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A Study on Improving the Performance of Dynamic Model Based Redundant Navigation System for Sensor Fault Scenarios

Journal of Institute of Control, robotics and Systems 2025年第4期31卷 351-359页

作者： Ko, Bo Sung Choi, Hyeong Jun Jeon, Myung Jun Cho, Hyeon Jin Song, Jin Woo Department of Artificial Intelligence and Robotics and Convergence Engineering for Intelligent Drone AIRI Sejong University Korea Republic of Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent Drone AIRI Sejong University Korea Republic of Agency for Defense Development Maritime Technology Research Institute Korea Republic of

This study proposes a redundant navigation system for UUV (Unmanned Underwater Vehicle) utilizing a 6 DOF (Degree of Freedom) VDM (Vehicle Dynamic Model). The system is designed to enable safe UUV asset return when all navigation sensors become inoperable. Since it is impossible to perfectly determine VDM coefficients through water tank experiments, a method to improve VDM output accuracy in real time during UUV mission is necessary. Here in this study, we introduce real-time adaptive 6 DOF VDM output error compensation algorithm to enhance the performance of redundant navigation systems. This research aims to establish the foundation for backup navigation solutions in situations when critical UUV sensor failures occur. © ICROS 2025.

关键词： Water tanks

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Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第10期5卷 4893-4904页

作者： Liu, Yexin Zhang, Weiming Zhao, Guoyang Zhu, Jinjing Vasilakos, Athanasios V. Wang, Lin Robotics and Autonomous Systems Thrust Guangzhou511458 China Grimstad4879 Norway Artificial Intelligence Thrust Guangzhou511458 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong999077 Hong Kong

The ability to scene understanding in adverse visual conditions, e.g., nighttime, has sparked active research for color-thermal semantic segmentation. However, it is essentially hampered by two critical problems: 1) the day-night gap of color images is larger than that of thermal images;and 2) the classwise performance of color images at night is not consistently higher or lower than that of thermal images. We propose the first test-time adaptation (TTA) framework, dubbed Night-TTA, to address the problems for nighttime color-thermal semantic segmentation without access to the source (daytime) data during adaptation. Our method enjoys three key technical parts. First, as one modality (e.g., color) suffers from a larger domain gap than that of the other (e.g., thermal), imaging heterogeneity refinement (IHR) employs an interaction branch on the basis of color and thermal branches to prevent cross-modal discrepancy and performance degradation. Then, class aware refinement (CAR) is introduced to obtain reliable ensemble logits based on pixel-level distribution aggregation of the three branches. In addition, we also design a specific learning scheme for our TTA framework, which enables the ensemble logits and three student logits to collaboratively learn to improve the quality of predictions during the testing phase of our Night-TTA. Extensive experiments show that our method achieves state-of-the-art (SoTA) performance with a 13.07% boost in mean intersection over union (mIoU). © 2020 IEEE.

关键词： Color

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Deep Transfer Learning Based Detection and Classification of Citrus Plant Diseases

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Computers, Materials & Continua 2023年第7期76卷 895-914页

作者： Shah Faisal Kashif Javed Sara Ali Areej Alasiry Mehrez Marzougui Muhammad Attique Khan Jae-Hyuk Cha Department of Robotics and Artificial Intelligence SMME NUSTIslamabadPakistan College of Computer Science King Khalid UniversityAbha61413Saudi Arabia Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science Hanyang UniversitySeoul04763Korea

Citrus fruit crops are among the world’s most important agricultural products,but pests and diseases impact their cultivation,resulting in yield and quality *** vision and machine learning have been widely used to detect and classify plant diseases over the last decade,allowing for early disease detection and improving agricultural *** paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning(DL)model,which improved accuracy while decreasing computational *** most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification *** transfer learning,this study successfully proposed a Convolutional Neural Network(CNN)-based pre-trained model(EfficientNetB3,ResNet50,MobiNetV2,and InceptionV3)for the identification and categorization of citrus plant *** evaluate the architecture’s performance,this study discovered that transferring an EfficientNetb3 model resulted in the highest training,validating,and testing accuracies,which were 99.43%,99.48%,and 99.58%,*** identifying and categorizing citrus plant diseases,the proposed CNN model outperforms other cuttingedge CNN model architectures developed previously in the literature.

关键词： Citrus diseases classification deep learning transfer learning efficientNetB3 mobileNetV2 ResNet50 InceptionV3

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Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems 49

Efficient Real-time Smoke Filtration with 3D LiDAR for Searc...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Kyuroson, Alexander Koval, Anton Nikolakopoulos, George Luleå University of Technology Robotics and Artificial Intelligence Group Department of Computer Electrical and Space Engineering LuleåE-97187 Sweden

ISBN: (纸本)9798350331820

Search and Rescue (SAR) missions in harsh and unstructured Sub-Terranean (Sub-T) environments in the presence of aerosol particles have recently become the main focus in the field of robotics. Aerosol particles such as smoke and dust directly affect the performance of any mobile robotic platform due to their reliance on their onboard perception systems for autonomous navigation and localization in Global Navigation Satellite System (GNSS)-denied environments. Although obstacle avoidance and object detection algorithms are robust to the presence of noise to some degree, their performance directly relies on the quality of captured data by onboard sensors such as Light Detection And Ranging (LiDAR) and camera. Thus, this paper proposes a novel modular agnostic filtration pipeline based on intensity and spatial information such as local point density for removal of detected smoke particles from Point Cloud (PCL) prior to its utilization for collision detection. Furthermore, the efficacy of the proposed framework in the presence of smoke during multiple frontier exploration missions is investigated while the experimental results are presented to facilitate comparison with other methodologies and their computational impact. This provides valuable insight to the research community for better utilization of filtration schemes based on available computation resources while considering the safe autonomous navigation of mobile robots. © 2023 IEEE.

关键词： Smoke

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Wi-Fi Feedback-Based Power System in the Heterogenous IoT Era – An Overview

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data Engineering and Communications Technologies 2022年 129卷 432-442页

作者： Al-Turjman, Fadi Abdelazim, Ahmed Ali Artificial Intelligence Engineering Department Research Center for AI and IoT AI and Robotics Institute Near East University Mersin 10 Nicosia Turkey

Electric power has recently been optimized and witnessed a significant transformation in many aspects to providing sustainable clean, distributed energy for the fast-paced, growing world. Modern technologies such as the internet of things (IoT) technology is at the forefront of this digital revolution. Besides, heterogeneous IoT has enabled and offered a variety of applications in power systems since it has enhanced power efficiency, distribution of energy, especially in rural areas. Furthermore, it has accelerated transmitting and receiving the signals from the main electric power grid to the emergency backup system via a smart WI-FI feedback signal to sustain critical loads such as hospitals, schools, plants, banks, etc., working without interruptible. Also, IoT contributes to increasing the volume of the energy shared. This review paper discusses the impact of IoT technology in evolving electric power and energy systems at an unprecedented pace. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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Data augmentation and denoising of computed tomography scan images in training deep learning models for rapid COVID-19 detection

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International Journal of Business intelligence and Data Mining 2024年第2期24卷 203-216页

作者： Mubarak, Auwalu Saleh Serte, Sertan Al-Turjman, Fadi Ameen, Zubaida Sa'Id Artificial Intelligence Engineering Department AI and Robotics Institute Near East University Mersin10 Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin10 Turkey Department of Electrical and Electronics Engineering Near East University Mersin10 Turkey

The deadly respiratory disease corona virus-2 (COVID-19) which was declared a pandemic by the World Health Organization (WHO) has resulted in over a million deaths around the world within less than a year. With the rapid spread of the virus, the currently adopted COVID-19 test by the WHO is the reverse transcription polymerase chain reaction (RT-PCR) test, which is expensive, time-consuming and not accessed by underdeveloped countries. Computed tomography (CT) scan images that were used in profiling suspected COVID-19 patients can serve as an alternative to the RT PCR test method. In this study, two different pre-Trained deep learning models ResNet-50 and ResNet-101 were trained to classify positive COVID-19 scan images. The best model which was trained on the augmented CT scan images achieved an accuracy of 98.3%, a sensitivity of 0.984, specificity of 0.983. © 2024 Inderscience Enterprises Ltd.. All rights reserved.

关键词： COVID-19

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Diabetic Foot Ulcer Unsupervised Segmentation with Vision Transformers Attention 4th

Diabetic Foot Ulcer Unsupervised Segmentation with Vision T...

引用

4th Diabetic Foot Ulcers Grand Challenge, DFUC 2024 held in Conjunction 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Brodzicki, Andrzej Kucharski, Dariusz Moniak, Bartlomiej Kostuch, Aleksander Noworolnik, Filip Anna, W. Jaworek-Korjakowska, Joanna Department of Automatic Control and Robotics AGH University of Kraków Kraków Poland Center of Excellence in Artificial Intelligence AGH University of Kraków Kraków Poland

ISBN: (纸本)9783031808708

The Diabetic Foot Ulcer Challenge is a global computer vision competition organised to raise awareness of the complications caused by diabetes and motivate researchers to develop solutions that might help in the treatment. In 2024, the challenge focused on self-supervised learning methods and synthetic data, as the process of collecting and labelling medical data is a tedious one. In this research, we present a novel, two-stage method for self-supervised DFU segmentation. It is based on a Vision Transformer attention from classification pre-task, followed by U-Net weakly supervised segmentation refinement. Our solution took 3rd place on the challenge leaderboard, with a 0.4796 Dice Score on the test set. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distribution transformers

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Data-Driven Tracking Control of a Cushion Robot with Safe Autonomous Motion Considering Human-Machine Interaction Environment

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Journal of Dynamic Systems, Measurement and Control 2025年第4期147卷 041003页

作者： Sun, Ping Shan, Rui Chang, Hongbin Wang, Shuoyu School of Artificial Intelligence Shenyang University of Technology Shenyang110870 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Department of Intelligent Mechanical Systems Engineering Kochi University of Technology Kochi7828502 Japan

This study proposes a data-driven safety controller with velocity constraints for a cushion robot. We constructed a mathematical description of the human-machine interaction environment by decomposing the generalized input force and coefficient matrix in the dynamic model. We established a new equivalent data model, which considered various human-machine interaction environments using a pseudo-Jacobian matrix. A stochastic configuration network (SCN) estimation method for variations in the human-machine interaction environment was proposed, with hidden layer nodes added at random. We designed a safe autonomous navigation path and proposed a data-driven control method that limited the actual velocity while stabilizing the tracking error system. In addition, the desired motion velocity of the robot was designed. This approach has the advantage of ensuring safety at a specified velocity. We also demonstrated and validated the effectiveness of the proposed data-driven algorithm using simulation-based comparative analysis and an experimental study. Copyright © 2024 by ASME.

关键词： Stochastic systems

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PID-fuzzy switching-based strategy to heading control for remote operated vehicle

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Neural Computing and Applications 2024年 1-17页

作者： Xie, Baolong He, Shuping Cao, Xiang Wang, Honghai Stojanovic, Vladimir Shi, Kaibo School of Electrical Engineering and Automation Anhui University Hefei230039 China School of Artificial Intelligence Anhui University Hefei230039 China School of Computing and Artificial Intelligence Chaohu University Chaohu238024 China Department of Automatic Control Robotics and Fluid Technique Faculty of Mechanical and Civil Engineering University of Kragujevac Kraljevo36000 Serbia School of Electronic Information and Electrical Engineering Chengdu University Chengdu610106 China

Accurate heading control is the premise for underwater vehicles to complete underwater operations. In order to improve the performance of heading control, this paper proposes a heading control strategy based on PID-fuzzy switching. First, the force analysis of the steering motion was carried out to derive the dynamics model of the steering motion. Then, the whole heading control process is divided into two phases, steering phase and holding phase. Fuzzy control is used in the steering phase, and PID control is used in the holding phase. By setting the automatic switching switch, the control system can switch freely between the two to meet the needs of system adjustment under different errors. Finally, the performance of the established heading control system is verified through motion control simulations and pool experiments, and the proposed control strategy is compared with other methods. The results show that the control strategy can effectively achieve ROV heading control with good steady-state accuracy, and the overall performance has been greatly improved. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Fuzzy control

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Design and Development of a Device to Measure Tissue Strength in Experimental Animals 8

Design and Development of a Device to Measure Tissue Strengt...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and robotics, DISCOVER 2024

作者： Veeresha, R.K. Shastry, C.S. Moolya Shodhan, S. Karegoudra, Shilpa Kumar, Nithin Department of Robotics and Artificial Intelligence Udupi574110 India Paneer Deralakatte Mangalore575018 India Department of Mechanical Engineering Udupi574110 India Department of Computer Science and Engineering Udupi574110 India

ISBN: (纸本)9798350350593

Developing a wound strength measuring device for experimental animals involves a multidisciplinary approach that combines engineering, biology, and veterinary medicine. Such a device would help researchers assess the healing progress and strength of wounds in animals, enabling a better understanding of tissue regeneration and wound healing mechanisms. In this paper, We designed a mice holder along with a full experimental setup to apply load and measure the displacement automatically. Also, we carried out an experiment to measure load versus displacement. © 2024 IEEE.

关键词： Mammals

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