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IEEE/SICE International Symposium on System Integration

作者： Anh Quyen Pham Duc La Hung Manh La AIR-VIET Corporation in Vietnam Kahlert School of Computing University of Utah Salt Lake City UT USA Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA

More and more steel bridge collapse accidents occur worldwide due to broken steel structures, causing significant l oss o f l ife a nd p roperty t o m ankind. T his has spurred much research into robots that can climb steel surfaces and carry smart sensors with the desire to assist inspectors in inspecting steel defects. However, current non-destructive evaluation (NDE) sensors such as eddy-current and giant magnetoresistance (GMR) are not able to be integrated with the robotic platforms due to their large size, mass, and limited ability to interface. More importantly, these NDE sensors may fail to detect hidden and underlying cracks. Therefore, in this paper, we present a novel and compact analog magnetic sensor system to detect different types of cracks in steel. Validations are carried out on a steel test plate 600mm long, 140mm wide, and 6mm thick, and precision machined by CNCs to produce various man-made cracks including penetrating cracks, surface cracks, internal/hidden cracks, and underlying cracks. These cracks have a width varying from 0.6mm to 1.2mm with different depth levels. Combined with applying the Kalman filter t o t he n oise r eduction s ystem, t he r esults o btained are accurate, and the response speed is fast. The sensor is small enough and has firmware/software with an interface to make it possible to be integrated with a small drone or a climbing robot to perform an in-depth inspection of the steel bridge. A demonstration of the system can be seen in this video: https://***/OrdpLdnFlYk

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A Multi-model Fusion of LiDAR-inertial Odometry via Localization and Mapping

A Multi-model Fusion of LiDAR-inertial Odometry via Localiza...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： An Duy Nguyen Chuong Phuoc Le Pratik Walunj Anton Netchaev Thanh Nho Do Hung Manh La Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA USACE Engineer Research and Development Center (ERDC) Information Technology Lab Vicksburg MS USA Graduate School of Biomedical Engineering UNSW Sydney Kensington Campus NSW Australia

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This work presents a comprehensive LiDAR-inertial odometry framework featuring robust smoothing and mapping capabilities, effectively correcting LiDAR feature point skewness using an inertial measurement unit (IMU). While the Extended Kalman Filter (EKF) is a common choice for nonlinear motion estimation, its complexity grows when handling maneuvering targets. To overcome this challenge, a new framework that incorporates the Iterated Interactive Multiple Models of Kalman Filter (IMMKF) is given, providing a solution for reliable navigation in dynamic motion and noisy conditions. To ensure map consistency, an ikd-tree that facilitates continuous updates and adaptive rebalance is employed, preserving the map’s integrity. To guarantee the robustness of our approach, it undergoes extensive testing across diverse scales of indoor and outdoor environments. This testing scenario simulates absolute GPS denial. In terms of estimated motion, the new algorithm demonstrates superior accuracy compared to existing approaches. The implementation is openly accessible on GitHub 4 for further exploration.

关键词： Location awareness Technological innovation Laser radar Accuracy Smoothing methods Computational modeling Feature extraction Odometry Kalman filters Testing

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Rethinking development and major research plans of Industrial Internet in China

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Fundamental Research 2024年第1期4卷 3-7页

作者： Chunxiao Jiang Yang Cong Jiming Chen Chenghong Wang Guozheng Wu Ruizhen Zhao Zhiheng Wang Bin Xiao Ting Chen Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Tsinghua Space Center Tsinghua UniversityBeijing 100084China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China Division III Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Division II Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Chongqing Key Laboratory of Image Cognition School of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqing 400065China University of Electronic Science and Technology of China Chengdu 611731China

This paper gives a definition of the Industrial Internet and expounds on the academic connotation of the future Industrial *** this foundation,we outline the development and current status of the Industrial Internet in China and ***,we detail the avant-garde paradigms encompassed within the National Natural science Foundation of China(NSFC)’s“Future Industrial Internet Fundamental Theory and Key Technologies”research plan and its corresponding management *** research initiative endeavors to enhance interdisciplinary collaborations,aiming for a synergistic alignment of industry,academia,research,and practical *** primary focus of the research plan is on the pivotal scientific challenges inherent to the future industrial *** is poised to traverse the“first mile”,encompassing foundational research and pioneering innovations specific to the industrial internet,and seamlessly bridges to the“last mile”,ensuring the effective commercialization of scientific and technological breakthroughs into tangible industrial market *** anticipated contributions from this initiative are projected to solidify both the theoretical and practical scaffolding essential for the cultivation of a globally competitive industrial internet infrastructure in China.

关键词： Industrial Internet Academic connotation Major research plan Scientific problem Policy advice

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A Subspace-Based Non-Dominated Subset Selection Method

A Subspace-Based Non-Dominated Subset Selection Method

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Tan, Qingshan Li, Changhe Zeng, Sanyou Yang, Shengxiang Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

ISBN: (纸本)9798350314588

Environmental selection is an important process in multi-objective evolutionary algorithms (MOEAs). As the evolution progresses, the number of non-dominated solutions increases. This paper is focused on selecting a subset from excess non-dominated solutions for evolutionary or the final output. However, traditional selection methods in classical MOEAs encounter difficulties when dealing with candidate solutions that possess irregular topologies. Although the distance-based subset selection methods are not sensitive to the topologies of the candidate points, they have significant room for reducing computational complexity. In order to address the above issues, a subspace selection method is proposed in this paper. It partitions the objective space into multiple subspaces that have comparable volumes and shapes. The maximal minimum distance of each solution is considered to ensure that the sparsest solution is always chosen first. To save computational costs, only the solutions in the neighboring subspaces are taken into account. The experimental results demonstrate that the proposed subspace selection method outperforms classical selection methods in solving problems with various shapes of the Pareto front. © 2023 IEEE.

关键词： Multiobjective optimization

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Risk-Aware Non-Myopic Motion Planner for Large-Scale Robotic Swarm Using CVaR Constraints

Risk-Aware Non-Myopic Motion Planner for Large-Scale Robotic...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Xuru Yang Yunze Hu Han Gao Kang Ding Zhaoyang Li Pingping Zhu Ying Sun Chang Liu Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing China Department of Automation Tsinghua University Beijing China Department of Computer Sciences and Electrical Engineering (CSEE) Marshall University Huntington WV USA School of Electrical Engineering and Computer Science The Pennsylvania State University State College PA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Swarm robotics has garnered significant attention due to its ability to accomplish elaborate and synchronized tasks. Existing methodologies for motion planning of swarm robotic systems mainly encounter difficulties in scalability and safety guarantee. To address these limitations, we propose a Risk-aware swarm mOtion planner using conditional ValuE-at-Risk (ROVER) that systematically navigates large-scale swarms through cluttered environments while ensuring safety. ROVER formulates a finite-time model predictive control (FTMPC) problem predicated upon the macroscopic state of the robot swarm represented by a Gaussian Mixture Model (GMM) and integrates conditional value-at-risk (CVaR) to ensure collision avoidance. The key component of ROVER is imposing a CVaR constraint on the distribution of the Signed Distance Function between the swarm GMM and obstacles in the FTMPC to enforce collision avoidance. Utilizing the analytical expression of CVaR of a GMM derived in this work, we develop a computationally efficient solution to solve the non-linear constrained FTMPC through sequential linear programming. Simulations and comparisons with representative benchmark approaches demonstrate the effectiveness of ROVER in flexibility, scalability, and safety guarantee.

关键词： Navigation Scalability Swarm robotics Benchmark testing Safety Planning Trajectory Synchronization Collision avoidance Robots

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An Image Acquisition Scheme for Visual Odometry based on Image Bracketing and Online Attribute Control

An Image Acquisition Scheme for Visual Odometry based on Ima...

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IEEE International Conference on robotics and automation (ICRA)

作者： Shuyang Zhang Jinhao He Bohuan Xue Jin Wu Pengyu Yin Jianhao Jiao Ming Liu Department of Electronic and Computer Engineering the Hong Kong University of Science and Technology Hong Kong SAR China Thrust of Robotics and Autonomous Systems the Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Center for Advanced Robotics Technology Innovation (CARTIN) School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science University College London London UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Visual odometry (VO) system is challenged by complex illumination environments. Image quality and its consistency in the time domain directly determine feature detection and tracking performance, which further affect the robustness and accuracy of the entire system. In this paper, an image acquisition scheme with image bracketing patterns is proposed. Images with different exposure levels are continuously captured to sufficiently explore the scene under varying illumination. An attribute control method is designed to adjust image exposures within the brackets online. Gaussian process regression fits the relationship between image quality metric and exposure via image synthesis technique. The optimal exposures for the next bracket are obtained directly without attempts to ensure a quick response. Experiments show our acquisition system’s effectiveness and performance improvement for VO tasks in complex illumination scenes.

关键词： Image quality Measurement Image synthesis Lighting Gaussian processes Robustness Time-domain analysis

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Multi-feature Fusion Based Deep Forest for Hyperspectral Image Classification 16th

Multi-feature Fusion Based Deep Forest for Hyperspectral Ima...

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16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Liu, Xiaobo Mohammad, Mostofa Zaman Zhang, Chaochao Gong, Xin Cai, Zhihua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Computer Science China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789811912528

Multi-feature fusion is a useful way to improve the classification of hyperspectral image (HSI). But the multi-feature fusion is usually at the decision level of classifier, which causes less link between features or poor extensibility of feature. In this paper, we propose a multi-feature fusion based deep forest method for HSI classification, named mfdForest. In mfdForest, the morphological features, saliency features, and edge features are extracted, then the three deep multi-grained scanning branches in dgcForest (one of improved deep forest) are used to extract and fuse the extracted features deeply, and the fused features are sent into cascade forest in dgcForest for classification. Experimental results indicate that the proposed framework consumes less training time and has better performance on two HSI data sets. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Spectroscopy

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Value Stream Mapping for Smart Pharmaceutical Management in a Thai Hospital 6

Value Stream Mapping for Smart Pharmaceutical Management in ...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Boonsothonsatit, Ganda Silapunt, Rardchawadee Vongbunyong, Supachai Kaemarungsi, Kamol Chanpuypetch, Wirachchaya Chonsawat, Nilubon Graduate School of Management and Innovation King Mongkut's University of Technology Thonburi Thailand Electronic and Telecommunication Engineering King Mongkut's University of Technology Thonburi Thailand Hospital Automation Research Center Institute of Field Robotics King Mongkut's University of Technology Thonburi Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Thailand Faculty of Agro-Industry Chiang Mai University Thailand Department of Agricultural Economy and Developement Faculty of Agriculture Chiang Mai University Thailand

The evolution of healthcare systems worldwide necessitates continual improvement in hospital management practices, particularly pharmaceutical management. This paper explores transforming traditional pharmaceutical management systems in large and medium-sized hospitals in Thailand into smart systems using Value Stream Mapping (VSM) as a guiding tool. Traditional systems often face challenges such as inefficiencies and medication errors. VSM, rooted in lean management principles, offers a structured approach to visualize and optimize processes. Additionally, smart technologies like automated inventory systems and electronic health records enhance pharmaceutical management effectiveness. Through a comprehensive research methodology encompassing case studies, data collection, and VSM analysis, this study identifies inefficiencies and proposes evidence-based strategies for improvement. Results from a case study highlight the benefits of VSM implementation in enhancing pharmacy operations, emphasizing improved patient care and operational efficiency. The implementation of a smart medication dispensing system shows promising results in reducing workload and accommodating increased patient arrivals. Continued evaluation and refinement are crucial for maximizing benefits and addressing emerging challenges effectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： Electronic health record

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PTC-FOZNN: A Strictly Predefined-Time Convergent Fractional-Order Recurrent Neural Network for Solving Time-Variant Quadratic Programming

PTC-FOZNN: A Strictly Predefined-Time Convergent Fractional-...

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International Conference on Control and automation (ICCA)

作者： Yi Yang Weibing Li Jianshu Zhou Junda Huang Jinfei Hu Richard M. Voyles Xin Ma Department of Mechanical and Automation Engineering Multi-Scale Medical Robotics Center The Chinese University of Hong Kong Hong Kong China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China School of Engineering Technology Purdue University West Lafayette IN USA

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This study presents a Predefined-Time Convergent Fractional-Order Zeroing Neural Network (PTC-FOZNN) as an innovative solution for Time-Invariant Quadratic Programming (TIQP) and Time-Variant Quadratic Programming (TVQP) problems. This model represents a noteworthy integration of fractional calculus into recurrent neural networks for cyclic motion planning of robots. The PTC-FOZNN diverges from the conventional Zeroing Neural Network (ZNN) by implementing a conformable fractional derivative that complies with the Leibniz rule-a standard often unmet by existing fractional derivatives. Additionally, it employs a novel predefined-time stabilizer as its activation function. When compared to the traditional ZNN, the PTC-FOZNN, configured with $0 < \alpha\leq 1$ , exhibits enhanced convergence speed and greater positional accuracy for both TIQP and TVQP tasks. Through meticulous theoretical and empirical validation, including simulation trials with a Franka Emika Panda robotic arm, the PTC-FOZNN model has proven its effectiveness in precise trajectory tracking and computational efficacy.

关键词： Recurrent neural networks Trajectory tracking Kinematics Manipulators Planning Quadratic programming Task analysis

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Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 74-87页

作者： Bogang Qu Zidong Wang Bo Shen Hongli Dong Hongjian Liu the College of Automation Engineering Shanghai University of Electric PowerShanghai 200090China IEEE the Department of Computer Science Brunel University LondonUxbridgeMiddiesex UB83PHUK the College of Information Science and Technology Donghua UniversityShanghai 200051 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the Artificial Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast PetroleumDaqing 163318 the Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of EducationAnhui Polytechnic UniversityWuhu 2410000 the School of Mathematics and Physics Anhui Polytechnic UniversityWuhu 241000China

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

关键词： Decentralized state estimation(SE) measurements with anomalies minimum error entropy unscented Kalman filter wide-area power systems

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