Submit to Machines Review for Machines Propose a Special Issue

Journal Menu

Journal Browser

Special Issue "10th Anniversary of Machines—Feature Papers in Mechatronic and Intelligent Machines"

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Published Papers

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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 14319

Share This Special Issue

Special Issue Editors

Prof. Dr. Giuseppe Carbone

E-Mail Website
Guest Editor

Department of Mechanical, Energy and Management Engineering, Università della Calabria, 87036 Rende, Italy
Interests: robotics; robot design; mechatronics; walking hexapod; design procedure; mechanics of machinery; leg–wheel
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Med Amine Laribi

E-Mail Website
Guest Editor

Department GMSC, Institut PPRIME, CNRS, Université de Poitiers, ENSMA, UPR 3346, 86962 Poitiers, France
Interests: mechanical design; robotics; synthesis of mechanism; parallel robots; medical robot; safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our great pleasure to announce this Special Issue to celebrate the 10^th anniversary of Machines. This Special Issue aims to present and circulate recent developments and achievements in mechatronics technologies, which have become essential for developing devices/machines to support human life in our modern society. Related applications include assistive devices for the elderly and disabled, cooperative devices for factory workers, and automation in unstructured environments such as construction fields and farms. This Special Issue invites paper submissions on emerging research and technologies related to mechatronic and intelligent machines (design, control, materials, optimization, diagnosis). Papers are particularly welcome on topics related to theory, design, practice, and applications, including but not limited to the following:

Mechatronic devices and applications
Instrumentation and measurements
Industrial design
Intelligent mechatronic control
Design of intelligent machines

Prof. Dr. Giuseppe Carbone
Dr. Med Amine Laribi
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.

Published Papers (5 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

14 pages, 2678 KiB

Open AccessArticle

Design, Kinematics and Workspace Analysis of a Novel 4-DOF Kinematically Redundant Planar Parallel Grasping Manipulator

and

Machines 2023, 11(3), 319; https://doi.org/10.3390/machines11030319 - 22 Feb 2023

Cited by 1 | Viewed by 1320

Abstract

This article presents a model of a novel 4-DOF kinematically redundant planar parallel grasping manipulator. As distinct from the traditional 4-DOF manipulator, the proposed design includes an extensible platform, which provides kinematic redundancy. This constructive feature is used for grasping. The article discusses the inverse and forward kinematics of the proposed manipulator. The inverse kinematics algorithm provides the analytical relations between the platform coordinates and the driven (controlled) coordinates. The forward kinematics algorithm allows defining different assembly modes of the manipulator. Both algorithms are demonstrated using numerical examples. The article discusses different designs of the manipulator in which its links are placed in one, two, or three layers. Based on these designs, we performed their workspace analyses. Full article

(This article belongs to the Special Issue 10th Anniversary of Machines—Feature Papers in Mechatronic and Intelligent Machines)

► Show Figures

Figure 1

17 pages, 4635 KiB

Open AccessArticle

Fault Diagnosis of Mine Ventilator Bearing Based on Improved Variational Mode Decomposition and Density Peak Clustering

and

Machines 2023, 11(1), 27; https://doi.org/10.3390/machines11010027 - 26 Dec 2022

Cited by 2 | Viewed by 959

Abstract

The mine ventilator plays a role in protecting the life safety of underground workers, which is very significant to the production and development of coal mines. In total, 70% of ventilator failures are mechanical failures, and bearing failures are the most likely to occur in mechanical failures, which are also difficult to find. In order to identify fan bearing faults accurately, this paper proposes a fault diagnosis method based on improved variational mode decomposition and density peak clustering. First, the variational mode decomposition’s modal number K and secondary penalty factor α are chosen employing the improved sparrow optimization process. The bearing vibration signal is decomposed by the variational mode decomposition algorithm with optimized parameters. To create the characteristic vector, the multi-scale permutation entropy of the fourth order intrinsic mode function is determined. Then, the characteristic matrix is dimensionally reduced by kernel principal component analysis, and the two-dimensional matrix after dimensionality reduction is divided by density peak clustering method to find the clustering center of the training sample features. Lastly, the membership degree is assessed using the normalized clustering distance between the characteristic matrix of the test sample and the cluster center of the training sample. The accuracy of bearing fault identification on the self-constructed experimental platform can reach 100%, which verifies the effectiveness and potential of the proposed method. Full article

(This article belongs to the Special Issue 10th Anniversary of Machines—Feature Papers in Mechatronic and Intelligent Machines)

► Show Figures

Figure 1

19 pages, 5715 KiB

Open AccessArticle

Numerical Shape Planning Algorithm for Hyper-Redundant Robots Based on Discrete Bézier Curve Fitting

Ciprian Lapusan

Olimpiu Hancu

and

Ciprian Rad

Machines 2022, 10(10), 894; https://doi.org/10.3390/machines10100894 - 03 Oct 2022

Viewed by 1324

Abstract

The paper proposes a novel numerical method S-GUIDE that provides real-time planning of the shape of hyper-redundant robots with serial architecture by means of a guidance curve, represented in parametrized analytical form and in numerical form by a set of key points associated with the robot structure. To model the shape of the robot, the method uses an equivalent model, and a shape guidance curve obtained through a controlled adjustment of a Bézier curve. This is achieved in three computing steps were the robot equivalent structure, it’s associated kinematic parameters and the robot actuation parameters in joint space are calculated. The proposed method offers several advantages in relation with the precision, computing time and the feasibility for real-time applications. In the paper, the method accuracy, execution time, and the absolute error for different work scenarios are determined, compared and validated. Full article

(This article belongs to the Special Issue 10th Anniversary of Machines—Feature Papers in Mechatronic and Intelligent Machines)

► Show Figures

Figure 1

34 pages, 10494 KiB

Open AccessArticle

EEG-Based Empathic Safe Cobot

Alberto Borboni

Irraivan Elamvazuthi

and

Nicoletta Cusano

Machines 2022, 10(8), 603; https://doi.org/10.3390/machines10080603 - 24 Jul 2022

Cited by 2 | Viewed by 1783

Abstract

An empathic collaborative robot (cobot) was realized through the transmission of fear from a human agent to a robot agent. Such empathy was induced through an electroencephalographic (EEG) sensor worn by the human agent, thus realizing an empathic safe brain-computer interface (BCI). The empathic safe cobot reacts to the fear and in turn transmits it to the human agent, forming a social circle of empathy and safety. A first randomized, controlled experiment involved two groups of 50 healthy subjects (100 total subjects) to measure the EEG signal in the presence or absence of a frightening event. The second randomized, controlled experiment on two groups of 50 different healthy subjects (100 total subjects) exposed the subjects to comfortable and uncomfortable movements of a collaborative robot (cobot) while the subjects’ EEG signal was acquired. The result was that a spike in the subject’s EEG signal was observed in the presence of uncomfortable movement. The questionnaires were distributed to the subjects, and confirmed the results of the EEG signal measurement. In a controlled laboratory setting, all experiments were found to be statistically significant. In the first experiment, the peak EEG signal measured just after the activating event was greater than the resting EEG signal (p < 10⁻³). In the second experiment, the peak EEG signal measured just after the uncomfortable movement of the cobot was greater than the EEG signal measured under conditions of comfortable movement of the cobot (p < 10⁻³). In conclusion, within the isolated and constrained experimental environment, the results were satisfactory. Full article

(This article belongs to the Special Issue 10th Anniversary of Machines—Feature Papers in Mechatronic and Intelligent Machines)

► Show Figures

Figure 1

Review

Jump to: Research

28 pages, 2619 KiB

Open AccessReview

The Expanding Role of Artificial Intelligence in Collaborative Robots for Industrial Applications: A Systematic Review of Recent Works

Alberto Borboni

Karna Vishnu Vardhana Reddy

and

Machines 2023, 11(1), 111; https://doi.org/10.3390/machines11010111 - 13 Jan 2023

Cited by 11 | Viewed by 8124

Abstract

A collaborative robot, or cobot, enables users to work closely with it through direct communication without the use of traditional barricades. Cobots eliminate the gap that has historically existed between industrial robots and humans while they work within fences. Cobots can be used for a variety of tasks, from communication robots in public areas and logistic or supply chain robots that move materials inside a building, to articulated or industrial robots that assist in automating tasks which are not ergonomically sound, such as assisting individuals in carrying large parts, or assembly lines. Human faith in collaboration has increased through human–robot collaboration applications built with dependability and safety in mind, which also enhances employee performance and working circumstances. Artificial intelligence and cobots are becoming more accessible due to advanced technology and new processor generations. Cobots are now being changed from science fiction to science through machine learning. They can quickly respond to change, decrease expenses, and enhance user experience. In order to identify the existing and potential expanding role of artificial intelligence in cobots for industrial applications, this paper provides a systematic literature review of the latest research publications between 2018 and 2022. It concludes by discussing various difficulties in current industrial collaborative robots and provides direction for future research. Full article

(This article belongs to the Special Issue 10th Anniversary of Machines—Feature Papers in Mechatronic and Intelligent Machines)

► Show Figures

Journal Menu

Journal Browser

Special Issue "10th Anniversary of Machines—Feature Papers in Mechatronic and Intelligent Machines"

Share This Special Issue

Special Issue Editors

Special Issue Information

Published Papers (5 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI