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Technologies
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AI Robotics Technologies and Their Applications
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AI Robotics Technologies and Their Applications

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: closed (30 October 2025) | Viewed by 6519

Special Issue Editors

Prof. Dr. Youngwoo Kim

E-Mail Website
Guest Editor
Department of Electrical Engineering, Korea National University of Transportation, Chungju, Republic of Korea
Interests: human-robot interface; sensors; hybrid systems; medical systems
Dr. Changwon Kim

E-Mail Website
Guest Editor
School of Mechanical Engineering, Pukyong National University, Busan 48513, Republic of Korea
Interests: autonomous mobile robot navigation; autonomous system-based services; path planning; smart mechanism; intelligent control; intention-based control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Our Special Issue explores the diverse applications of AI in robotics. We cover topics ranging from the development of human–robot interfaces and AI in spaces where humans and robots coexist to AI applications in medical robotics and systems, as well as digital twin technology in robotics. We welcome research that examines how advancements in AI are driving innovation in robotics, along with real-world case studies. We encourage researchers who seek to expand the boundaries of AI and robotics to submit their work and contribute to this evolving field.

Prof. Dr. Youngwoo Kim
Dr. Changwon Kim
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. Technologies 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 1600 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
  • human-robot interfaces
  • medical and healthcare system
  • digital twin in robotics
  • biomimetic system

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Published Papers (6 papers)

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Research

23 pages, 13104 KB  
Article
A Hierarchical Distributed Control System Design for Lower Limb Rehabilitation Robot
by Aihui Wang, Jinkang Dong, Rui Teng, Ping Liu, Xuebin Yue and Xiang Zhang
Technologies 2025, 13(10), 462; https://doi.org/10.3390/technologies13100462 - 13 Oct 2025
Viewed by 450
Abstract
With the acceleration of global aging and the rising incidence of stroke, the demand for lower limb rehabilitation has been steadily increasing. Traditional therapeutic methods can no longer meet the growing needs, which has led to the widespread application of robotic solutions to [...] Read more.
With the acceleration of global aging and the rising incidence of stroke, the demand for lower limb rehabilitation has been steadily increasing. Traditional therapeutic methods can no longer meet the growing needs, which has led to the widespread application of robotic solutions to address labor shortages. In this context, this paper presents a hierarchical and distributed control system based on ROS 2 and Micro-ROS. The distributed architecture decouples functional modules, improving system maintainability and supporting modular upgrades. The control system consists of a three-layer structure, including a high-level controller, Jetson Nano, for gait data processing and advanced command generation; a middle-layer controller, ESP32-S3, for sensor data fusion and inter-layer communication bridging; and a low-level controller, STM32F405, for field-oriented control to drive the motors along a predefined trajectory. Experimental validation in both early and late rehabilitation stages demonstrates the system’s ability to achieve accurate trajectory tracking. In the early rehabilitation stage, the maximum root mean square error of the joint motors is 1.143°; in the later rehabilitation stage, the maximum root mean square error of the joint motors is 1.833°, confirming the robustness of the control system. Additionally, the hierarchical and distributed architecture ensures maintainability and facilitates future upgrades. This paper provides a feasible control scheme for the next generation of lower limb rehabilitation robots. Full article
(This article belongs to the Special Issue AI Robotics Technologies and Their Applications)
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30 pages, 6751 KB  
Article
Web System for Solving the Inverse Kinematics of 6DoF Robotic Arm Using Deep Learning Models: CNN and LSTM
by Mayra A. Torres-Hernández, Teodoro Ibarra-Pérez, Eduardo García-Sánchez, Héctor A. Guerrero-Osuna, Luis O. Solís-Sánchez and Ma. del Rosario Martínez-Blanco
Technologies 2025, 13(9), 405; https://doi.org/10.3390/technologies13090405 - 5 Sep 2025
Cited by 1 | Viewed by 1032
Abstract
This work presents the development of a web system using deep learning (DL) neural networks to solve the inverse kinematics problem of the Quetzal robotic arm, designed for academic and research purposes. Two architectures, LSTM and CNN, were designed, trained, and evaluated using [...] Read more.
This work presents the development of a web system using deep learning (DL) neural networks to solve the inverse kinematics problem of the Quetzal robotic arm, designed for academic and research purposes. Two architectures, LSTM and CNN, were designed, trained, and evaluated using data generated through the Denavit–Hartenberg (D-H) model, considering the robot’s workspace. The evaluation employed the mean squared error (MSE) as the loss metric and mean absolute error (MAE) and accuracy as performance metrics. The CNN model, featuring four convolutional layers and an input of 4 timesteps, achieved the best overall performance (95.9% accuracy, MSE of 0.003, and MAE of 0.040), significantly outperforming the LSTM model in training time. A hybrid web application was implemented, allowing offline training and real-time online inference under one second via an interactive interface developed with Streamlit 1.16. The solution integrates tools such as TensorFlow™ 2.15, Python 3.10, and Anaconda Distribution 2023.03-1, ensuring portability to fog or cloud computing environments. The proposed system stands out for its fast response times (1 s), low computational cost, and high scalability to collaborative robotics environments. It is a viable alternative for applications in educational or research settings, particularly in projects focused on industrial automation. Full article
(This article belongs to the Special Issue AI Robotics Technologies and Their Applications)
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17 pages, 5431 KB  
Article
Localization Meets Uncertainty: Uncertainty-Aware Multi-Modal Localization
by Hye-Min Won, Jieun Lee and Jiyong Oh
Technologies 2025, 13(9), 386; https://doi.org/10.3390/technologies13090386 - 1 Sep 2025
Viewed by 843
Abstract
Reliable localization is critical for robot navigation in complex indoor environments. In this paper, we propose an uncertainty-aware localization method that enhances the reliability of localization outputs without modifying the prediction model itself. This study introduces a percentile-based rejection strategy that filters out [...] Read more.
Reliable localization is critical for robot navigation in complex indoor environments. In this paper, we propose an uncertainty-aware localization method that enhances the reliability of localization outputs without modifying the prediction model itself. This study introduces a percentile-based rejection strategy that filters out unreliable 3-degree-of-freedom pose predictions based on aleatoric and epistemic uncertainties the network estimates. We apply this approach to a multi-modal end-to-end localization that fuses RGB images and 2D LiDAR data, and we evaluate it across three real-world datasets collected using a commercialized serving robot. Experimental results show that applying stricter uncertainty thresholds consistently improves pose accuracy. Specifically, the mean position error, calculated as the average Euclidean distance between the predicted and ground-truth (x, y) coordinates, is reduced by 41.0%, 56.7%, and 69.4%, and the mean orientation error, representing the average angular deviation between the predicted and ground-truth yaw angles, is reduced by 55.6%, 65.7%, and 73.3%, when percentile thresholds of 90%, 80%, and 70% are applied, respectively. Furthermore, the rejection strategy effectively removes extreme outliers, resulting in better alignment with ground truth trajectories. To the best of our knowledge, this is the first study to quantitatively demonstrate the benefits of percentile-based uncertainty rejection in multi-modal and end-to-end localization tasks. Our approach provides a practical means to enhance the reliability and accuracy of localization systems in real-world deployments. Full article
(This article belongs to the Special Issue AI Robotics Technologies and Their Applications)
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18 pages, 4865 KB  
Article
A Multi-Scale Cross-Layer Fusion Method for Robotic Grasping Detection
by Chengxuan Huang, Jing Xu, Xinyu Cai and Shiying Shen
Technologies 2025, 13(8), 357; https://doi.org/10.3390/technologies13080357 - 13 Aug 2025
Viewed by 756
Abstract
Measurement of grasp configurations (position, orientation, and width) in unstructured environments is critical for robotic systems. Accurate and robust prediction relies on rich multi-scale object representations; however, detail loss and fusion conflicts in multi-scale processing often cause measurement errors, particularly for complex objects. [...] Read more.
Measurement of grasp configurations (position, orientation, and width) in unstructured environments is critical for robotic systems. Accurate and robust prediction relies on rich multi-scale object representations; however, detail loss and fusion conflicts in multi-scale processing often cause measurement errors, particularly for complex objects. This study proposes a multi-scale and cross-layer fusion grasp detection network (MCFG-Net) based on a skip-connected encoder–decoder architecture. The sampling module in the encoder–decoder is optimized, and the multi-scale fusion strategy is improved, enabling pixel-level grasp rectangles to be generated in real time. A multi-scale spatial feature enhancement module (MSFEM) addresses spatial detail loss in traditional feature pyramids and preserves spatial consistency by capturing contextual information within the same scale. In addition, a cascaded fusion attention module (CFAM) is designed to assist skip connections and mitigate redundant information and semantic mismatch during feature fusion. Experimental results show that MCFG-Net achieves grasp detection accuracies of 99.62% ± 0.11% on the Cornell dataset and 94.46% ± 0.22% on the Jacquard dataset. Real-world tests on an AUBO i5 robot yield success rates of 98.5% for single-target and 95% for multi-target grasping tasks, demonstrating practical applicability in unstructured environments. Full article
(This article belongs to the Special Issue AI Robotics Technologies and Their Applications)
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24 pages, 2229 KB  
Article
Mathematical Modeling of Optimal Drone Flight Trajectories for Enhanced Object Detection in Video Streams Using Kolmogorov–Arnold Networks
by Aida Issembayeva, Oleksandr Kuznetsov, Anargul Shaushenova, Ardak Nurpeisova, Gabit Shuitenov and Maral Ongarbayeva
Technologies 2025, 13(6), 235; https://doi.org/10.3390/technologies13060235 - 6 Jun 2025
Viewed by 1556
Abstract
This study addresses the critical challenge of optimizing drone flight parameters for enhanced object detection in video streams. While most research focuses on improving detection algorithms, the relationship between flight parameters and detection performance remains poorly understood. We present a novel approach using [...] Read more.
This study addresses the critical challenge of optimizing drone flight parameters for enhanced object detection in video streams. While most research focuses on improving detection algorithms, the relationship between flight parameters and detection performance remains poorly understood. We present a novel approach using Kolmogorov–Arnold Networks (KANs) to model complex, non-linear relationships between altitude, pitch angle, speed, and object detection performance. Our main contributions include the following: (1) the systematic analysis of flight parameters’ effects on detection performance using the AU-AIR dataset, (2) development of a KAN-based mathematical model achieving R2 = 0.99, (3) identification of optimal flight parameters through multi-start optimization, and (4) creation of a flexible implementation framework adaptable to different UAV platforms. Sensitivity analysis confirms the solution’s robustness with only 7.3% performance degradation under ±10% parameter variations. This research bridges flight operations and detection algorithms, offering practical guidelines that enhance the detection capability by optimizing image acquisition rather than modifying detection algorithms. Full article
(This article belongs to the Special Issue AI Robotics Technologies and Their Applications)
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19 pages, 7039 KB  
Article
A New Contact Force Estimation Method for Heavy Robots Without Force Sensors by Combining CNN-GRU and Force Transformation
by Peizhang Wu, Hui Dong, Pengfei Li, Yifei Bao, Wei Dong and Lining Sun
Technologies 2025, 13(5), 192; https://doi.org/10.3390/technologies13050192 - 9 May 2025
Viewed by 1345
Abstract
In response to the safety control requirements of heavy robot operations, to address the problems of cumbersome, time-consuming, poor accuracy and low real-time performance in robot end contact force estimation without force sensors by using traditional manual modeling and identification methods, this paper [...] Read more.
In response to the safety control requirements of heavy robot operations, to address the problems of cumbersome, time-consuming, poor accuracy and low real-time performance in robot end contact force estimation without force sensors by using traditional manual modeling and identification methods, this paper proposes a new contact force estimation method for heavy robots without force sensors by combining CNN-GRU and force transformation. Firstly, the CNN-GRU machine learning method is utilized to construct the robot Joint Motor Current-Joint External Force Model; then, the Joint External Force-End Contact Force Model is constructed through the Kalman filter and Jacobian force transformation method, and the robot end contact force is estimated by finally uniting them. This method can achieve robot end contact force estimation without a force sensor, avoiding the cumbersome manual modeling and identification process. Compared with traditional manual modeling and identification methods, experiments show that the proposed method in this paper can approximately double the estimation accuracy of the contact force of heavy robots and reduce the time consumption by approximately half, with advantages such as convenience, efficiency, strong real-time performance, and high accuracy. Full article
(This article belongs to the Special Issue AI Robotics Technologies and Their Applications)
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