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IEEE Conference on Decision and Control

作者： Biqiang Mu Hongsheng Qi Ian R. Petersen Guodong Shi Key Laboratory of Systems and Control Institute of Systems Science Chinese Academy of Sciences Beijing China Research School of Electrical Energy and Materials Engineering The Australian National University Canberra Australia Australian Center for Field Robotics School of Aerospace The University of Sydney NSW Australia

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

Hamiltonian identification plays a key role in learning the dynamics of closed quantum systems. A novel strategy for Hamiltonian identification is developed in the paper by applying repeated projective measurements with a fixed period. The corresponding measurement outcome sequence forms a time-homogeneous Markov chain, the transition matrix of which is a quadratic function of the Hamiltonian. As a result, the identification method is achieved by two steps: 1) learning the transition matrix of the observed Markov chains; 2) identifying the Hamiltonian using the function relating it to the transition matrix, which is transformed to a manifold optimization problem. A simulation example is provided to show the efficacy of the proposed method.

关键词： Markov processes Qubit Maximum likelihood estimation Tomography Optimization Eigenvalues and eigenfunctions Mathematical model

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Passive Quadrupedal Gait Synchronization for Extra Robotic Legs Using a Dynamically Coupled Double Rimless Wheel Model

Passive Quadrupedal Gait Synchronization for Extra Robotic L...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Daniel J. Gonzalez H. Harry Asada Robotics Research Center in the Department of Electrical Engineering and Computer Science United States Military Academy West Point NY USA d’Arbeloff Laboratory for Information Systems and Technology in the Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

The Extra Robotic Legs (XRL) system is a robotic augmentation worn by a human operator consisting of two articulated robot legs that walk with the operator and help bear a heavy backpack payload. It is desirable for the Human-XRL quadruped system to walk with the rear legs lead the front by 25% of the gait period, minimizing the energy lost from foot impacts while maximizing balance stability. Unlike quadrupedal robots, the XRL cannot command the human's limbs to coordinate quadrupedal locomotion. Using a pair of Rimless Wheel models, it is shown that the systems coupled with a spring and damper converge to the desired 25% phase difference. A Poincaré return map was generated using numerical simulation to examine the convergence properties to different coupler design parameters, and initial conditions. The Dynamically Coupled Double Rimless Wheel system was physically realized with a spring and dashpot chosen from the theoretical results, and initial experiments indicate that the desired synchronization properties may be achieved within several steps using this set of passive components alone.

关键词： Legged locomotion Wheels Synchronization Couplers Robot kinematics

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Continuity scaling: A rigorous framework for detecting and quantifying causality accurately

arXiv

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arXiv 2022年

作者： Ying, Xiong Leng, Si-Yang Ma, Huan-Fei Nie, Qing Lai, Ying-Cheng Lin, Wei School of Mathematical Sciences SCMS SCAM Fudan University Shanghai200433 China Research Institute for Intelligent Complex Systems CCSB LCNBI Fudan University Shanghai200433 China State Key Laboratory of Medical Neurobiology MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai200032 China Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China School of Mathematical Sciences Soochow University Suzhou215006 China Department of Mathematics Department of Developmental and Cell Biology NSF-Simons Center for Multiscale Cell Fate Research University of California IrvineCA92697-3875 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287-5706 United States Shanghai Artificial Intelligence Laboratory Shanghai200232 China

Data based detection and quantification of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms, which is consistent with the natural interpretation of causality. In particular, instead of measuring the smoothness of the cross map as conventionally implemented, we define causation through measuring the scaling law for the continuity of the investigated dynamical system directly. The uncovered scaling law enables accurate, reliable, and efficient detection of causation and assessment of its strength in general complex dynamical systems, outperforming those existing representative methods. The continuity scaling based framework is rigorously established and demonstrated using datasets from model complex systems and the real world. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

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Online collision avoidance for human-robot collaborative interaction concerning safety and efficient

TechRxiv

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TechRxiv 2020年

作者： Liu, Guoliang He, Haoyang Tian, Guohui Zhang, Jianhua Ji, Ze The School of Control Science and Engineering Shandong University Jinan250061 China School of Mechanical Engineering Hebei University of Technology Tianjin300131 China The Robotics and Autonomous Systems Laboratory School of Engineering Cardiff University CardiffCF10 3AT United Kingdom

With the development of robot technology and the arrival of industry 4.0 era, society pays more attention to collaboration and interaction between human and robots. However, safety is still main concern in the development of human-robot collaboration. In this paper, a novel real-time collision avoidance approach for mobile manipulator is proposed by considering the motion status of the human, which includes the relative minimum distance and velocity (both magnitude and direction) between the robot and the human. The distance and velocity of the human hand are first estimated online using a vision sensor, and then defined as danger factors in the potential function of the potential field. The novel potential function proposed in this paper considers not only the safety problem, but also the efficient problem, i.e., the manipulator can make smart control decision to avoid the collision according to the relative velocity in case of the cross over. To overcome the local minimum problem and choose a best motion direction, we propose a motion sampling mechanism for motion planning. For each sample, the robot calculates the potential function to evaluate the safety and efficiency, and chooses a direction which is best for avoidance. We finally demonstrate our idea on a real mobile manipulator platform in a simulated co-worker environment. © 2020, CC BY.

关键词： Collision avoidance

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Learning offline: Memory replay in biological and artificial reinforcement learning

arXiv

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arXiv 2021年

作者： Roscow, Emma L. Chua, Raymond Costa, Rui Ponte Jones, Matt W. Lepora, Nathan Centre de Recerca Matemàtica Bellaterra Spain McGill University and Mila Montréal Canada Bristol Computational Neuroscience Unit Intelligent Systems Lab Department of Computer Science University of Bristol United Kingdom School of Physiology Pharmacology and Neuroscience University of Bristol Bristol United Kingdom Department of Engineering Mathematics Bristol Robotics Laboratory University of Bristol Bristol United Kingdom

Learning to act in an environment to maximise rewards is among the brain’s key functions. This process has often been conceptualised within the framework of reinforcement learning, which has also gained prominence in machine learning and artificial intelligence (AI) as a way to optimise decision- making. A common aspect of both biological and machine reinforcement learning is the reactivation of previously experienced episodes, referred to as replay. Replay is important for memory consolidation in biological neural networks, and is key to stabilising learning in deep neural networks. Here, we review recent developments concerning the functional roles of replay in the fields of neuroscience and AI. Complementary progress suggests how replay might support learning processes, including generalisation and continual learning, affording opportunities to transfer knowledge across the two fields to advance the understanding of biological and artificial learning and memory. © 2021, CC BY-SA.

关键词： Reinforcement learning

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Adaptive safe distance prediction using MPC for bridge cranes considering anti-swing in dynamic environment

TechRxiv

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TechRxiv 2020年

作者： Chen, Huili Liu, Guoliang Tian, Guohui Zhang, Jianhua Ji, Ze The School of Control Science and Engineering Shandong University Jinan250061 China School of Mechanical Engineering Hebei University of Technology Tianjin300131 China The Robotics and Autonomous Systems Laboratory School of Engineering Cardiff University CardiffCF10 3AT United Kingdom

In dynamic environment, the suddenly appeared human or other moving obstacles can affect the safety of the bridge crane. For such dangerous situation, the bridge crane must predict potential collisions between the payload and the obstacle, keep safe distance while the swing of the payload must be considered in the mean time. Therefore, the safe distance is not a constant value, which must be adaptive to the relative speed of the bridge crane. However, as far as we know, the mathematical model between the safe distance and the relative speed of the bridge crane has never been fully discussed. In this paper, we propose a safe distance prediction method using model prediction control (MPC), which can make sure that the crane can stop before the obstacle, and avoid possible collisions, while the relative speed and anti-swing are both considered. The experimental results prove the effectiveness of our idea. © 2020, CC BY.

关键词： Forecasting

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Distributedly Solving Boolean Equations over Networks

Distributedly Solving Boolean Equations over Networks

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IEEE Conference on Decision and Control

作者： Hongsheng Qi Bo Li Rui-Juan Jing Alexandre Proutiere Guodong Shi Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Faculty of Science Jiangsu University Zhenjiang China KTH Royal Institute of Technology Stockholm Sweden Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney NSW Australia

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

In this paper, we propose distributed algorithms that solve a system of Boolean equations over a network, where each node in the network possesses only one Boolean equation from the system. The Boolean equation assigned at any particular node is a private equation known to this node only, and the nodes aim to compute the exact set of solutions to the system without exchanging their local equations. We show that each private Boolean equation can be locally lifted to a linear algebraic equation under a basis of Boolean vectors, leading to a network linear equation that is distributedly solvable. A number of exact or approximate solutions to the induced linear equation are then computed at each node from different initial values. The solutions to the original Boolean equations are eventually computed locally via a Boolean vector search algorithm. We prove that given solvable Boolean equations, when the initial values of the nodes for the distributed linear equation solving step are i.i.d selected according to a uniform distribution in a high-dimensional cube, our algorithms return the exact solution set of the Boolean equations at each node with high probability.

关键词： Signal processing algorithms Heuristic algorithms Consensus algorithm Distributed algorithms Approximation algorithms Signal processing Convergence

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A Sextuple Cross-Coupled SRAM Cell Protected against Double-Node Upsets

A Sextuple Cross-Coupled SRAM Cell Protected against Double-...

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Asian Test Symposium (ATS)

作者： Aibin Yan Yan Chen Jun Zhou Jie Cui Tianming Ni Xiaoqing Wen Patrick Girard School of Computer Science and Technology Anhui University Hefei China College of Electrical Engineering Anhui Polytechnic University Wuhu China Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Laboratory of Informatics Robotics and Microelectronics of Montpellier University of Montpellier / CNRS Montpellier France

ISBN: (数字)9781728174679

ISBN: (纸本)9781728174686

In this paper, we propose a sextuple cross-coupled SRAM cell, namely SCCS18T, protected against double-node upsets. Since the proposed SCCS18T cell forms a large feedback loop for value retention and error interception, the cell can provide self-recoverability from any single-node upsets (SNUs) and partial double-node upsets (DNUs). Moreover, the proposed cell has optimized operation speed due to the use of six access transistors. Simulation results show that the SCCS18T cell can save approximately 65% read access time at the cost of 49% power dissipation and 50% silicon area on average, compared with typical hardened SRAM cells.

关键词： Transistors SRAM cells Simulation Radiation hardening (electronics) Power dissipation Latches Integrated circuit reliability

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Kalman filtering technique for attitude estimation on SO(3) using single inertial vector observations

Kalman filtering technique for attitude estimation on SO(3) ...

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OCEANS

作者： Joel Reis Pedro Batista Paulo Oliveira Carlos Silvestre Faculty of Science and Technology University of Macau Taipa Macao China Institute for Systems and Robotics LARSyS Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal LAETA - Associated Laboratory for Energy Transports and Aeronautics IDMEC - Institute of Mechanical Engineering Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Instituto Superior Técnico Universidade de Lisboa Portugal

ISBN: (纸本)9781665427883

This paper presents an attitude estimation methodology for robotic platforms equipped with a set of rate gyros that can implicitly measure the Earth’s rotation rate. As is well known, standard strategies for attitude determination rely on explicit body-fixed measurements of at least two inertial reference vectors. In this work, the Earth’s spin is regarded as one of two constant inertial reference vectors. However, because that spin is only implicitly measured, we first implement a Kalman filter to obtain an estimate of it. This filter also yields: unbiased estimates of the second inertial reference vector, which is explicitly measured in the body-fixed frame; and estimates of two biases related to the sensor measurements. The platform’s rotation matrix is then computed using a Kalman-based observer evolving on the manifold $SO(3)$ that is driven by the output of the first filter. Realistic simulation results are presented to showcase the performance of the proposed technique in two different scenarios.

关键词： Manifolds Simulation Sea measurements Estimation Observers Robot sensing systems Gyroscopes

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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