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Machines
Special Issues
Recent Trends and Interdisciplinary Applications of AI & Robotics
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Recent Trends and Interdisciplinary Applications of AI & Robotics

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 12432

Special Issue Editor

Dr. Shabnam Sadeghi Esfahlani

E-Mail Website
Guest Editor
Faculty of Science and Engineering, Engineering and the Built Environment, Anglia Ruskin University, Cambridge, UK
Interests: mechatronics and robotics engineering with specific artificial intelligence application; serious games; mechanical engineer

Special Issue Information

Dear Colleagues,

Robotics is a vast topic encompassing interdisciplinary subjects involving robot configuration, manufacture, operation, and services. Robots are becoming increasingly popular in industrial businesses, and it is believed that this preference will never go away, instead growing over time. Artificial intelligence (AI) emulates the human brain and develops intelligent machines capable of conducting processes concerning logic, planning, and knowledge perception with an interleaved set of capabilities, such as invention, emotional skills, and self-awareness.

The fusion of AI and robotics enables the optimisation of systems' level of self-sufficiency, improving accuracy, reliability, efficiency, cost-effectiveness, and competitiveness. Improving the performance in interdisciplinary areas allows systems to envision the future, plan tasks or interact with the world. This could be via learning and acquiring knowledge from sophisticated perceptions based on imitation and experience, through exploitation or navigation.

Thus, it is essential to investigate the recent trends and interdisciplinary applications of AI and robotics, continually optimise systems' performance, analyse human–robot interactions, examine independent systems and implementation, and conceptualise systems with practical, theoretical and experimental knowledge approaches.

This Special Issue will be committed to state-of-the-art research on cutting-edge robotics and AI research in interdisciplinary domains, including but not limited to human–robot interactions, its potential service and entertainment applications, smart city strategy and development, sustainability, renewable energy, healthcare, agriculture, automotive, warehousing, and emerging technologies.

We encourage original manuscript submissions with innovative perspectives and advanced thinking on the theme addressed. Research on theories, simulations, experimentation, and engineering applications is encouraged.

Possible topics include but are not limited to the following:

AI & Machine Learning, Simultaneous Localization and Mapping, Computer Vision, Machine Leaning, Neural Networks, Expert Systems, Machine Perception, Conversational Natural Language, Convolutional/Deep Neural Networks, Fuzzy Systems, Evolutionary Computation, Adaptive Motor Control, Collective Intelligence, Fully/Semi-Autonomous Systems, Cognitive Architecture.

Dr. Shabnam Sadeghi Esfahlani
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. 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

  • automation
  • machine learning
  • robotics
  • autonomous systems
  • computational intelligence
  • machine conceptualization
  • social robotics
  • human–robot interactions
  • cognitive robotics

Published Papers (4 papers)

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Research

18 pages, 4231 KiB  
Article
Research on Vibration Suppression Methods for Industrial Robot Time-Lag Filtering
by Shichang Liu, Chengdong Wu, Liang Liang, Bin Zhao and Ruohuai Sun
Machines 2024, 12(4), 250; https://doi.org/10.3390/machines12040250 - 10 Apr 2024
Viewed by 398
Abstract
This paper analyzes traditional vibration suppression methods in order to solve the vibration problem caused by the stiffness of flexible industrial robots. The principle of closed-loop control dynamic feedforward vibration suppression is described as the main method for solving robot vibration suppression. This [...] Read more.
This paper analyzes traditional vibration suppression methods in order to solve the vibration problem caused by the stiffness of flexible industrial robots. The principle of closed-loop control dynamic feedforward vibration suppression is described as the main method for solving robot vibration suppression. This paper proposes a method for time-lag filtering based on T-trajectory interpolation, which combines the T-planning curve and the time-lag filtering method. The method’s basic principle is to dynamically adjust the trajectory output through the algorithm, which effectively suppresses the amplitude of the harmonic components of a specific frequency band to improve the vibration response of industrial robot systems. This experiment compared traditional vibration suppression methods with the time-lag filtering method based on T-trajectory interpolation. A straight-line method was proposed to measure the degree of vibration. The results demonstrate that the time-lag filtering method based on T-trajectory interpolation is highly effective in reducing the vibration of industrial robots. This makes it an excellent option for scenarios that demand real-time response and high-precision control, ultimately enhancing the efficiency and stability of robots in performing their tasks. Full article
(This article belongs to the Special Issue Recent Trends and Interdisciplinary Applications of AI & Robotics)
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19 pages, 6920 KiB  
Article
Vision-Based Robotic Object Grasping—A Deep Reinforcement Learning Approach
by Ya-Ling Chen, Yan-Rou Cai and Ming-Yang Cheng
Machines 2023, 11(2), 275; https://doi.org/10.3390/machines11020275 - 12 Feb 2023
Cited by 6 | Viewed by 3730
Abstract
This paper focuses on developing a robotic object grasping approach that possesses the ability of self-learning, is suitable for small-volume large variety production, and has a high success rate in object grasping/pick-and-place tasks. The proposed approach consists of a computer vision-based object detection [...] Read more.
This paper focuses on developing a robotic object grasping approach that possesses the ability of self-learning, is suitable for small-volume large variety production, and has a high success rate in object grasping/pick-and-place tasks. The proposed approach consists of a computer vision-based object detection algorithm and a deep reinforcement learning algorithm with self-learning capability. In particular, the You Only Look Once (YOLO) algorithm is employed to detect and classify all objects of interest within the field of view of a camera. Based on the detection/localization and classification results provided by YOLO, the Soft Actor-Critic deep reinforcement learning algorithm is employed to provide a desired grasp pose for the robot manipulator (i.e., learning agent) to perform object grasping. In order to speed up the training process and reduce the cost of training data collection, this paper employs the Sim-to-Real technique so as to reduce the likelihood of damaging the robot manipulator due to improper actions during the training process. The V-REP platform is used to construct a simulation environment for training the deep reinforcement learning neural network. Several experiments have been conducted and experimental results indicate that the 6-DOF industrial manipulator successfully performs object grasping with the proposed approach, even for the case of previously unseen objects. Full article
(This article belongs to the Special Issue Recent Trends and Interdisciplinary Applications of AI & Robotics)
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18 pages, 5557 KiB  
Article
Early Wildfire Smoke Detection Using Different YOLO Models
by Yazan Al-Smadi, Mohammad Alauthman, Ahmad Al-Qerem, Amjad Aldweesh, Ruzayn Quaddoura, Faisal Aburub, Khalid Mansour and Tareq Alhmiedat
Machines 2023, 11(2), 246; https://doi.org/10.3390/machines11020246 - 07 Feb 2023
Cited by 18 | Viewed by 4300
Abstract
Forest fires are a serious ecological concern, and smoke is an early warning indicator. Early smoke images barely capture a tiny portion of the total smoke. Because of the irregular nature of smoke’s dispersion and the dynamic nature of the surrounding environment, smoke [...] Read more.
Forest fires are a serious ecological concern, and smoke is an early warning indicator. Early smoke images barely capture a tiny portion of the total smoke. Because of the irregular nature of smoke’s dispersion and the dynamic nature of the surrounding environment, smoke identification is complicated by minor pixel-based traits. This study presents a new framework that decreases the sensitivity of various YOLO detection models. Additionally, we compare the detection performance and speed of different YOLO models such as YOLOv3, YOLOv5, and YOLOv7 with prior ones such as Fast R-CNN and Faster R-CNN. Moreover, we follow the use of a collected dataset that describes three distinct detection areas, namely close, medium, and far distance, to identify the detection model’s ability to recognize smoke targets correctly. Our model outperforms the gold-standard detection method on a multi-oriented dataset for detecting forest smoke by an mAP accuracy of 96.8% at an IoU of 0.5 using YOLOv5x. Additionally, the findings of the study show an extensive improvement in detection accuracy using several data-augmentation techniques. Moreover, YOLOv7 outperforms YOLOv3 with an mAP accuracy of 95%, compared to 94.8% using an SGD optimizer. Extensive research shows that the suggested method achieves significantly better results than the most advanced object-detection algorithms when used on smoke datasets from wildfires, while maintaining a satisfactory performance level in challenging environmental conditions. Full article
(This article belongs to the Special Issue Recent Trends and Interdisciplinary Applications of AI & Robotics)
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17 pages, 7794 KiB  
Article
A SLAM-Based Localization and Navigation System for Social Robots: The Pepper Robot Case
by Tareq Alhmiedat, Ashraf M. Marei, Wassim Messoudi, Saleh Albelwi, Anas Bushnag, Zaid Bassfar, Fady Alnajjar and Abdelrahman Osman Elfaki
Machines 2023, 11(2), 158; https://doi.org/10.3390/machines11020158 - 23 Jan 2023
Cited by 7 | Viewed by 2814
Abstract
Robot navigation in indoor environments has become an essential task for several applications, including situations in which a mobile robot needs to travel independently to a certain location safely and using the shortest path possible. However, indoor robot navigation faces challenges, such as [...] Read more.
Robot navigation in indoor environments has become an essential task for several applications, including situations in which a mobile robot needs to travel independently to a certain location safely and using the shortest path possible. However, indoor robot navigation faces challenges, such as obstacles and a dynamic environment. This paper addresses the problem of social robot navigation in dynamic indoor environments, through developing an efficient SLAM-based localization and navigation system for service robots using the Pepper robot platform. In addition, this paper discusses the issue of developing this system in a way that allows the robot to navigate freely in complex indoor environments and efficiently interact with humans. The developed Pepper-based navigation system has been validated using the Robot Operating System (ROS), an efficient robot platform architecture, in two different indoor environments. The obtained results show an efficient navigation system with an average localization error of 0.51 m and a user acceptability level of 86.1%. Full article
(This article belongs to the Special Issue Recent Trends and Interdisciplinary Applications of AI & Robotics)
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