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Radar Dead-reckoning Based EKF-SLAM Using Virtual Line Segment

Journal of Institute of Control, robotics and Systems 2024年第9期30卷 996-1003页

作者： Lee, Min-Ho Seo, Kyeong-Wook Hwang, Dong-Yun Song, Jin-Woo Do, Hoang-Viet Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent Drone Sejong University Korea Republic of Department of Artificial Intelligence and Robotics and Convergence Engineering for Intelligent Drone Sejong University Korea Republic of

In this paper, we propose a dead reckoning–based radar simultaneous localization and mapping (SLAM) system utilizing constrained virtual line segments. In particular, we utilize Doppler velocity from radar for odometry while constructing a map using 3D radar point-cloud measurements. Unlike the conventional approach where accelerometers are employed, our system integrates radar velocity for pose calculation, thereby preventing double-integration and accelerometer bias and potentially improving the localization accuracy. Additionally, we analyze radar 3D point cloud characteristics and employ a recursive random sample consensus algorithm to eliminate outliers. The resulting set of inlier point clouds is then used to predict virtual line segments. Using virtual lines instead of feature points mitigates the sparse and noisy nature of radar point clouds. Moreover, considering the typical characteristics of indoor environments where walls intersect perpendicularly, we impose constraints on estimates to enhance performance further. We evaluate the performance of the proposed algorithm through experiments conducted in real indoor environments using an unmanned ground vehicle platform. Our results demonstrate significant improvements over traditional odometer-based systems, with reductions of approximately 41% in 2D trajectory error and 49.5% in heading angle error. © ICROS 2024.

关键词： Accelerometers

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Reinforcement Learning for Highway Driving Decision-making Using CDR (Curriculum based Domain Randomization)

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Journal of Institute of Control, robotics and Systems 2025年第2期31卷 114-123页

作者： Yong, Jae-Hyoung Kim, Min-Sung Park, Tae-Hyoung Department of Intelligent System and Robotics Chungbuk National University Korea Republic of Department of Control Robot Engineering Chungbuk National University Korea Republic of Department of Intelligent System and Robotics Chungbuk National University Chungbuk National University Hospital Korea Republic of

Incorrect autonomous driving decisions on highways can lead to traffic congestion and accidents. Therefore, accurate decision-making in highways is essential. However, decision-making in highways is a challenging task due to the complexity of traffic situations. Thus, reinforcement learning is an appropriate approach for decision-making in highways. However, a domain gap often arises between the training and testing environments due to differences in them. Therefore, a curriculum-based domain randomization approach is applied, which progressively adjusts the level of random parameters. The RL model is trained using the SUMO simulator and tested in the MORAI simulator, where white noise is added to simulate real-world conditions. Consequently, applying curriculum domain randomization has the lowest collision rate. © ICROS 2025.

关键词： Traffic congestion

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Camera-LiDAR Fusion for Detecting Traffic Law Violations in Surveillance Systems

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Journal of Institute of Control, robotics and Systems 2025年第3期31卷 187-195页

作者： Kim, Taek-Lim Jang, Byoung-Jin Yeon, Joo-Yeon Park, Tae Hyoung Department of Control and Robot Engineering Chungbuk National University Korea Republic of Department of Intelligent System and Robotics Chungbuk National University Korea Republic of

Traffic surveillance systems are essential for ensuring public safety and optimizing urban traffic flow by accurately detecting, classifying, and monitoring traffic law violations such as illegal parking and jaywalking. However, detecting such violations in dynamic urban environments poses significant challenges due to unpredictable conditions. Traditional camera-based systems excel in capturing visual details but suffer performance degradation under low light, occlusion, or complex intersection scenarios, leading to false positives and missed detections. Therefore, we propose a camera-LiDAR fusion-based traffic violation detection system. LiDAR's independence from lighting conditions complements camera capabilities, especially in night scenarios or occluded environments. Experimental results in a real-world urban environment demonstrate the system’s effectiveness in reducing false positives and missed detections compared to traditional camera-only approaches. This study underscores the potential of sensor fusion to improve traffic management systems and points out future research opportunities in challenging conditions. © ICROS 2025.

关键词： Urban transportation

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

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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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Position-aware pushing and grasping synergy with deep reinforcement learning in clutter

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 738-755页

作者： Min Zhao Guoyu Zuo Shuangyue Yu Daoxiong Gong Zihao Wang Ouattara Sie Intelligent Robotics Laboratory Faculty of Information TechnologyBeijing University of TechnologyBeijingChina Beijing Key Laboratory of Computational Intelligence and Intelligent Systems BeijingChina Laboratory of Biomechatronics and Intelligent Robotics(BIRO) Department of Mechanical and Aerospace EngineeringNorth Carolina State UniversityRaleighNorth CarolinaUSA

The positional information of objects is crucial to enable robots to perform grasping and pushing manipulations in *** effectively perform grasping and pushing manipu-lations,robots need to perceive the position information of objects,including the co-ordinates and spatial relationship between objects(e.g.,proximity,adjacency).The authors propose an end-to-end position-aware deep Q-learning framework to achieve efficient collaborative pushing and grasping in ***,a pair of conjugate pushing and grasping attention modules are proposed to capture the position information of objects and generate high-quality affordance maps of operating positions with features of pushing and grasping *** addition,the authors propose an object isolation metric and clutter metric based on instance segmentation to measure the spatial re-lationships between objects in cluttered *** further enhance the perception capacity of position information of the objects,the authors associate the change in the object isolation metric and clutter metric in cluttered environment before and after performing the action with reward function.A series of experiments are carried out in simulation and real-world which indicate that the method improves sample efficiency,task completion rate,grasping success rate and action efficiency compared to state-of-the-art end-to-end *** that the authors’system can be robustly applied to real-world use and extended to novel *** material is available at https://***/NhG\_k5v3NnM}{https://***/NhG\_k5v3NnM.

关键词： deep learning deep neural networks intelligent robots

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Image Segmentation System of Soft Capsules Based on Deep Learning

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Journal of Institute of Control, robotics and Systems 2024年第6期30卷 607-613页

作者： Jung, Young-Gil Park, Tae-Hyoung Department of Control and Robot Engineering Chungbuk National University Korea Republic of Department of Intelligent Systems and Robotics Chungbuk National University Korea Republic of

Pharmacists in conventional pharmacies and hospitals prepare and visually inspect medications before dispensing these medications to patients, thereby posing risks of exposure through skin contact or inhalation, as well as the potential for generating compounding errors such as incorrect medication types or quantities. To address the limitations of the manual compounding processes, automated compounding and medication-verification systems have been introduced and implemented. The automated compounding system prepares medications in pouch units based on prescriptions written by doctors, whereas the medication verification system verifies whether the types and quantities of the medication dispensed in these pouches match the prescription information. Medications are categorized broadly into solid tablets, hard capsules, and soft capsules. Color and shape information is used to classify medication types;solid tablets and hard capsules can be classified using rule-based image processing techniques such as image binarization. However, extracting shape information from soft capsules through which light passes is impossible via image binarization alone;therefore pattern matching is employed. Nevertheless, soft capsules change their forms, causing significant variations in pattern-matching similarity;therefore the detection accuracies of these capsules are low. To address this issue, this paper proposes a deep learning-based method for detecting soft capsules. © ICROS 2024.

关键词： Pattern matching

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Bifunctional nanoprobe for simultaneous detection of intracellular reactive oxygen species and temperature in single cells

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Microsystems & Nanoengineering 2024年第6期10卷 161-172页

作者： Yanmei Ma Weikang Hu Jian Hu Muyang Ruan Jie Hu Ming Yang Yi Zhang Hanhan Xie Chengzhi Hu Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy EngineeringSouthern University of Science and TechnologyShenzhen 518000China Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhen 518000China

Living cells can rapidly adjust their metabolic activities in response to external stimuli,leading to fluctuations in intracellular temperature and reactive oxygen species(ROS)*** these parameters is essential for understanding cellular metabolism,particularly during dynamic biological *** this study,we present a bifunctional nanoprobe capable of simultaneous measurement of ROS levels and temperature within single *** nanoprobe features two individually addressable nanoelectrodes,with platinum(Pt)and nickel(Ni)coatings on both *** the tip,these two metal layers form a nano-thermocouple,enabling precise intracellular temperature measurements,while the Pt layer facilitates selective ROS *** dual functionality allows for real-time monitoring of cellular responses during synergistic chemo-photothermal therapy of cancer cells and zebrafish embryos subjected to mitochondrial toxic *** results demonstrate that the nanoprobe effectively measures increases in temperature and ROS levels in HeLa cells undergoing chemo-photothermal therapy,as well as in chemically stimulated zebrafish *** providing detailed analysis of submicrometer-scale temperature and ROS variations within living cells,this nanoprobe offers valuable insights into cellular processes and holds promise for early disease detection and drug development.

关键词： metabolism simultaneous holds

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A trajectory-level robot skill learning method based on fuzzy systems

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Science China(Information Sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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Skeleton-Based Action Analysis for Improving Jump Height in Volleyball 6

Skeleton-Based Action Analysis for Improving Jump Height in ...

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6th International Symposium on Advanced Technologies and Applications in the Internet of Things, ATAIT 2024

作者： Yoshioka, Shuma Kong, Xiangbo Department of Intelligent Robotics Faculty of Information Engineering Toyama Prefectural University Japan

This study aims to improve the jumping height for spiking in volleyball through skeleton-based action analysis using the Azure Kinect DK. Building on previous studies, this work focuses on the stride length, arm swing height, and spine angle during the jumping motion. Experiments are conducted to compare the effects of relatively better and relatively worse executions of these movements on jumping height. The results indicate that a relatively better stride length resulted in a significant increase of 187 mm in jumping height, while a relatively better arm swing height led to a 34 mm increase. However, variations in spine angle do not produce a significant difference in jumping height. These results suggest that optimizing stride length and arm swing height can effectively enhance the jumping performance of volleyball players. © 2024 Copyright for this paper by its authors.

关键词： Musculoskeletal system

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Heat Transfer Area Optimization for Heat Exchanger System

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Computer Modeling in engineering & Sciences 2025年第4期143卷 335-349页

作者： Yu-Cheng Liao Fu-I Chou Po-Yuan Yang Jyh-Horng Chou Department of Electrical Engineering National Kaohsiung University of Science and TechnologyKaohsiung807Taiwan Department of Intelligent Robotics National Pingtung UniversityPingtung900Taiwan Department of Mechanical and Computer-Aided Engineering Feng Chia UniversityTaichung407Taiwan

This paper presents an allowable-tolerance-based group search optimization(AT-GSO),which combines the robust GSO(R-GSO)and the external quality design planning of the Taguchi ***-GSO algorithm is used to optimize the heat transfer area of the heat exchanger *** R-GSO algorithm integrates the GSO algorithm with the Taguchi method,utilizing the Taguchi method to determine the optimal producer in each iteration of the GSO algorithm to strengthen the robustness of the search process and the ability to find the global *** conventional parameter design optimization,it is typically assumed that the designed parameters can be applied accurately and consistently throughout ***,for systems that are sensitive to changes in design parameters,even minor inaccuracies can substantially reduce overall system ***,the permissible variations of the design parameters are considered in the tolerance-optimized design to ensure the robustness of the *** optimized design of the heat exchanger system assumes that the system’s operating temperature parameters are ***,fixing the systemoperating temperature parameters at a constant value is *** paper assumes that the system operating temperature parameters have an uncertainty error when optimizing the heat transfer area of the heat exchanger *** results show that the AT-GSO algorithm optimizes the heat exchanger system and finds the optimal operating temperature in the absence of tolerance and under three tolerance conditions.

关键词： Heat exchanger system allowable tolerance-based group search optimization Taguchi method tolerance design

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