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Applied Sciences
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Robotics and Intelligent Systems: Technologies and Applications
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Robotics and Intelligent Systems: Technologies and Applications

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

Deadline for manuscript submissions: 20 June 2025 | Viewed by 700

Special Issue Editors

Dr. Qiang Liu

E-Mail Website
Guest Editor
School of Engineering Mathematics, Technology University of Bristol, Bristol, UK
Interests: robotics; intelligent systems; autonomous systems; machine learning in robotics; robot grasping; human-robot interaction; swarm robotics; robotic sensing and perception; adaptive control systems; robotic applications
Prof. Dr. Alessandro Gasparetto

E-Mail Website
Guest Editor
Polytechnic Department of Engineering and Architecture, University of Udine, 33100 Udine, Italy
Interests: robotics; trajectory planning; modeling of mechatronic systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to showcase groundbreaking advancements and interdisciplinary research in robotics and intelligent systems. The rapid evolution of these technologies has profoundly influenced a wide array of industries, including healthcare, manufacturing, transportation, agriculture, and more. This Special Issue invites innovative contributions that explore the design, development, and deployment of intelligent robotic systems, with an emphasis on their practical applications and societal impact.

Potential topics include, but are not limited to, autonomous systems, machine learning in robotics, robot grasping, human–robot interaction, robotic perception and sensing, swarm robotics, and adaptive control systems. By bridging theoretical advancements with real-world applications, this issue seeks to foster collaboration among researchers, engineers, and practitioners.

We encourage submissions presenting novel methodologies, case studies, and emerging technologies that address challenges in robotics and intelligent systems. This Special Issue aims to serve as a platform for disseminating cutting-edge knowledge and inspiring future innovations in this transformative field.

Dr. Qiang Liu
Prof. Dr. Alessandro Gasparetto
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

  • robotics
  • intelligent systems
  • autonomous systems
  • machine learning in robotics
  • robot grasping
  • human–robot interaction
  • swarm robotics
  • robotic sensing and perception
  • adaptive control systems
  • robotic applications

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Published Papers (2 papers)

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Research

23 pages, 6376 KiB  
Article
Deep Reinforcement Learning-Based Uncalibrated Visual Servoing Control of Manipulators with FOV Constraints
by Xungao Zhong, Qiao Zhou, Yuan Sun, Shaobo Kang and Huosheng Hu
Appl. Sci. 2025, 15(8), 4447; https://doi.org/10.3390/app15084447 - 17 Apr 2025
Viewed by 99
Abstract
In this article, we put forward a brand-new uncalibrated image-based visual servoing (IBVS) method. It is designed for monocular hand–eye manipulators with Field-of-View (FOV) feature constraints and makes use of a deep reinforcement learning (DRL) approach. First, the IBVS and its feature-loss problems [...] Read more.
In this article, we put forward a brand-new uncalibrated image-based visual servoing (IBVS) method. It is designed for monocular hand–eye manipulators with Field-of-View (FOV) feature constraints and makes use of a deep reinforcement learning (DRL) approach. First, the IBVS and its feature-loss problems are introduced. Then, a uncalibrated IBVS method is presented to address the feature-loss issue and improve servo efficiency with DRL. Specifically, the uncalibrated IBVS is integrated into the deep Q-network (DQN) control framework to ensure analytical stability. Additionally, a feature-constrained Q-network based on offline camera FOV environment feature mapping is designed and trained to adaptively output compensation for the IBVS controller, which helps maintain the feature within the camera’s FOV and improve servo performance. Finally, to further demonstrate the effectiveness and practicality of the proposed DQN-based uncalibrated IBVS method, experiments are conducted on a 6-DOF manipulator, and the results validate the proposed approach. Full article
(This article belongs to the Special Issue Robotics and Intelligent Systems: Technologies and Applications)
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18 pages, 26619 KiB  
Article
A Framework for 3D Plant Simulation of Meal-Kit-Packaging Robot Automation System
by Tae Hyong Kim, Byoung Il Gu, Ki Hyun Kwon and Ah-Na Kim
Appl. Sci. 2025, 15(8), 4116; https://doi.org/10.3390/app15084116 - 9 Apr 2025
Viewed by 265
Abstract
A data-driven 3D simulation for the robotic automation of the most labor-intensive packaging process in meal kit production was developed using Tecnomatix plant simulation software. The workflow and environments of the existing manual process were analyzed. An existing production site was scanned using [...] Read more.
A data-driven 3D simulation for the robotic automation of the most labor-intensive packaging process in meal kit production was developed using Tecnomatix plant simulation software. The workflow and environments of the existing manual process were analyzed. An existing production site was scanned using a 3D Lidar scanner to create 3D models and design the initial assembly layout. Two types of 3D simulation models, implemented with a single or double delta robot, were designed to determine the optimal robot-automated packaging process. Key performance indicators for simulation models of a manual and two robot automation systems were analyzed. The throughputs of the manual, single delta robot and double delta robot models were 2112, 1510, and 2568 ea/h, respectively. The single robot system achieved only 68.3% of the throughput of the manual process, which is attributed to a cycle time of 2.36 s for picking and placing all components. On the other hand, the cycle time of the double robot system was 1.66 times faster, and the throughput was 1.7 times greater compared to the single robot system. The developed 3D simulation model for the meal kit packaging system demonstrates the potential of robotic automation in addressing the labor shortage issue as well as improving production efficiency. Full article
(This article belongs to the Special Issue Robotics and Intelligent Systems: Technologies and Applications)
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