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Artificial Intelligence and Its Application in Robotics
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Artificial Intelligence and Its Application in Robotics

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

Deadline for manuscript submissions: 20 September 2024 | Viewed by 3390

Special Issue Editors

Dr. Seong Hyeon Hong

E-Mail Website
Guest Editor
Department of Mechanical and Civil Engineering, Florida Institute of Technology, Melbourne, FL 32901, USA
Interests: intelligent perception and control; path planning; prognostics and health management; machine learning (physics-informed learning)
Prof. Dr. Yi Wang

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA
Interests: multidisciplinary analysis and optimization; large-scale system-level simulation; adaptive model integration; machine learning

Special Issue Information

Dear Colleagues,

Intelligent robots are critical in various domains such as personal service, medical support, smart manufacturing, transportation, military applications, smart farming, unmanned exploration, and various industrial applications. To facilitate and advance the technological developments in intelligent systems, the prestigious journal of Applied Sciences invites you to propose novel research in the areas of artificial intelligence and robotics. This Special Issue seeks research dedicated to artificial intelligence applied across various fields of robotics, including but not limited to advanced perception (computer vision), intelligent control, reinforcement learning, meta-learning, human–robot interaction, human–machine interfaces, unmanned and autonomous systems, multi-robot systems, path planning, mapping, innovative sensor systems, field robotics, industrial robotics, medical robotics, and service robotics.

Dr. Seong Hyeon Hong
Prof. Dr. Yi Wang
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

  • machine learning
  • machine vision
  • robotic control systems
  • human–robot interaction
  • innovative sensors
  • unmanned vehicles
  • field/industrial robotics

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Published Papers (5 papers)

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Research

19 pages, 1829 KiB  
Article
Refined Prior Guided Category-Level 6D Pose Estimation and Its Application on Robotic Grasping
by Huimin Sun, Yilin Zhang, Honglin Sun and Kenji Hashimoto
Appl. Sci. 2024, 14(17), 8009; https://doi.org/10.3390/app14178009 - 7 Sep 2024
Viewed by 365
Abstract
Estimating the 6D pose and size of objects is crucial in the task of visual grasping for robotic arms. Most current algorithms still require the 3D CAD model of the target object to match with the detected points, and they are unable to [...] Read more.
Estimating the 6D pose and size of objects is crucial in the task of visual grasping for robotic arms. Most current algorithms still require the 3D CAD model of the target object to match with the detected points, and they are unable to predict the object’s size, which significantly limits the generalizability of these methods. In this paper, we introduce category priors and extract high-dimensional abstract features from both the observed point cloud and the prior to predict the deformation matrix of the reconstructed point cloud and the dense correspondence between the reconstructed and observed point clouds. Furthermore, we propose a staged geometric correction and dense correspondence refinement mechanism to enhance the accuracy of regression. In addition, a novel lightweight attention module is introduced to further integrate the extracted features and identify potential correlations between the observed point cloud and the category prior. Ultimately, the object’s translation, rotation, and size are obtained by mapping the reconstructed point cloud to a normalized canonical coordinate system. Through extensive experiments, we demonstrate that our algorithm outperforms existing methods in terms of performance and accuracy on commonly used benchmarks for this type of problem. Additionally, we implement the algorithm in robotic arm-grasping simulations, further validating its effectiveness. Full article
(This article belongs to the Special Issue Artificial Intelligence and Its Application in Robotics)
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21 pages, 4861 KiB  
Article
Surveillance Unmanned Ground Vehicle Path Planning with Path Smoothing and Vehicle Breakdown Recovery
by Tyler Parsons, Farhad Baghyari, Jaho Seo, Byeongjin Kim, Mingeuk Kim and Hanmin Lee
Appl. Sci. 2024, 14(16), 7266; https://doi.org/10.3390/app14167266 - 19 Aug 2024
Viewed by 539
Abstract
As unmanned ground vehicles (UGV) continue to be adapted to new applications, an emerging area lacks proper guidance for global route optimization methodology. This area is surveillance. In autonomous surveillance applications, a UGV is equipped with a sensor that receives data within a [...] Read more.
As unmanned ground vehicles (UGV) continue to be adapted to new applications, an emerging area lacks proper guidance for global route optimization methodology. This area is surveillance. In autonomous surveillance applications, a UGV is equipped with a sensor that receives data within a specific range from the vehicle while it traverses the environment. In this paper, the ant colony optimization (ACO) algorithm was adapted to the UGV surveillance problem to solve for optimal paths within sub-areas. To do so, the problem was modeled as the covering salesman problem (CSP). This is one of the first applications using ACO to solve the CSP. Then, a genetic algorithm (GA) was used to schedule a fleet of UGVs to scan several sub-areas such that the total distance is minimized. Initially, these paths are infeasible because of the sharp turning angles. Thus, they are improved using two methods of path refinement (namely, the corner-cutting and Reeds–Shepp methods) such that the kinematic constraints of the vehicles are met. Several test case scenarios were developed for Goheung, South Korea, to validate the proposed methodology. The promising results presented in this article highlight the effectiveness of the proposed methodology for UGV surveillance applications. Full article
(This article belongs to the Special Issue Artificial Intelligence and Its Application in Robotics)
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15 pages, 3294 KiB  
Article
Implementation of a Small-Sized Mobile Robot with Road Detection, Sign Recognition, and Obstacle Avoidance
by Ching-Chang Wong, Kun-Duo Weng, Bo-Yun Yu and Yung-Shan Chou
Appl. Sci. 2024, 14(15), 6836; https://doi.org/10.3390/app14156836 - 5 Aug 2024
Viewed by 729
Abstract
In this study, under the limited volume of 18 cm × 18 cm × 21 cm, a small-sized mobile robot is designed and implemented. It consists of a CPU, a GPU, a 2D LiDAR (Light Detection And Ranging), and two fisheye cameras to [...] Read more.
In this study, under the limited volume of 18 cm × 18 cm × 21 cm, a small-sized mobile robot is designed and implemented. It consists of a CPU, a GPU, a 2D LiDAR (Light Detection And Ranging), and two fisheye cameras to let the robot have good computing processing and graphics processing capabilities. In addition, three functions of road detection, sign recognition, and obstacle avoidance are implemented on this small-sized robot. For road detection, we divide the captured image into four areas and use Intel NUC to perform road detection calculations. The proposed method can significantly reduce the system load and also has a high processing speed of 25 frames per second (fps). For sign recognition, we use the YOLOv4-tiny model and a data augmentation strategy to significantly improve the computing performance of this model. From the experimental results, it can be seen that the mean Average Precision (mAP) of the used model has increased by 52.14%. For obstacle avoidance, a 2D LiDAR-based method with a distance-based filtering mechanism is proposed. The distance-based filtering mechanism is proposed to filter important data points and assign appropriate weights, which can effectively reduce the computational complexity and improve the robot’s response speed to avoid obstacles. Some results and actual experiments illustrate that the proposed methods for these three functions can be effectively completed in the implemented small-sized robot. Full article
(This article belongs to the Special Issue Artificial Intelligence and Its Application in Robotics)
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14 pages, 687 KiB  
Article
U-TFF: A U-Net-Based Anomaly Detection Framework for Robotic Manipulator Energy Consumption Auditing Using Fast Fourier Transform
by Ge Song, Seong Hyeon Hong, Tristan Kyzer and Yi Wang
Appl. Sci. 2024, 14(14), 6202; https://doi.org/10.3390/app14146202 - 17 Jul 2024
Viewed by 569
Abstract
Robotic manipulators play a key role in modern industrial manufacturing processes. Monitoring their operational health is of paramount importance. In this paper, a novel anomaly detection framework named U-TFF is introduced for energy consumption auditing of robotic manipulators. It comprises a cascade of [...] Read more.
Robotic manipulators play a key role in modern industrial manufacturing processes. Monitoring their operational health is of paramount importance. In this paper, a novel anomaly detection framework named U-TFF is introduced for energy consumption auditing of robotic manipulators. It comprises a cascade of Time–Frequency Fusion (TFF) blocks to extract both time and frequency domain features from time series data. The block applies the Fast Fourier Transform to convert the input to the frequency domain, followed by two separate dense layers to process the resulting real and imaginary components, respectively. The frequency and time features are then combined to reconstruct the input. A U-shaped architecture is implemented to link corresponding TFF blocks of the encoder and decoder at the same level through skip connections. The semi-supervised model is trained using data exclusively from normal operations. Significant errors were generated during testing for anomalies with data distributions deviating from the training samples. Consequently, a threshold based on the magnitude of reconstruction errors was implemented to identify anomalies. Experimental validation was conducted using a custom dataset, including physical attacks as abnormal cases. The proposed framework achieved an accuracy and recall of approximately 0.93 and 0.83, respectively. A comparison with other benchmark models further verified its superior performance. Full article
(This article belongs to the Special Issue Artificial Intelligence and Its Application in Robotics)
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27 pages, 3642 KiB  
Article
Nonlinear Trajectory Tracking Controller for Underwater Vehicles with Shifted Center of Mass Model
by Przemyslaw Herman
Appl. Sci. 2024, 14(13), 5376; https://doi.org/10.3390/app14135376 - 21 Jun 2024
Viewed by 461
Abstract
This paper addresses the issue of trajectory tracking control for an autonomous underwater vehicle in the presence of parameter perturbations and disturbances in three-dimensional space. The control scheme is based on a combination of the backstepping method, the adaptive integral sliding mode control [...] Read more.
This paper addresses the issue of trajectory tracking control for an autonomous underwater vehicle in the presence of parameter perturbations and disturbances in three-dimensional space. The control scheme is based on a combination of the backstepping method, the adaptive integral sliding mode control scheme, and velocity transformation resulting from the decomposition of the inertia matrix, which is symmetric. In addition, adaptive laws were applied to eliminate the effects of parameter perturbations and external disturbances. The main feature of the proposed approach is that the vehicle model is not fully symmetric but contains quantities due to the shift of the center of mass. Another important feature of the control scheme is the ability to detect some of the consequences caused by reducing the vehicle model by neglecting dynamic couplings. Numerical results on the five degrees of freedom (DOF) vehicle model show the efficiency, effectiveness, and robustness of the developed controller. Full article
(This article belongs to the Special Issue Artificial Intelligence and Its Application in Robotics)
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