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Automation
Special Issues
AI-Enhanced Measurement and Control for Robotic Systems
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AI-Enhanced Measurement and Control for Robotic Systems

A Special Issue of Automation (ISSN 2673-4052) belonging to the section "Robotics and Autonomous Systems".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 3448

Editors

Dr. Ameer Tamoor Khan

E-Mail Website
Guest Editor
Department of Plant and Environmental Sciences, University of Copenhagen, DK-1350 Copenhagen, Denmark
Interests: multi-robots; machine learning; dynamic systems; artificial neural networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shuai (Steven) Li

E-Mail Website
Guest Editor
Faculty of Information Technology and Electrical Engineering, University of Oulu, 90570 Oulu, Finland
Interests: robotics and automation; intelligent control and optimization; signal processing; embedded systems; robotic manipulation; autonomous manufacturing; multi-robot coordination
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) into robotic systems is transforming traditional approaches to measurement, control, and decision-making. This Special Issue aims to highlight recent advances in AI-enhanced methodologies that improve the precision, adaptability, and autonomy of robotic platforms across diverse domains, including industry, healthcare, agriculture, and services.

With the rise of machine learning, computer vision, and data-driven optimization, intelligent control strategies are now more capable than ever of addressing uncertainty, navigating dynamic environments, and performing complex interactive tasks.

We welcome original research articles and comprehensive reviews focusing on the synergy between AI algorithms and robotic control architectures. This Special Issue seeks to bridge the gap between theoretical developments and practical implementations, showcasing innovations that push the boundaries of perception, feedback control, sensor fusion, and adaptive behavior.

Topics include, but are not limited to:

  • AI-based sensor calibration and fusion;
  • Learning-based motion and force control;
  • Intelligent feedback and adaptive control systems;
  • Deep learning for robotic perception and state estimation;
  • Bio-inspired and evolutionary control techniques;
  • Real-time decision-making and trajectory optimization;
  • AI-driven predictive maintenance and fault detection;
  • Applications in collaborative, mobile, aerial, and agricultural robotics.

Dr. Ameer Tamoor Khan
Prof. Dr. Shuai (Steven) 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. Automation 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 1200 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

  • AI-based sensor calibration and fusion
  • learning-based motion and force control
  • intelligent feedback and adaptive control systems
  • deep learning for robotic perception and state estimation
  • bio-inspired and evolutionary control techniques
  • real-time decision-making and trajectory optimization
  • AI-driven predictive maintenance and fault detection
  • applications in collaborative, mobile, aerial, and agricultural robotics

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

Published Papers (3 papers)

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Research

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25 pages, 26579 KB  
Article
Reliability-Aware Occupancy Map Merging in Dynamic Environments Using Temporal and Probabilistic Maps
by Hanngyoo Kim and Seunghwan Lee
Automation 2026, 7(4), 119; https://doi.org/10.3390/automation7040119 - 1 Aug 2026
Viewed by 633
Abstract
Occupancy map merging in dynamic environments is challenging because moving objects introduce time-varying disturbances that degrade registration accuracy and structural consistency. This paper proposes a reliability-aware map-merging method that explicitly models temporal validity and probabilistic reliability in occupancy grid maps. The method constructs [...] Read more.
Occupancy map merging in dynamic environments is challenging because moving objects introduce time-varying disturbances that degrade registration accuracy and structural consistency. This paper proposes a reliability-aware map-merging method that explicitly models temporal validity and probabilistic reliability in occupancy grid maps. The method constructs two complementary maps: a trajectory-guided temporal reliability map that reflects the recency and persistence of cell observations and a probabilistic reliability map that refines detected candidate regions based on temporal persistence and spatial reliability criteria. By suppressing transient clutter before registration, the proposed approach focuses alignment on structurally stable regions and improves merging robustness. Experiments in simulation and real-world indoor environments demonstrate clear improvements over a conventional feature-based baseline, substantially reducing both translation and rotation errors. These results show that incorporating temporal validity and probabilistic reliability can improve occupancy map merging under dynamic conditions. Full article
(This article belongs to the Special Issue AI-Enhanced Measurement and Control for Robotic Systems)
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21 pages, 1972 KB  
Article
Feedforward Neural Network-Based MPC Optimized by Hybrid Fractional PSO–SQP for Trajectory Tracking of Autonomous Vehicles
by Fahad Alotaibi, Habib Dhahri, Saleh Almohaimeed and Awais Mahmood
Automation 2026, 7(3), 95; https://doi.org/10.3390/automation7030095 - 15 Jun 2026
Viewed by 849
Abstract
Background/Objective: Autonomous vehicles (AVs) require control algorithms capable of handling complex and dynamic environments while satisfying multiple conflicting objectives such as safety, comfort, energy efficiency, and trajectory accuracy. Model predictive control (MPC) offers a principled framework for multi-constraint optimization, yet its real-time feasibility [...] Read more.
Background/Objective: Autonomous vehicles (AVs) require control algorithms capable of handling complex and dynamic environments while satisfying multiple conflicting objectives such as safety, comfort, energy efficiency, and trajectory accuracy. Model predictive control (MPC) offers a principled framework for multi-constraint optimization, yet its real-time feasibility remains challenging for nonlinear vehicle dynamics. Methods: This paper presents a feedforward neural network (FNN)-based MPC framework for autonomous vehicle trajectory tracking. The FNN approximates the coupled vehicle dynamics and visual preview error model using an algebraic sum of log-sigmoid functions. Three adaptive FNN parameter sets, namely, the scaling factor, convergence parameter, and time-shifting parameter, are jointly optimized using a hybrid algorithm that combines the global search capability of fractional particle swarm optimization (FPSO) with the local refinement of sequential quadratic programming (SQP). Results: Comprehensive scenario-based simulations are performed to evaluate trajectory tracking dynamics under dry conditions with an adhesion coefficient of 0.8 and a vehicle mass of 1723 kg moving at a speed of 80 km/h. The results are quantitatively compared with a traditional PID controller and a structurally comparable MPC framework from the literature under identical simulation conditions; related DRL- and RL-based methods are discussed qualitatively for contextual orientation only. The stability, reliability, and computational complexity of the proposed framework are examined based on the mean square error, fitness value, and computational budget in GFLOPs for 100 independent runs. Conclusions: The proposed FNN-based MPC framework demonstrates improved tracking accuracy and optimizer reliability in simulation. While the present results indicate promising computational behavior, real-time deployment will require further validation on embedded automotive hardware and under closed-loop real-time constraints. Full article
(This article belongs to the Special Issue AI-Enhanced Measurement and Control for Robotic Systems)
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Other

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31 pages, 1527 KB  
Systematic Review
A Taxonomy-Driven Analysis of Learning-Based Approaches in SLAM
by Rafael Rojas-Galván, Luis F. Olmedo-García, José R. García-Martínez, José Manuel Alvarez-Alvarado, Ricardo Rojas-Galván and Juvenal Rodríguez-Reséndiz
Automation 2026, 7(4), 101; https://doi.org/10.3390/automation7040101 - 1 Jul 2026
Viewed by 781
Abstract
Learning-based approaches have significantly advanced the capabilities of Simultaneous Localization and Mapping (SLAM) systems, particularly in challenging environments characterized by noise, dynamic objects, and perceptual ambiguity. However, the literature remains highly heterogeneous in terms of sensing modalities, datasets, evaluation protocols, and reporting practices, [...] Read more.
Learning-based approaches have significantly advanced the capabilities of Simultaneous Localization and Mapping (SLAM) systems, particularly in challenging environments characterized by noise, dynamic objects, and perceptual ambiguity. However, the literature remains highly heterogeneous in terms of sensing modalities, datasets, evaluation protocols, and reporting practices, making systematic comparison difficult. This paper presents a taxonomy-driven review of learning-based SLAM approaches, with particular emphasis on LiDAR-based systems in mobile robotics, and introduces a functional taxonomy that categorizes methods according to the role of learning within the SLAM architecture: (i) learning-enhanced front-end SLAM (T1), (ii) learning-enhanced back-end SLAM (T2), and (iii) learning-centric SLAM systems (T3). Representative studies were analyzed with respect to performance characteristics, robustness, computational requirements, datasets, and deployment-related evidence. The analysis shows that T1 approaches primarily improve local pose estimation and robustness, T2 methods enhance global consistency through learning-based loop closure and relocalization, and T3 approaches explore unified representations, semantic reasoning, and learning-centric autonomy, albeit with greater computational demands and limited deployment evidence. The review further indicates that hybrid approaches combining geometric and learning-based components constitute a prominent trend in the literature, frequently reporting improvements in accuracy and adaptability while maintaining compatibility with established SLAM frameworks. Nevertheless, these observations should be interpreted cautiously, as stronger empirical evidence for hybrid systems may partially reflect their greater technological maturity and broader evaluation history. Finally, the review identifies persistent challenges, including limited cross-domain generalization, high computational requirements, limited deployment-oriented evaluation, and the lack of standardized benchmarking and reporting practices. These findings highlight the need for more reproducible evaluation methodologies, uncertainty-aware learning strategies, and computationally efficient architectures for robust real-world autonomous SLAM. Full article
(This article belongs to the Special Issue AI-Enhanced Measurement and Control for Robotic Systems)
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