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Machines
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Recent Developments in Machine Design, Automation and Robotics, Second Edition
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Recent Developments in Machine Design, Automation and Robotics, Second Edition

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machine Design and Theory".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 19043

Special Issue Editor

Dr. Raul D. S. G. Campilho

E-Mail Website
Guest Editor
Department of Mechanical Engineering, ISEP-School of Engineering, Polytechnic of Porto, R. Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal
Interests: composite materials; joining processes; automation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The competitiveness of companies in the global market largely depends on the efficiency of their industrial processes, which rely on technologically advanced machines and equipment. Moreover, through the extensive use of automation and robotics, it is possible to attain the required product quality and production flexibility to adapt to new reference products, increase production rates, and lower fabrication costs. Over time, automation and robotics have become the best way to achieve the goals of the market. Therefore, these technologies are subject to continuous evolution, with new solutions constantly being presented. There have been many recent advances and developments, both academically and industrially, and this Special Issue wishes to emphasize the following:

  • Collaborative robotics (cobots): human–robot collaboration in industrial settings, safety protocols and advancements in cobot technology, and cobots in small and medium-sized enterprises.
  • Advanced control systems in automation: adaptive and predictive control algorithms, real-time control strategies for industrial processes, and the integration of AI and machine learning into control systems.
  • Additive manufacturing for machine design: The use of 3D printing in designing and manufacturing machine parts, optimization, and material advancements in additive manufacturing.
  • Smart factories and Industry 4.0: Internet of Things (IoT) applications in manufacturing, cyber–physical systems and their role in modern factories, and digital twins for predictive maintenance and optimization.
  • Sustainable manufacturing and green design: energy-efficient design and automation, recycling and eco-friendly materials in machine design, and sustainable practices in industrial robotics and automation.
  • Machine learning in robotics: reinforcement learning for robotic applications, vision-based learning and object recognition in robotics, and autonomous decision-making in robotic systems.

This Special Issue intends to bring together and publish a significant number of high-quality original works in the field and subsequently promote them by disseminating them through MDPI’s open access system.

Prof. Dr. Raul D. S. G. Campilho
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 250 words) can be sent to the Editorial Office for assessment.

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

  • machine design
  • industrial automation
  • industrial robotics
  • collaborative robotics
  • advanced control systems
  • additive manufacturing
  • smart factory
  • Industry 4.0
  • Internet of Things (IoT)
  • sustainable manufacturing
  • green design
  • machine learning in robotics
  • automation technologies
  • robotics integration
  • adaptive control systems

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Related Special Issue

Published Papers (12 papers)

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Research

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24 pages, 2506 KB  
Article
A Predictive Maintenance Approach for Composting Plants Based on ERP and Digital Twin Integration
by Hamed Nozari and Agnieszka Szmelter-Jarosz
Machines 2025, 13(12), 1123; https://doi.org/10.3390/machines13121123 - 6 Dec 2025
Viewed by 240
Abstract
This study presents an integrated predictive maintenance framework for industrial machinery, designed through the combined use of digital twin technology, enterprise resource planning (ERP) systems, and machine learning algorithms. The proposed system focuses on enhancing machine reliability and operational automation by connecting physical [...] Read more.
This study presents an integrated predictive maintenance framework for industrial machinery, designed through the combined use of digital twin technology, enterprise resource planning (ERP) systems, and machine learning algorithms. The proposed system focuses on enhancing machine reliability and operational automation by connecting physical assets with their virtual counterparts and management systems. The digital twin acts as a real-time virtual model of critical equipment—such as aeration motors, mixers, and reactors—enabling continuous monitoring, dynamic simulation, and predictive fault detection. Meanwhile, the ERP system provides an integrated environment for maintenance scheduling, data management, and resource allocation, ensuring that maintenance decisions are data-driven and synchronized with operational workflows. Machine learning algorithms, implemented using hybrid physical–data models, predict equipment degradation trends and optimize maintenance interventions. The proposed framework was validated in an industrial-scale composting facility, where results demonstrated a 40% increase in mean time to failure (MTTF), a 35% reduction in repair time, and a 30% decrease in maintenance costs, resulting in a return on investment of 42.5% within the first year. The system’s modular architecture and high adaptability to different machinery types confirm its potential applicability to broader machine design and automation contexts, supporting the transition toward intelligent, self-maintaining industrial systems. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)
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28 pages, 7719 KB  
Article
A Digital Twin Model for UAV Control to Lift Irregular-Shaped Payloads Using Robust Model Predictive Control
by Umar Farid, Bilal Khan, Sahibzada Muhammad Ali and Zahid Ullah
Machines 2025, 13(11), 1069; https://doi.org/10.3390/machines13111069 - 20 Nov 2025
Viewed by 587
Abstract
This paper presents an innovative digital twin (DT) model integrated with robust model predictive control (MPC) to enhance the performance of an unmanned air vehicle (UAV) tasked with lifting and transporting irregular-shaped payloads. Traditional UAV control systems face complex challenges in stability and [...] Read more.
This paper presents an innovative digital twin (DT) model integrated with robust model predictive control (MPC) to enhance the performance of an unmanned air vehicle (UAV) tasked with lifting and transporting irregular-shaped payloads. Traditional UAV control systems face complex challenges in stability and accuracy when dealing with asymmetrical payloads, as such payloads cause continuous shifts in the center of gravity (CoG) and variable inertial forces, which lead to unpredictable flight dynamics. The proposed DT framework enables the creation of a real-time replica of the UAV payload system. It creates an adaptive control environment that anticipates and mitigates disturbances before they impact the stability of the UAV during a mission. By combining a DT with MPC, the control system dynamically adjusts to variations in payload characteristics, namely (a) changes in mass distribution and (b) aerodynamic drag force. As a result, a stable flight path is ensured even under challenging environmental conditions. The DT model continuously forecasts potential destabilizing events and modifies MPC constraints to accommodate complex shifting dynamics, achieving improved control accuracy and energy efficiency. Extensive simulations across various hanging payload configurations and environmental disturbance scenarios validate the effectiveness of the proposed model. The simulation results show that the DT-MPC strategy significantly improves stability, control precision, and energy conservation, outperforming conventional methods. A comparative analysis is also carried out with a conventional control scheme to validate the robustness of the proposed framework. This research work advances the development of intelligent, autonomous UAV systems capable of reliably managing complex and irregularly shaped payloads with varying mass distributions in real-world scenarios, thereby broadening their potential applications in logistics, emergency response, and industrial transportation. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)
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27 pages, 6674 KB  
Article
Design and Development of an Autonomous Mobile Robot for Unstructured Indoor Environments
by Ameur Gargouri, Mohamed Karray, Bechir Zalila and Mohamed Ksantini
Machines 2025, 13(11), 1044; https://doi.org/10.3390/machines13111044 - 12 Nov 2025
Viewed by 1478
Abstract
This research work presents the design and the development of a cost-effective autonomous mobile robot for locating misplaced objects within unstructured indoor environments. The tools integrated into the proposed system for perception and localization are a hardware architecture equipped with LiDAR, an inertial [...] Read more.
This research work presents the design and the development of a cost-effective autonomous mobile robot for locating misplaced objects within unstructured indoor environments. The tools integrated into the proposed system for perception and localization are a hardware architecture equipped with LiDAR, an inertial measurement unit (IMU), and wheel encoders. The system also includes an ROS2-based software stack enabling autonomous navigation via the NAV2 framework and Adaptive Monte Carlo Localization (AMCL). For real-time object detection, a lightweight YOLO11n model is developed and implemented on a Raspberry Pi 4 to enable the robot to identify common household items. The robot’s motion control is achieved by a fuzzy logic-enhanced PID controller that dynamically modifies gain values based on navigation conditions. Remote supervision, task management, and real-time status monitoring are provided by a user-friendly Flutter-based mobile application. Simulations and real-world experiments demonstrate the robustness, modularity, and responsiveness of the robot in dynamic environments. This robot achieves a 3 cm localization error and a 95% task execution success rate. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)
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23 pages, 4947 KB  
Article
Graded Evaluation and Optimal Scheme Selection of Mine Rock Diggability Based on the Multidimensional Cloud Model
by Shibin Yao, Xiaoyuan Li, Jian Zhou and Manoj Khandelwal
Machines 2025, 13(11), 1019; https://doi.org/10.3390/machines13111019 - 3 Nov 2025
Viewed by 345
Abstract
With the advancement of mining technologies, the evaluation of rock diggability has become a critical research topic for ensuring both safety and efficiency in mining operations. This study establishes a comprehensive evaluation system for mine rock diggability and proposes corresponding grading criteria. For [...] Read more.
With the advancement of mining technologies, the evaluation of rock diggability has become a critical research topic for ensuring both safety and efficiency in mining operations. This study establishes a comprehensive evaluation system for mine rock diggability and proposes corresponding grading criteria. For the determination of indicator weights, a combination of subjective and objective methods is employed, integrating expert knowledge and data characteristics to identify optimal weights, thereby providing a reliable basis for comprehensive evaluation. The single-indicator cloud model effectively mitigates the difficulties associated with defining transitional values between adjacent intervals. The multidimensional cloud model, by considering the interactions among indicators, enables the optimization of indicator interactions and enhances the interpretability of diggability grades. Comparison with the Diggability Index (DI) method shows a high consistency between the two approaches (R2 = 0.991). The absolute accuracy of diggability levels reaches 74%, while the accuracy based on cloud model fuzzy evaluation reaches 100%, demonstrating the effectiveness of the cloud model in handling transitional intervals and capturing uncertainty. This study provides a novel methodology and theoretical foundation for the scientific evaluation of mine rock diggability, offering practical guidance for reasonable grading, optimization of mining parameters, and interpretation of diggability levels in engineering practice. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)
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18 pages, 10019 KB  
Article
Belt Sanding Robot for Large Convex Surfaces Featuring SEA Arms and an Active Re-Tensioner with PI Force Control
by Hongjoo Jin, Chanhyuk Moon, Taegyun Kim and TaeWon Seo
Machines 2025, 13(11), 1012; https://doi.org/10.3390/machines13111012 - 2 Nov 2025
Viewed by 479
Abstract
This study presents a belt sanding robot for large convex surfaces together with a proportional–integral force control method. Sanding belt tension strongly affects area coverage and spatial normal-force uniformity on large curved surfaces; existing approaches typically use fixed tool positions or lack active [...] Read more.
This study presents a belt sanding robot for large convex surfaces together with a proportional–integral force control method. Sanding belt tension strongly affects area coverage and spatial normal-force uniformity on large curved surfaces; existing approaches typically use fixed tool positions or lack active tension regulation, which limits coverage and makes force distribution difficult to control. The mechanism consists of two series elastic actuator arms and an active re-tensioner that adjusts belt tension during contact. In contrast to a conventional belt sander, the series elastic configuration enables indirect estimation of the reaction force without load cells and provides compliant interaction with contact transients. The system is evaluated on curved steel plates using vertical scans with a belt width of 50 mm and a drive wheel speed of 300 rpm. Performance is reported for two target curvature values, namely 0.47 and 1.37, with five trials for each condition. The control objective is a constant normal force along the contact, achieved through proportional–integral control of the arms for normal-force tracking and the re-tensioner for belt tension regulation. To quantify spatial force uniformity, the distribution rate is defined as the ratio of the difference between the maximum and minimum normal forces to the maximum normal force measured across the belt–workpiece contact region. Compared with a simple belt sander baseline, the proposed system increased the sanded area coverage by 31.85%, from 62.20% to 94.05%, at the curvature value of 0.47, and by 8.49%, from 81.21% to 89.70%, at the curvature value of 1.37. The distribution rate improved by 113% at the curvature value of 0.47 and by 16.7% at the curvature value of 1.37. Under identical operating conditions of 50 mm belt width, 300 rpm, and five repeated trials, these results indicate higher area coverage and more uniform force distribution relative to the baseline. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)
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26 pages, 5753 KB  
Article
An Optimized Few-Shot Learning Framework for Fault Diagnosis in Milling Machines
by Faisal Saleem, Muhammad Umar and Jong-Myon Kim
Machines 2025, 13(11), 1010; https://doi.org/10.3390/machines13111010 - 2 Nov 2025
Viewed by 687
Abstract
Reliable fault diagnosis of milling machines is essential for maintaining operational stability and cost-effective maintenance; however, it remains challenging due to limited labeled data and the highly non-stationary nature of acoustic emission (AE) signals. This study introduces an optimized Few-Shot Learning framework (FSL) [...] Read more.
Reliable fault diagnosis of milling machines is essential for maintaining operational stability and cost-effective maintenance; however, it remains challenging due to limited labeled data and the highly non-stationary nature of acoustic emission (AE) signals. This study introduces an optimized Few-Shot Learning framework (FSL) that integrates time–frequency analysis with attention-guided representation learning and distribution-aware classification for data-efficient fault detection. The framework converts AE signals into Continuous Wavelet Transform (CWT) scalograms, which are processed using a self-attention-enhanced ResNet-50 backbone to capture both local texture features and long-range dependencies in the signal. Adaptive prototype computation with learnable importance weighting refines class representations, while Mahalanobis distance-based matching ensures robust alignment between query and prototype embeddings under limited sample conditions. To further strengthen discriminability, contrastive loss with hard negative mining enforces compact intra-class clustering and clear inter-class separation. Comprehensive experiments under 7-way 5-shot settings and 5-fold stratified cross-validation demonstrate consistent and reliable performance, achieving a mean accuracy of 98.86% ± 0.97% (95% CI: [98.01%, 99.71%]). Additional evaluations across multiple spindle speeds (660 rpm and 1440 rpm) confirm that the model generalizes effectively under varying operating conditions. Grad-CAM++ activation maps further illustrate that the network focuses on physically meaningful fault-related regions, enhancing interpretability. The results verify that the proposed framework achieves robust, scalable, and interpretable fault diagnosis using minimal labeled data, offering a practical solution for predictive maintenance in modern intelligent manufacturing environments. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)
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21 pages, 3933 KB  
Article
Mechanical Design and Experimental Study of a Small-Scale Wind Turbine Model
by Eduardo Muñoz-Palomeque, Segundo Esteban and Matilde Santos
Machines 2025, 13(10), 929; https://doi.org/10.3390/machines13100929 - 8 Oct 2025
Viewed by 1527
Abstract
The advancement of onshore and offshore wind turbines depends on the experimental validation of new technologies, novel component designs, and innovative concepts. However, full-scale models are typically very expensive, have limited functionality, and are difficult to adapt to diverse research needs. To address [...] Read more.
The advancement of onshore and offshore wind turbines depends on the experimental validation of new technologies, novel component designs, and innovative concepts. However, full-scale models are typically very expensive, have limited functionality, and are difficult to adapt to diverse research needs. To address this shortcoming, this article presents the design of a low-cost, modular 3D-printed small prototype of a wind turbine. It includes a multi-hollow platform for marine environments configuration and stabilization, the turbine tower, and three blades with active pitch control, not always included in wind turbine prototypes. The modular tower design allows for easy height extensions, while the rotor incorporates custom blades optimized for the prototype geometry and experimental setup. Tests were conducted to evaluate the system’s operational response and verify the proper functioning of the assembled components at various wind speeds and blade pitch angles. The results confirm that the rotor speed with the prototype’s onshore configuration is highly pitch-dependent, reaching a maximum efficiency of approximately 5°. The tower displacement, measured with an IMU, remained within a narrow range, oscillating around 2° and reaching up to 4° at higher wind speeds due to elastic deflections of the PLA structure. These results, consistent with the prototype scale, validate its usefulness in capturing essential aerodynamic and structural behaviors of the wind turbine. They also demonstrate its relevance as a new tool for experimental studies of wind turbines and open up new research, validation, and control possibilities not considered in previous developments by incorporating blade pitch control. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)
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19 pages, 1584 KB  
Article
The Development of a Predictive Maintenance System for Gearboxes Through a Statistical Diagnostic Analysis of Lubricating Oil and Artificial Intelligence
by Diego Rigolli, Lorenzo Pompei, Massimo Manfredini, Massimiliano Vignoli, Vincenzo La Battaglia and Alessandro Giorgetti
Machines 2025, 13(8), 693; https://doi.org/10.3390/machines13080693 - 6 Aug 2025
Viewed by 1961
Abstract
This paper addressed the problem of oil diagnostics lubricants applied to the predictive maintenance of industrial gearboxes, proposing the integration of an artificial intelligence (AI) system into the process analysis. The main objective was to overcome the critical issues of the traditional method, [...] Read more.
This paper addressed the problem of oil diagnostics lubricants applied to the predictive maintenance of industrial gearboxes, proposing the integration of an artificial intelligence (AI) system into the process analysis. The main objective was to overcome the critical issues of the traditional method, characterized by long analysis times and a marked dependence on the subjective interpretation of operators. The method includes a detailed statistical analysis of the common ways to assess the condition of lubricants, such as optical emission spectroscopy, particle counting, measuring viscosity and density, and Fourier-transform infrared spectroscopy (FT-IR). These methods are then combined with an artificial intelligence model. Tested on commercial gearbox data, the proposed approach demonstrates agreement between IA and expert evaluation. The application has shown that it can effectively support diagnoses, reduce processing time by 60%, and minimize human errors. It also improves knowledge sharing through an increase in the stability and repetitiveness of diagnoses and promotes consistency and clarity in reporting. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)
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Review

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36 pages, 3105 KB  
Review
Reinforcement Learning for Industrial Automation: A Comprehensive Review of Adaptive Control and Decision-Making in Smart Factories
by Yasser M. Alginahi, Omar Sabri and Wael Said
Machines 2025, 13(12), 1140; https://doi.org/10.3390/machines13121140 - 15 Dec 2025
Viewed by 34
Abstract
The accelerating integration of Artificial Intelligence (AI) in Industrial Automation has established Reinforcement Learning (RL) as a transformative paradigm for adaptive control, intelligent optimization, and autonomous decision-making in smart factories. Despite the growing literature, existing reviews often emphasize algorithmic performance or domain-specific applications, [...] Read more.
The accelerating integration of Artificial Intelligence (AI) in Industrial Automation has established Reinforcement Learning (RL) as a transformative paradigm for adaptive control, intelligent optimization, and autonomous decision-making in smart factories. Despite the growing literature, existing reviews often emphasize algorithmic performance or domain-specific applications, neglecting broader links between methodological evolution, technological maturity, and industrial readiness. To address this gap, this study presents a bibliometric review mapping the development of RL and Deep Reinforcement Learning (DRL) research in Industrial Automation and robotics. Following the PRISMA 2020 protocol to guide the data collection procedures and inclusion criteria, 672 peer-reviewed journal articles published between 2017 and 2026 were retrieved from Scopus, ensuring high-quality, interdisciplinary coverage. Quantitative bibliometric analyses were conducted in R using Bibliometrix and Biblioshiny, including co-authorship, co-citation, keyword co-occurrence, and thematic network analyses, to reveal collaboration patterns, influential works, and emerging research trends. Results indicate that 42% of studies employed DRL, 27% focused on Multi-Agent RL (MARL), and 31% relied on classical RL, with applications concentrated in robotic control (33%), process optimization (28%), and predictive maintenance (19%). However, only 22% of the studies reported real-world or pilot implementations, highlighting persistent challenges in scalability, safety validation, interpretability, and deployment readiness. By integrating a review with bibliometric mapping, this study provides a comprehensive taxonomy and a strategic roadmap linking theoretical RL research with practical industrial applications. This roadmap is structured across four critical dimensions: (1) Algorithmic Development (e.g., safe, explainable, and data-efficient RL), (2) Integration Technologies (e.g., digital twins and IoT), (3) Validation Maturity (from simulation to real-world pilots), and (4) Human-Centricity (addressing trust, collaboration, and workforce transition). These insights can guide researchers, engineers, and policymakers in developing scalable, safe, and human-centric RL solutions, prioritizing research directions, and informing the implementation of Industry 5.0–aligned intelligent automation systems emphasizing transparency, sustainability, and operational resilience. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)
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19 pages, 1375 KB  
Review
Recent Developments in Electroadhesion Grippers for Automated Fruit Grasping
by Turac I. Ozcelik, Enrico Masi, Seyyed Masoud Kargar, Chiara Scagliarini, Adyan Fatima, Rocco Vertechy and Giovanni Berselli
Machines 2025, 13(12), 1128; https://doi.org/10.3390/machines13121128 - 8 Dec 2025
Viewed by 206
Abstract
As global food demand rises and agricultural labor shortages intensify, robotic automation has become essential for sustainable fruit grasping. Among emerging technologies, ElectroAdhesion (EA) grippers offer a promising alternative to traditional mechanical end-effectors, enabling gentle, low-pressure handling through electrostatically induced adhesion. This paper [...] Read more.
As global food demand rises and agricultural labor shortages intensify, robotic automation has become essential for sustainable fruit grasping. Among emerging technologies, ElectroAdhesion (EA) grippers offer a promising alternative to traditional mechanical end-effectors, enabling gentle, low-pressure handling through electrostatically induced adhesion. This paper presents a methodical review of EA grippers applied to fruit grasping, focusing on their advantages, limitations, and key design considerations. A targeted literature search identified ten EA-based and hybrid EA gripping systems tested on fruit manipulation, though none has yet been tested in real-world environments such as fields or greenhouses. Despite a significant variability in experimental setups, materials, and grasp types, qualitative insights are drawn from our analysis demonstrating the potentialities of EA technologies. The EA grippers found in the targeted review are effective on diverse fruits, shapes, and surface textures; they can hold load capacities ranging from 10 g (~0.1 N) to 600 g (~6 N) and provide minimal compressive stress at high electrostatic shear forces. Along with custom EA grippers designed accordingly to specific use cases, field and greenhouse testing will be crucial for advancing the technology readiness level of EA grippers and unlocking their full potential in automated crop harvesting. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)
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34 pages, 999 KB  
Review
Robotic Prostheses and Neuromuscular Interfaces: A Review of Design and Technological Trends
by Pedro Garcia Batista, André Costa Vieira and Pedro Dinis Gaspar
Machines 2025, 13(9), 804; https://doi.org/10.3390/machines13090804 - 3 Sep 2025
Viewed by 5343
Abstract
Neuromuscular robotic prostheses have emerged as a critical convergence point between biomedical engineering, machine learning, and human–machine interfaces. This work provides a narrative state-of-the-art review regarding recent developments in robotic prosthetic technology, emphasizing sensor integration, actuator architectures, signal acquisition, and algorithmic strategies for [...] Read more.
Neuromuscular robotic prostheses have emerged as a critical convergence point between biomedical engineering, machine learning, and human–machine interfaces. This work provides a narrative state-of-the-art review regarding recent developments in robotic prosthetic technology, emphasizing sensor integration, actuator architectures, signal acquisition, and algorithmic strategies for intent decoding. Special focus is given to non-invasive biosignal modalities, particularly surface electromyography (sEMG), as well as invasive approaches involving direct neural interfacing. Recent developments in AI-driven signal processing, including deep learning and hybrid models for robust classification and regression of user intent, are also examined. Furthermore, the integration of real-time adaptive control systems with surgical techniques like Targeted Muscle Reinnervation (TMR) is evaluated for its role in enhancing proprioception and functional embodiment. Finally, this review highlights the growing importance of modular, open-source frameworks and additive manufacturing in accelerating prototyping and customization. Progress in this domain will depend on continued interdisciplinary research bridging artificial intelligence, neurophysiology, materials science, and real-time embedded systems to enable the next generation of intelligent prosthetic devices. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)
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30 pages, 2660 KB  
Review
A Scoping Review of Energy Consumption in Industrial Robotics
by Johannes Muru and Anton Rassõlkin
Machines 2025, 13(7), 542; https://doi.org/10.3390/machines13070542 - 23 Jun 2025
Cited by 1 | Viewed by 4788
Abstract
The increasing adoption of industrial robots has significantly advanced manufacturing efficiency and flexibility. However, this expansion introduces new energy consumption challenges, especially as electricity has become the dominant energy source in automated systems. As the industrial sector faces rising energy costs and ambitious [...] Read more.
The increasing adoption of industrial robots has significantly advanced manufacturing efficiency and flexibility. However, this expansion introduces new energy consumption challenges, especially as electricity has become the dominant energy source in automated systems. As the industrial sector faces rising energy costs and ambitious sustainability goals, understanding and minimizing the energy consumption of robotic systems is imperative. This review presents a structured analysis of energy consumption in industrial robots, linking mechanical design, actuation systems, and control strategies to their energetic effects. We first discuss different industrial robot types and their kinematic configurations, identifying how structural characteristics influence energy use. The article then categorizes energy consumption optimization strategies into software-based and hardware-based approaches. A comparative SWOT analysis highlights the strengths and limitations of each approach. The review also explores emerging trends such as DC microgrid integration. The future directions underline the need for standardized energy assessment frameworks and the development of hybrid optimization strategies that combine the reviewed approaches, suitable for being applied in real-world industrial robot applications. This work provides a comprehensive foundation for establishing best practices in energy consumption optimization for industrial robots. Full article
(This article belongs to the Special Issue Recent Developments in Machine Design, Automation and Robotics, Second Edition)
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