies frequently used in robotics. Compliant mechanisms allow the robot to adapt to objects and perform a broader range of tasks, unlike rigid bodies that are generally designed for specific applications. However, soft robotics presents its own set of challenges in both design and implementation, particularly in sensing and control. These challenges are abundant when dealing with the force control problem of a compliant gripping mechanism. The ability to effectively regulate the applied force of a gripper is a critical task in many control operations, as it allows the precise manipulation of objects, which drives the need for enhanced force control strategies for soft or flexible grippers. Standard sensing techniques, such as motor current monitoring and strain-based sensors, add complexities and uncertainties when establishing mathematical models of soft grippers to the required gripping forces. In addition, the soft gripper creates a complex non-linear system, compounded by adding an adhesive-type sensor. This work develops a unique visual force sensor trained on synthetic data generated using finite element analysis (FEA) and implemented by integrating a non-linear model reference adaptive controller (MRAC) to control gripping force on a fixed 6-DOF robot. The robot can be placed on a mobile platform to perform various tasks. The virtual FEA sensor and controller, combined, are termed virtual reference adaptive control (VRAC). The VRAC was compared to other methods and achieved comparable control sensing and control performance while reducing the complexity of the sensor requirements and its integration. The VRAC strategy effectively controlled the gripping force by driving the dynamics to match the desired performance after a limited amount of training cycles. The controller proposed in this work was designed to be generally applicable to most objects that the gripper will interact with and easily adaptable to a wide variety of soft grippers. Full article
(This article belongs to the Special Issue Advanced Technologies in AI Mobile Robots)
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24 pages, 3201 KiB  
Article
Comparison between an Adaptive Gain Scheduling Control Strategy and a Fuzzy Multimodel Intelligent Control Applied to the Speed Control of Non-Holonomic Robots
by Mateus G. Miquelanti, Luiz F. Pugliese, Waner W. A. G. Silva, Rodrigo A. S. Braga and Juliano A. Monte-Mor
Appl. Sci. 2024, 14(15), 6675; https://doi.org/10.3390/app14156675 - 31 Jul 2024
Cited by 1 | Viewed by 753
Abstract
The main objective of this work is to address problems related to the speed control of mobile robots with non-holonomic constraints and differential traction—specifically, robots for football games in the VSS (Very Small Size) category. To achieve this objective, an implementation and comparison [...] Read more.
The main objective of this work is to address problems related to the speed control of mobile robots with non-holonomic constraints and differential traction—specifically, robots for football games in the VSS (Very Small Size) category. To achieve this objective, an implementation and comparison is carried out between two control strategies: an adaptive control strategy by gain scheduling and a fuzzy multimodel intelligent control strategy. The mathematical models of the wheel motors for each operating range are approximated by a first-order system since data acquisition is performed using the step response. Tuning of the proportional and integral gains of the local controllers is carried out using the root locus technique in discrete time. For each mathematical model obtained for an operating range, a local controller is tuned. Finally, with the local controllers in hand, the implementation of and comparison between the gain scheduling adaptive control strategy and the fuzzy multimodel intelligent control strategy are carried out, in which the control strategies are programmed into the low-level code of a non-holonomic robot with a differential drive to verify the performance of the speed tracking dynamics imposed on the wheel motors to improve robot navigation during a robot football match. Full article
(This article belongs to the Special Issue Advanced Technologies in AI Mobile Robots)
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