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Multiagent Systems, Learning and Their Applications to Robotic (Mobile, Aerial, Underwater, Industrial and Medical) Systems

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

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 7983

Special Issue Editor

Prof. Dr. Shafiqul Islam

E-Mail Website
Guest Editor
Intelligent Robotics, Mechatronics and Autonomous Systems LabDepartment of Computer Science and Engineering, Xavier University of Louisiana, NCF-235 Academic Science Complex,1 Drexel Drive, New Orleans, LA 70125, USA
Interests: machine learning for autonomous multi-vehicles (aerial, ground, underwater)/robotics (medical, industrial) network systems; collaborative learning control and manipulation for cyber-physical systems; intelligent robotics; mechatronics and unmanned vehicles

Special Issue Information

Dear Colleagues,

We are pleased to inform you that the Special Issue of the open-access journal Sensors (ISSN 1424-8220, IF 3.275) entitled “Multiagent Systems, Learning and Their Applications to Robotic (Mobile, Aerial, Underwater, Industrial and Medical) Systems” is accepting contributions.

In recent years, multi-agent systems have received a great deal of attention from researchers in different disciplines far beyond computer science and artificial intelligence communities. This is because multi-agent systems can be used to solve complex problems in various fields that are difficult or impossible for single agents or systems.

The goal in Special Issue in the "Physical" section is to gather novel contributions on multiagent systems, learning and their applications to mobile, aerial, underwater, industrial and medical robotic systems. This Special Issue will focus on identification, modeling, coordination, control and machine-learning methods for robotic systems with the presence of uncertainty. Manuscripts specifically addressing the theoretical, experimental/practical and technological aspects of identification, modeling, cooperation/synchronization, control and learning techniques for robotic systems and extending the concepts and methodologies from classical methods to advanced robust methods with the presence of uncertainty will be highly desirable for this Issue. The possible topics of interest include but are not limited to:

  • Multi-agent reconfigurable robotics;
  • Multi-agent swarm robotics, multi-agent leader–follower robotics; 
  • Multi-agent robot localization, mapping and exploration;
  • Multi-agent robot motion coordination;
  • Multi-agent robot architectures and task planning;
  • Multi-agent robot control mechanisms and multi-agent robot learning;
  • Multi-agent robot object grasping, transportation and manipulation.

We look forward to receiving your contributions.

Prof. Dr. Shafiqul Islam
Guest Editor

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. 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 robotic agents
  • Multi-agent robotic systems
  • Modeling, identification and multi-agent autonomous learning
  • Distribution, cooperation and machine learning
  • Architecture and task planning
  • Communication, interaction and applications

Published Papers (3 papers)

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Research

23 pages, 2911 KiB  
Article
Xiroi II, an Evolved ASV Platform for Marine Multirobot Operations
by Antoni Martorell-Torres, Eric Guerrero-Font, José Guerrero-Sastre and Gabriel Oliver-Codina
Sensors 2023, 23(1), 109; https://0-doi-org.brum.beds.ac.uk/10.3390/s23010109 - 22 Dec 2022
Cited by 5 | Viewed by 1785
Abstract
In this paper, we present the design, development and a practical use of an Autonomous Surface Vehicle (ASV) as a modular and flexible platform for a large variety of marine tasks including the coordination strategies with other marine robots. This work tackles the [...] Read more.
In this paper, we present the design, development and a practical use of an Autonomous Surface Vehicle (ASV) as a modular and flexible platform for a large variety of marine tasks including the coordination strategies with other marine robots. This work tackles the integration of an open-source Robot-Operating-System (ROS)-based control architecture that provides the ASV with a wide variety of navigation behaviors. These new ASV capabilities can be used to acquire useful data from the environment to survey, map, and characterize marine habitats. In addition, the ASV is used as a radio frequency relay point between an Autonomous Underwater Vehicle (AUV) and the ground station as well as to enhance the Acoustic Communication Link (ACL) with the AUV. In order to improve the quality of the ACL, a new Marine Multirobot System (MMRS) coordination strategy has been developed that aims to keep both vehicles close to each other. The entire system has been successfully designed, implemented, and tested in real marine environment robotic tasks. The experimental tests show satisfactory results both in ROS-based navigation architecture and the MMRS coordination strategy resulting in a significant improvement of the quality of the ACL. Full article
(This article belongs to the Special Issue Multiagent Systems, Learning and Their Applications to Robotic (Mobile, Aerial, Underwater, Industrial and Medical) Systems)
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28 pages, 6534 KiB  
Article
Dynamics and Control of a Magnetic Transducer Array Using Multi-Physics Models and Artificial Neural Networks
by Georgios Tsakyridis and Nikolaos I. Xiros
Sensors 2021, 21(20), 6788; https://0-doi-org.brum.beds.ac.uk/10.3390/s21206788 - 13 Oct 2021
Cited by 3 | Viewed by 1868
Abstract
A linear mechanical oscillator is non-linearly coupled with an electromagnet and its driving circuit through a magnetic field. The resulting non-linear dynamics are investigated using magnetic circuit approximations without major loss of accuracy and in the interest of brevity. Different computational approaches to [...] Read more.
A linear mechanical oscillator is non-linearly coupled with an electromagnet and its driving circuit through a magnetic field. The resulting non-linear dynamics are investigated using magnetic circuit approximations without major loss of accuracy and in the interest of brevity. Different computational approaches to simulate the setup in terms of dynamical system response and design parameters optimization are pursued. A current source operating in baseband without modulation directly feeds the electromagnet, which consists commonly of a solenoid and a horseshoe-shaped core. The electromagnet is then magnetically coupled to a mass made of soft magnetic material and attached to a spring with damping. The non-linear system is described by a linearized steady-space representation while is examined for controllability and observability. A controller using a pole placement approach is built to stabilize the element. Drawing upon the fact that coupling works both ways, enabling estimation of the mass position and velocity (state variables) by processing the induced voltage across the electromagnet, a state observer is constructed. Accurate and fast tracking of the state variables, along with the possibility of driving more than one module from the same source using modulation, proves the applicability of the electro-magneto-mechanical transducer for sensor applications. Next, a three-layer feed-forward artificial neural network (ANN) system equivalent was trained using the non-linear plant-linear controller-linear observer configuration. Simulations to investigate the robustness of the system with respect to different equilibrium points and input currents were carried out. The ANN proved robust with respect to position accuracy. Full article
(This article belongs to the Special Issue Multiagent Systems, Learning and Their Applications to Robotic (Mobile, Aerial, Underwater, Industrial and Medical) Systems)
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19 pages, 24568 KiB  
Article
Reinforcement-Learning-Based Route Generation for Heavy-Traffic Autonomous Mobile Robot Systems
by Dominik Kozjek, Andreja Malus and Rok Vrabič
Sensors 2021, 21(14), 4809; https://0-doi-org.brum.beds.ac.uk/10.3390/s21144809 - 14 Jul 2021
Cited by 6 | Viewed by 2678
Abstract
Autonomous mobile robots (AMRs) are increasingly used in modern intralogistics systems as complexity and performance requirements become more stringent. One way to increase performance is to improve the operation and cooperation of multiple robots in their shared environment. The paper addresses these problems [...] Read more.
Autonomous mobile robots (AMRs) are increasingly used in modern intralogistics systems as complexity and performance requirements become more stringent. One way to increase performance is to improve the operation and cooperation of multiple robots in their shared environment. The paper addresses these problems with a method for off-line route planning and on-line route execution. In the proposed approach, pre-computation of routes for frequent pick-up and drop-off locations limits the movements of AMRs to avoid conflict situations between them. The paper proposes a reinforcement learning approach where an agent builds the routes on a given layout while being rewarded according to different criteria based on the desired characteristics of the system. The results show that the proposed approach performs better in terms of throughput and reliability than the commonly used shortest-path-based approach for a large number of AMRs operating in the system. The use of the proposed approach is recommended when the need for high throughput requires the operation of a relatively large number of AMRs in relation to the size of the space in which the robots operate. Full article
(This article belongs to the Special Issue Multiagent Systems, Learning and Their Applications to Robotic (Mobile, Aerial, Underwater, Industrial and Medical) Systems)
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