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arXiv 2021年

作者： Cai, Kuanqi Chen, Weinan Wang, Chaoqun Zhang, Hong Meng, Max Q.-H. Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China School of Electro-mechanical Engineering Guangdong University of Technology China School of control science and Engineering Shandong University Shandong China Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Shenzhen Research Institute of The Chinese University of Hong Kong Shenzhen518057 China

Mobile robots have become more and more popular in large-scale and crowded environments, such as airports, shopping malls, etc. However, due to sparse landmarks and crowd noise, localization in this environment is a great challenge. Furthermore, it is unreliable for the robot to navigate safely in crowds while considering human comfort. Thus, how to navigate safely with localization precision in that environment is a critical problem. To solve this problem, we proposed a curiosity-based framework that can find an effective path with the consideration of human comfort and crowds, localization uncertainty, and the cost-to-go to the target. Three parts are involved in the proposed framework: the distance assessment module, the Curiosity for Positive Content (CPC), namely information-rich areas, and the Curiosity for Negative Content (CNC), namely crowded areas. CPC is introduced when the real-time localization uncertainty evaluation is not satisfied. This factor is predicted through the propagation of uncertainty along the candidate trajectory to provoke the robot to approach localization-referenced landmarks. The Human Comfort and Crowd Density Map (HCCDM) based on the Gaussian Mixture Model (GMM) is established to calculate CNC, which drives the robot to bypass the crowd and consider human comfort. The evaluation is conducted in a series of large-scale and crowded environments. The results show that our method can find a feasible path that can consider the localization uncertainty while simultaneously avoiding the crowded area. Copyright © 2021, The Authors. All rights reserved.

关键词： Motion planning

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SHUYU Robot:An Automatic Rapid Temperature Screening System

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Chinese Journal of Mechanical engineering 2020年第2期33卷 1-4页

作者： Zhao Gong Songwen Jiang Qizhi Meng Yanlei Ye Peng Li Fugui Xie Huichan Zhao Chunzhe Lv Xiaojie Wang Xinjun Liu The State Key Laboratory of Tribology&Tsinghua University(DME)‑Siemens Joint Research Center for Advanced Robotics Department of Mechanical Engineering(DME)Tsinghua UniversityBeijing 100084China Beijing Key Lab of Precision/Ultra‑precision Manufacturing Equipments and Control Tsinghua UniversityBeijing 100084China Yantai Tsingke+Robot Joint Research Institute Co. Ltd.Yantai 264000ShandongChina The First Affiliated Hospital of Tsinghua University Beijing 100016China

The health of people around the world and the global economy are under substantial threat from the outbreak of pandemics[1].controlling pandemics is extremely challenging,with preventing the spread of pathogens the most important and critical *** all preventative actions,body temperature screening is undoubtedly highly necessary and effective[2].

关键词： Robot doubt critical

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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Dynamics of opinions with bounded confidence in social cliques: Emergence of fluctuations

arXiv

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arXiv 2021年

作者： Zhang, Jiangbo Yuan, Deming Wang, Lei Altafini, Claudio Shi, Guodong School of Sciences Southwest Petroleum University Chengdu China Research School of Engineering The Australian National University CanberraACT Australia School of Automation Nanjing University of Science and Technology Nanjing China Australian Center for Field Robotics The University of Sydney SydneyNSW2008 Australia Division of Automatic Control Department of Electrical Engineering Linköping University LinköpingSE-58183 Sweden Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney SydneyNSW2008 Australia

In this paper, we study the evolution of opinions over social networks with bounded confidence in social cliques. Node initial opinions are independently and identically distributed;at each time step, nodes review the average opinions of a randomly selected local clique. The clique averages may represent local group pressures on peers. Then nodes update their opinions under bounded confidence: only when the difference between an agent individual opinion and the corresponding local clique pressure is below a threshold, this agent opinion is updated according to the DeGroot rule as a weighted average of the two values. As a result, this opinion dynamics is a generalization of the classical Deffuant-Weisbuch model in which only pairwise interactions take place. First of all, we prove conditions under which all node opinions converge to finite limits. We show that in the limits the event that all nodes achieve a consensus, and the event that all nodes achieve pairwise distinct limits, i.e., social disagreements, are both nontrivial events. Next, we show that opinion fluctuations may take place in the sense that at least one agent in the network fails to hold a converging opinion trajectory. In fact, we prove that this fluctuation event happens with a strictly positive probability, and also constructively present an initial value event under which the fluctuation event arises with probability one. These results add to the understanding of the role of bounded confidence in social opinion dynamics, and the possibility of fluctuation reveals that bringing in cliques in Deffuant-Weisbuch models have fundamentally changed the behavior of such opinion dynamical processes. © 2021, CC BY-NC-ND.

关键词： Dynamics

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An energy efficiency evaluation method for parallel robots based on the kinetic energy change rate

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Science China(Technological Sciences) 2019年第6期62卷 1035-1044页

作者： LIU XinJun BI WeiYao XIE FuGui The State Key Laboratory of Tribology & Tsinghua University (DME)-Siemens Joint Research Center for Advanced Robotics Department of Mechanical Engineering (DME) Tsinghua University Beijing 100084 China Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipments and Control Tsinghua University Beijing 100084 China

Benefit from the high payload-to-weight ratio, parallel robots are expected to have a high potential for energy savings. However,it is a challenging issue to evaluate the energy efficiency of parallel robots with a quantitative method. Quantitative energy efficiency evaluation methods include energy efficiency evaluation models and indices which mathematically describe the relationship between energy consumers in models and design variables of robots, such as geometry, mass and inertia *** the structural features of parallel robots, the chains and the end effectors are identified as two separated energy consumers. Besides, the chains in parallel robots are identified as a transmission system which transfers energy from drives to the end effectors. On this basis, an energy efficiency evaluation model considering the change rate of kinetic energy stored in chains is built. The kinetic energy change rate of chains is influenced by design variables of robots as well as motion of the end *** order to give a quantitative description of energy efficiency performance of parallel robots, indices considering arbitrary velocity vector of the end effector are proposed. The evaluation method is suitable for all kinds of parallel robots with various motion conditions. Furthermore, the method can be used to optimize machining parameters and guide the design of energyefficient machines.

关键词： energy efficiency evaluation parallel robots kinetic energy power transmission

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Reinforcement learning control of constrained dynamic systems with uniformly ultimate boundedness stability guarantee

arXiv

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arXiv 2020年

作者： Han, Minghao Tian, Yuan Zhang, Lixian Wang, Jun Pan, Wei Department of Control Science and Engineering Harbin Institute of Technology China Department of Cognitive Robotics Delft University of Technology Netherlands Department of Computer Science University College London United Kingdom

Reinforcement learning (RL) is promising for complicated stochastic nonlinear control problems. Without using a mathematical model, an optimal controller can be learned from data evaluated by certain performance criteria through trial-and-error. However, the data-based learning approach is notorious for not guaranteeing stability, which is the most fundamental property for any control system. In this paper, the classic Lyapunov’s method is explored to analyze the uniformly ultimate boundedness stability (UUB) solely based on data without using a mathematical model. It is further shown how RL with UUB guarantee can be applied to control dynamic systems with safety constraints. Based on the theoretical results, both off-policy and on-policy learning algorithms are proposed respectively. As a result, optimal controllers can be learned to guarantee UUB of the closed-loop system both at convergence and during learning. The proposed algorithms are evaluated on a series of robotic continuous control tasks with safety constraints. In comparison with the existing RL algorithms, the proposed method can achieve superior performance in terms of maintaining safety. As a qualitative evaluation of stability, our method shows impressive resilience even in the presence of external disturbances. © 2020, CC-BY.

关键词： Reinforcement learning

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Visual Object Detection Based LiDAR Point Cloud Classification

Visual Object Detection Based LiDAR Point Cloud Classificati...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Sualeh Muhammad Gon-Woo Kim Intelligent Robotics Laboratory Control and Robot Engineering Department Chungbuk National University Cheongju Republic of Korea Chungbuk National University Cheongju South Korea

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

The environmental perception plays a pivotal role in autonomous driving tasks and demands robustness in cluttered dynamic environments such as complex urban scenarios. LiDAR being one of the popular perceptual sensor, suffers with large number of inaccurate object detections. This work is an extension of an ongoing research on multiple object detection and tracking. Where, Neural Network based approach is considered for visual detection to aid the LiDAR point cloud processing, and to address the inherent shortcoming of the sensor. It is inferred that the proposed framework would perform in real-time on an embedded platform. In addition, the separate processing of visual and LiDAR sensor data will enable switching to a light weight LiDAR only setup in runtime when required.

关键词： Three-dimensional displays Laser radar Object detection Visualization Robot sensing systems Task analysis Cameras

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A Task Allocation Approach for Human-Robot Collaboration in Product Defects Inspection Scenarios

A Task Allocation Approach for Human-Robot Collaboration in ...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Hossein Karami Kourosh Darvish Fulvio Mastrogiovanni Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Dynamic Interaction Control Istituto Italiano di Tecnologia Genova Italy

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

The presence and coexistence of human operators and collaborative robots in shop-floor environments raises the need for assigning tasks to either operators or robots, or both. Depending on task characteristics, operator capabilities and the involved robot functionalities, it is of the utmost importance to design strategies allowing for the concurrent and/or sequential allocation of tasks related to object manipulation and assembly. In this paper, we extend the FLEXHRC framework presented in [1] to allow a human operator to interact with multiple, heterogeneous robots at the same time in order to jointly carry out a given task. The extended FLEXHRC framework leverages a concurrent and sequential task representation framework to allocate tasks to either operators or robots as part of a dynamic collaboration process. In particular, we focus on a use case related to the inspection of product defects, which involves a human operator, a dual-arm Baxter manipulator from Rethink robotics and a Kuka youBot mobile manipulator.

关键词：

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Finite-time Frequency Estimator for Harmonic Signal

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IFAC-PapersOnLine 2020年第2期53卷 584-589页

作者： Alexey A. Bobtsov Anastasiia O. Vediakova Nikolay A. Nikolaev Olga V. Slita Anton A. Pyrkin Alexey A. Vedyakov Department of Control Systems and Robotics ITMO University Saint Petersburg Russia Laboratory “Control of Complex Systems” Institute of Problems of Mechanical Engineering V.O. Bolshoj pr. 61 St. Petersburg 199178 Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Department of Computer Applications and Systems Saint Petersburg State University Saint Petersburg Russia

This paper is devoted to a frequency estimation of a pure sinusoidal signal in finite-time. The parameterization is based on applying delay operators to a measurable signal. The result is the first-order linear regression model with one parameter, which depends on the signal frequency. The proposed method of finite-time estimation consists of two steps. On the first step, the standard gradient descent method is used to estimate the regression model parameter. On the next step using algebraic equations, finite-time frequency estimate is found. The described method does not require measuring or calculating derivatives of the input signal and uses one integrator for the gradient method and another one for the finite-time estimation. The efficiency of the proposed approach is demonstrated through the set of numerical simulations.

关键词： parameter estimation observers frequency estimation finite-time estimation

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3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video

arXiv

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arXiv 2021年

作者： Wang, Guangming Zhong, Jiquan Zhao, Shijie Wu, Wenhua Liu, Zhe Wang, Hesheng Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Engineering Mechanics Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom

Depth and ego-motion estimations are essential for the localization and navigation of autonomous robots and autonomous driving. Recent studies make it possible to learn the per-pixel depth and ego-motion from the unlabeled monocular video. A novel unsupervised training framework is proposed with 3D hierarchical refinement and augmentation using explicit 3D geometry. In this framework, the depth and pose estimations are hierarchically and mutually coupled to refine the estimated pose layer by layer. The intermediate view image is proposed and synthesized by warping the pixels in an image with the estimated depth and coarse pose. Then, the residual pose transformation can be estimated from the new view image and the image of the adjacent frame to refine the coarse pose. The iterative refinement is implemented in a differentiable manner in this paper, making the whole framework optimized uniformly. Meanwhile, a new image augmentation method is proposed for the pose estimation by synthesizing a new view image, which creatively augments the pose in 3D space but gets a new augmented 2D image. The experiments on KITTI demonstrate that our depth estimation achieves state-of-the-art performance and even surpasses recent approaches that utilize other auxiliary tasks. Our visual odometry outperforms all recent unsupervised monocular learning-based methods and achieves competitive performance to the geometry-based method, ORB-SLAM2 with back-end optimization. Copyright © 2021, The Authors. All rights reserved.

关键词： Robots

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