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9.1
5.1
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Informatics
Special Issues
Advances in Artificial Intelligence, Robotics, and Control
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Advances in Artificial Intelligence, Robotics, and Control

A Special Issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: 30 May 2027 | Viewed by 1094

Editors

Dr. Weitian Tong

E-Mail Website
Guest Editor
Department of Computer Science, Georgia Southern University, Statesboro, GA 30458, USA
Interests: algorithm design; artificial intelligence; data science; operations research
Special Issues, Collections and Topics in MDPI journals
Dr. Lei Chen

E-Mail Website
Guest Editor
School of Computing, Georgia Southern University, Statesboro, GA 30458, USA
Interests: cybersecurity; machine learning; digital forensics
Special Issues, Collections and Topics in MDPI journals
Dr. Lixin Li

E-Mail Website
Guest Editor
School of Computing, Georgia Southern University, Statesboro, GA 30458, USA
Interests: machine learning; spatiotemporal interpolation; algorithms; GIS

Special Issue Information

Dear Colleagues,

In 2026, the 7th International Conference on Artificial Intelligence, Robotics, and Control (AIRC 2026) will bring together researchers and practitioners working across the fields of artificial intelligence, intelligent robotics, and control engineering. In cooperation with AIRC 2026, this Special Issue will publish high-quality extended papers and original contributions that advance theory, algorithms, systems, and real-world applications in these closely connected areas.

We welcome submissions on, but not limited to, machine learning, large language models, computer vision, generative AI, explainable and trustworthy AI, human–robot interaction, autonomous robots, robotic perception, multi-robot systems, cyber–physical systems, intelligent manufacturing, optimal and robust control, data-driven control, intelligent transportation, production planning, scheduling, and related interdisciplinary topics. Both extended versions of conference papers and new external submissions are welcome, provided that all manuscripts contain substantial original contributions and satisfy the journal's peer-review standards.

This Special Issue will provide a forum for disseminating recent advances, emerging challenges, and innovative applications that connect informatics with AI-enabled robotics and intelligent control systems.

Dr. Weitian Tong
Dr. Lei Chen
Dr. Lixin Li
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 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Informatics 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 1800 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

  • artificial intelligence
  • intelligent robotics
  • control engineering
  • machine learning
  • computer vision
  • large language models
  • autonomous systems
  • cyber–physical systems
  • intelligent manufacturing
  • optimization and scheduling

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

18 pages, 3526 KB  
Article
Learning-Based Data-Driven Heading Control for Unmanned Surface Vehicles: A Nussbaum-RBF Sliding Mode Approach
by Jianlin Zhou, Yuhao Dai, Wentao Xue and Wei Liu
Informatics 2026, 13(9), 144; https://doi.org/10.3390/informatics13090144 - 7 Sep 2026
Abstract
Owing to unknown hydrodynamic characteristics, unmeasurable external disturbances and uncertain control coefficients, conventional control strategies that rely on precise mechanistic models often suffer from notable performance degradation. To address this problem, a novel data-driven Nussbaum-RBF Sliding Mode Control (NRSMC) strategy is proposed for [...] Read more.
Owing to unknown hydrodynamic characteristics, unmeasurable external disturbances and uncertain control coefficients, conventional control strategies that rely on precise mechanistic models often suffer from notable performance degradation. To address this problem, a novel data-driven Nussbaum-RBF Sliding Mode Control (NRSMC) strategy is proposed for USV heading control. In the proposed framework, a Radial Basis Function (RBF) neural network is employed as a data-driven approximator to learn unknown nonlinear system dynamics online, avoiding the dependence on accurate prior mathematical models. Based on the online-learned dynamic information, a sliding mode control (SMC) mechanism is developed to enhance robustness against approximation errors and external disturbances, and a boundary layer technique is introduced to alleviate the chattering phenomenon. Furthermore, a Nussbaum function is incorporated to address the unknown control coefficients problem, ensuring system stability without requiring prior knowledge of the control coefficients. The stability of the closed-loop system is rigorously analyzed using Lyapunov theory. Comparative simulation results demonstrate that the proposed NRSMC strategy achieves superior tracking accuracy and robustness compared with the Nussbaum-RBF Adaptive Backstepping Control (NRABC) method. Moreover, field experiments conducted further validate the effectiveness and adaptability of the proposed data-driven control approach. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Robotics, and Control)
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21 pages, 2036 KB  
Article
Visual Autonomous Docking for Unmanned Surface Vehicles Using Lightweight Supervised Learning Framework
by Junyan He and Wei Liu
Informatics 2026, 13(8), 132; https://doi.org/10.3390/informatics13080132 - 14 Aug 2026
Viewed by 411
Abstract
Autonomous docking is a core capability enabling full autonomy of unmanned surface vehicles (USVs), whose practical deployment demands visual pose estimation with high efficiency, temporal stability, and closed-loop control compatibility. This paper proposes a lightweight monocular visual docking perception framework based on MobileNetV2 [...] Read more.
Autonomous docking is a core capability enabling full autonomy of unmanned surface vehicles (USVs), whose practical deployment demands visual pose estimation with high efficiency, temporal stability, and closed-loop control compatibility. This paper proposes a lightweight monocular visual docking perception framework based on MobileNetV2 and a temporal convolutional network (TCN). In this framework, a MobileNetV2 backbone is adopted to perform end-to-end regression of the USV’s relative pose with respect to the dock from a single monocular image, while a feature-level TCN module fuses sequential visual features across consecutive frames to enhance the short-term stability of pose estimation. To validate the performance and reliability of the proposed method, a high-fidelity simulation environment is established to conduct closed-loop USV docking tests. Comparative results demonstrate that the MobileNetV2 backbone reduces inference latency compared with the VGG19 architecture, and the embedded TCN module effectively suppresses inter-frame pose fluctuations and abnormal estimation jumps. The proposed method provides an efficient and temporally consistent visual perception solution for simulation-validated USV autonomous docking systems. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence, Robotics, and Control)
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