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IEEE International Conference on robotics and Biomimetics

作者： Jinrui Liu Ting Song Zhilin Shu Jianda Han Ningbo Yu College of Artificial Intelligence Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen China

ISBN: (纸本)9781665405362

It has been of increasing interest to decode the movements based on the changes in hemoglobin concentration in the cerebral cortex that can be measured by functional near-infrared spectroscopy (fNIRS). Existing work were mainly focused on motor imagery tasks for the advantages of fNIRS on cognitive activation detection, and motor tasks were not well studied. This paper investigated fNIRS feature extraction and classification methods for grip-force tasks. Two methods were implemented and compared. The first method took the classic machine learning approach. Features were extracted with respect to the physiological characteristics of fNIRS signals, including the mean, area and slope features. Then, support vector machine was used for classification of grip-force tasks with the extracted features. The second method took the deep learning approach without extracting features. An LSTM-based network was constructed to take in the fNIRS data and output the classified task type. The two methods were tested and compared with an grip-force task fNIRS dataset. The classic machine learning method with extracted features achieved the accuracy of 71.70%, and the LSTM-based deep learning method achieved 82.86%. The results demonstrate the feasibility and potential of fNIRS-based decoding of motor tasks.

关键词： Deep learning Support vector machines Cerebral cortex Stroke (medical condition) Feature extraction Decoding Task analysis

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Land Surface Displacement Geohazards Monitoring Using Multi-temporal InSAR Techniques

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Journal of Geodesy and Geoinformation science 2021年第1期4卷 77-87页

作者： Guang LIU Perski ZBIGNIEW Salvi STEFANO Thiebes BENNI Lixin WU Jinghui FAN Shibiao BAI Lianhuan WEI Shiyong YAN Rui SONG Bignami CHRISTIAN Tolomei CRISTIANO Stefan SCHNEIDERBAUER Joao Sousa JOAQUIM 无 Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100094China Carpathian Branch Polish Geological Institute-National Research InstituteCracow 31-560Poland National Institute of Geophysics and Volcanology Roma 605-00143Italy German Committee for Disaster Reduction Bonn 1353113Germany School of Resources and Civil Engineering Northeastern UniversityShenyang 110819China Division of Remote Sensing for Environment China Aero Geophysical Survey&Remote Sensing Center for Natural ResourcesBeijing 100083China School of Marine Science and Ocean Engineering Nanjing Normal UniversityNanjing 210046China School of Environment Science and Spatial Informatics China University of Mining and TechnologyXuzhou 221116China Mullard Space Science Laboratory Department of Space and Climate Physics University College London Holmbury RH56NTUK Eurac Research Bolzano 39100Italy School of Science and Technology University of Trás-os-Montes e Alto DouroVila Real 5000-801Portugal Centre for Robotics in Industry and Intelligent Systems(CRIIS) INESC Technology and Science(INESC-TEC)Porto 4200-465Portugal

China has been affected by some of the world’s most serious geological disasters and experiences high economic damage every *** occur not only in remote areas but also in highly populated *** the framework of the Dragon-432365 Project,this paper presents the main results and the major conclusions derived from an extensive exploitation of Sentinel-1,ALOS-2(Advanced Land Observing Satellite 2),GF-3(Gao Fen Satellite 3),and latest launched SAR(Synthetic Aperture Radar),together with methods that allow the evaluation of their importance for various ***,in the scope of this project,the great benefits of recent remote sensing data(wide spatial and temporal coverage)that allow a detailed reconstruction of past displacement events and to monitor currently occurring phenomena are exploited to study different areas and geohazards problems,including:surface deformation of mountain slopes;identification and monitoring of ground movements and subsidence;landslides;ground fissure;and building inclination *** movements detected in the different study areas were cross validated with different SAR sensors and truth data.

关键词： Dragon-4 project Sentinel-1 GF-3 landslide geohazards InSAR

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Using Machine Learning Approaches to Localization in an Embedded System on RobotAtFactory 4.0 Competition: A Case Study

Using Machine Learning Approaches to Localization in an Embe...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Luan C. Klein João Braun Felipe N. Martins Heinrich Wörtche Andre Schneider de Oliveira João Mendes Vítor H. Pinto Paulo Costa José Lima Universidade Tecnológica Federal do Paraná Campus Curitiba UTFPR/PR Brasil Research Centre in Digitalization and Intelligent Robotics (CeDRI) Instituto Politécnico de Braganca Campus de Santa Apolonia Portugal Laboratory for Sustainability and Technology in Mountain Regions (SusTEC) Instituto Politécnico de Bragança Bragança Portugal Faculty of Engineering of University of Porto Portugal INESC Technology and Science Porto Portugal Sensors and Smart Systems Group Institute of Engineering Hanze University of Applied Sciences The Netherlands Department of Electrical Engineering Eindhoven University of Technology The Netherlands SYSTEC (DIGI2) ARISE & ECE Dept. Fac. de Engenharia Universidade do Porto Rua Dr. Roberto Frias 4200-465 Porto Portugal

The use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.

关键词： Location awareness Training Embedded systems Machine learning algorithms Navigation Decision making Machine learning

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Achieving consensus in spite of stubbornness: time-varying concatenated Friedkin-Johnsen models

Achieving consensus in spite of stubbornness: time-varying c...

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IEEE Conference on Decision and Control

作者： L. Wang C. Bernardo Y. Hong F. Vasca G. Shi C. Altafini Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Group for Research on Automatic Control Engineering University of Sannio Benevento Italy Tongji University Shanghai China Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney NSW Sydney Division of Automatic Control Linköping University Linköping Sweden

ISBN: (纸本)9781665436601

A concatenated Friedkin-Johnsen (FJ) model is a two time-scale opinion dynamics model in which stubborn agents discuss a sequence of issues. For each issue, a FJ model is adopted, and concatenation refers to the fact that the final opinion of the agents at issue s becomes the initial condition at issue s + 1. In this paper we deal with the case in which the system is open, i.e., the group of interacting agents changes at each issue, and so does their stubbornness. A concatenated FJ model can in this case be represented as an infinite product of stochastic matrices. For such system, we obtain sufficient conditions under which the opinions of the agents converge to consensus.

关键词： Sufficient conditions Conferences Stochastic processes

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Cooperative range-based navigation using a beacon with circular motion installed on board the support platform

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IFAC-PapersOnLine 2019年第21期52卷 390-395页

作者： Santiago Rúa Naveen Crasta Rafael E. Vásquez Manuel J. Betancur Antonio M. Pascoal Universidad Pontificia Bolivariana Circular 1 # 70-01 School of Engineering 050031 Medellín Colombia Laboratory of Robotics and Systems in Engineering and Science (LARSyS) IST/University of Lisbon Av. Rovisco Pais 1 1049-001 Lisbon Portugal

This work addresses the observability analysis for a cooperative range-based navigation system that uses a beacon with circular motion installed on board the support platform to find the location of an underwater vehicle. A nonlinear model is first obtained in order to describe the motion of the vehicle and the beacon, that is attached to the tip of a 1 DoF rotating arm (deployed from a stationary support platform or vessel). A state-space realization is obtained and augmented in order to convert the nonlinear system into a Linear Time Varying (LTV) system. Then, the observability analysis is performed for the new system considering its equivalence since the state trajectories of the LTV system have one-to-one correspondence to the state trajectories of the nonlinear system. We show that, when the vehicle moves in circles or in straight lines, it is possible to ensure observability by rotating the arm that has the beacon attached. This result is important from a theoretical point of view, and represents a new way of implementing range-based navigation systems, without relying on deviation of the vehicle’s mission or the need of another vehicle to ensure observability.

关键词： Vehicle guidance navigation Underwater vehicles Control system design applications Cooperative control Acoustic-Based Networked Control Navigation

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Fundus-Image-Based Retinal Vascular Analysis in Myopic Eyes

Fundus-Image-Based Retinal Vascular Analysis in Myopic Eyes

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Chinese Control Conference (CCC)

作者： Jichen Shen Haohan Zou Yan Wang Jinchao Liu Ningbo Yu College of Artificial Intelligence Nankai University Tianjin China Clinical College of Ophthalmology Tianjin Medical University Tianjin China Tianjin Key Lab of Ophthalmology and Visual Science Tianjin Eye Institute Tianjin Eye Hospital Nankai University Affiliated Eye Hospital Tianjin China Nankai University Eye Institute Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China Engineering Research Center of Trusted Behavior Intelligence Ministry of Education Nankai University Tianjin China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen China

Myopia is prevalent worldwide and it is of great significance to prevent, control and intervene myopia in an early stage. The etiopathogenesis of myopia remains unclear and needs further research. Fundus photographs can show biomarkers and lesions of the retina in a non-invasive way directly. To discover some potential changes in myopic eyes, we extracted the retinal vascular features from vessel segmented results, and calculated the Spearman correlation coefficients between the retinal vascular features and the spherical refractive errors. The Kruskal-Wallis test was applied to explore the difference exists in the characteristics of retinal vessels among the mild, moderate and high myopia groups. The statistical analysis results indicated that the higher the degrees of myopia, the smaller the retinal vessels. This study found vascular changes in myopic eyes through fundus images, which could help to explain the mechanism of myopia and the damage caused by myopia.

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Mapping Surgeons Hand/Finger Movements to Surgical Tool Motion during Conventional Microsurgery Using Machine Learning

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Journal of Medical robotics Research 2021年第3-4期6卷

作者： Fattahi Sani, Mohammad Ascione, Raimondo Dogramadzi, Sanja Bristol Robotics Laboratory University of the West of England Bristol United Kingdom Bristol Heart Institute and Translational Biomedical Research Centre Faculty of Health Science University of Bristol Bristol United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffiled United Kingdom

Purpose: Recent developments in robotics and artificial intelligence (AI) have led to significant advances in healthcare technologies enhancing robot-Assisted minimally invasive surgery (RAMIS) in some surgical specialties. However, current human-robot interfaces lack intuitive teleoperation and cannot mimic surgeon's hand/finger sensing required for fine motion micro-surgeries. These limitations make teleoperated robotic surgery not less suitable for, e.g. cardiac surgery and it can be difficult to learn for established surgeons. We report a pilot study showing an intuitive way of recording and mapping surgeon's gross hand motion and the fine synergic motion during cardiac micro-surgery as a way to enhance future intuitive teleoperation. Methods: We set to develop a prototype system able to train a Deep Neural Network (DNN) by mapping wrist, hand and surgical tool real-Time data acquisition (RTDA) inputs during mock-up heart micro-surgery procedures. The trained network was used to estimate the tools poses from refined hand joint angles. Outputs of the network were surgical tool orientation and jaw angle acquired by an optical motion capture system. Results: Based on surgeon's feedback during mock micro-surgery, the developed wearable system with light-weight sensors for motion tracking did not interfere with the surgery and instrument handling. The wearable motion tracking system used 12 finger/thumb/wrist joint angle sensors to generate meaningful datasets representing inputs of the DNN network with new hand joint angles added as necessary based on comparing the estimated tool poses against measured tool pose. The DNN architecture was optimized for the highest estimation accuracy and the ability to determine the tool pose with the least mean squared error. This novel approach showed that the surgical instrument's pose, an essential requirement for teleoperation, can be accurately estimated from recorded surgeon's hand/finger movements with a mean squared error (M

关键词： feature extraction hand tracking machine learning minimally invasive surgery real-Time low-cost hand tracking Robot-Assisted surgery

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Heuristics-based Adaptive Biased Random Walk Algorithm for Chemical Source Localization using AUVs

Heuristics-based Adaptive Biased Random Walk Algorithm for C...

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OCEANS

作者： Shubham Garg Antonio Pascoal Sanjeev Afzulpurkar Laboratory of Robotics and Engineering Systems (LARSyS) ISR/IST University of Lisbon Portugal Marine Instrumentation Division National Institute of Oceanography Goa India

We draw inspiration from the behavior of single-celled organisms to present a chemotaxis-inspired Adaptive Biased Random Walk (ABRW) guidance-control law for an Autonomous Underwater Vehicle (AUV). We build on previous results available in the literature to derive a random-walk based guidance-control law for an AUV to track-in and localize a potential chemical source in a turbulence-dominated environment. The ABRW-Strategy makes use of common plume-tracking and heuristic schemes for real-time path planning of the AUV. We further draw out a more comprehensive study of the guidance-strategy and extend the work for implementation in the Medusa class of vehicles that are developed in-house by Instituto Superior Tecnico (IST). The performance of the system is assessed via Hardware-in-the-loop (HIL) simulations to illustrate the viability of using random-walk for chemical source localization. The results obtained are encouraging for in-water tests with an autonomous vehicle of the Medusa class aiming at the validation of the proposed guidance-strategy in real-time experiments.

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HITTSFL: Design of a Cost-Effective HIS-Insensitive TNU-Tolerant and SET-Filterable Latch for Safety-Critical Applications

HITTSFL: Design of a Cost-Effective HIS-Insensitive TNU-Tole...

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Design Automation Conference

作者： Aibin Yan Xiangfeng Feng Xiaohu Zhao Hang Zhou Jie Cui Zuobin Ying Patrick Girard Xiaoqing Wen Anhui Engineering Laboratory of IoT Security Technologies / School of Computer Science and Technology Anhui University Hefei China Laboratory of Informatics Robotics and Microelectronics of Montpellier University of Montpellier / CNRS Montpellier France Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan

ISBN: (数字)9781728110851

ISBN: (纸本)9781728158020

This paper proposes a cost-effective, high-impedance-state (HIS)-insensitive, triple-node-upset (TNU)-tolerant and single-event-transient (SET)-filterable latch, namely HITTSFL, to ensure high reliability with low-cost. The latch mainly comprises an output-level SET-filterable Schmitt-trigger and three inverters that make the values stored in three parallel single-node-upset (SNU)-recoverable dual-interlocked-storage-cells (DICEs) converge at a common node to tolerate any possible TNU. The latch does not use C-elements to be insensitive to the HIS. Simulation results demonstrate the TNU-tolerability and SET-filterability of the proposed HITTSFL latch. Moreover, due to the use of clock-gating technologies and fewer transistors, the proposed latch can reduce delay, power, and area by 76.65%, 6.16%, and 28.55%, respectively, compared with the state-of-the-art TNU hardened latch (TNUHL) that cannot filter SETs.

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Switching function-based optimal active fault-tolerant bipartite consensus control for UAV swarm

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Automatica 2025年 179卷

作者： Pei-Ming Liu Xiang-Gui Guo Jian-Liang Wang Hao Lu Zheng-Guang Wu Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 100083 China Shunde Innovation School University of Science and Technology Beijing Foshan 528000 China Collaborative Flying Robotics Lab Hangzhou Innovation Institute of Beihang University Hangzhou 310051 China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 China

This paper studies the problem of optimal active fault-tolerant bipartite consensus control for flying wing unmanned aerial vehicle (UAV) swarm with nonidentical and unknown direction faults (NUDFs) and disturbances by integrating switching and saturation functions. An observer–controller framework is employed to prevent error propagation among follower UAVs. Based on this framework, an active fault-tolerant control strategy is proposed to improve the system’s transient performance. This strategy prevents sudden shocks from excessive reverse inputs on the system and avoids state chattering caused by excessive adjustment inputs, which often occur in the Nussbaum function-based fault-tolerant control method. Additionally, the newly proposed switching criterion directly matches the desired operating mode, even in the presence of disturbances and multiple fault direction changes, thus avoiding ineffective switching and enhancing the robustness of the designed controller. To further improve the UAVs’ transient performance, a reinforcement learning (RL)-based preset performance backstepping control method is introduced. This method optimizes output regulation, saves energy consumption, and reduces the impact of soft saturation on system performance. Finally, simulation results validate the effectiveness and superiority of the proposed scheme.

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