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IEEE International Conference on Human-Machine systems

作者： Hang Su Chenguang Yang Jiehao Li Yiming Jiang Giancarlo Ferrigno Elena De Momi Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Bristol Robotics Laboratory University of the West of England Bristol United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy

ISBN: (数字)9781728158716

ISBN: (纸本)9781728158723

Flexibility and robustness have become key points in the development of surgical robot controller for physical interactions. However, the conventional impedance control schemes unaware of the actual surgical scenario, including complex physical interaction on the robot arm, lead to the loss of accuracy. In this paper, a hierarchical task impedance control scheme is proposed for Minimally Invasive Surgery (MIS) based on an operational space formulation of a 7 DoFs redundant robot. Its redundancy is exploited to guarantee a remote center of motion (RCM) constraint and to provide a flexible workspace for the medical staff to assist physicians. In addition to the achievement of the classical whole-body impedance control, the issue of uncertain disturbances will be addressed by a decoupled adaptive approximation based on a radial basis function neural network (RBFNN) within the control framework. Task performances under the hierarchical task impedance controller were validated and compared with previous work in the literature. Experimental results showed its improved performance in terms of positional error and RCM constraint, regardless of the existing uncertain physical interaction.

关键词： Task analysis Impedance Manipulators Tools Surgery Jacobian matrices

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NeRF in robotics: A Survey

arXiv

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arXiv 2024年

作者： Wang, Guangming Pan, Lei Peng, Songyou Liu, Shaohui Xu, Chenfeng Miao, Yanzi Zhan, Wei Tomizuka, Masayoshi Pollefeys, Marc Wang, Hesheng The University of Cambridge ETH Zurich Switzerland The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education of China China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China ETH Zurich Zurich Switzerland Mechanical Systems Control Laboratory University of California Berkeley United States

Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields. The recent emergence of neural implicit representations has introduced radical innovation to this field as implicit representations enable numerous capabilities. Among these, the Neural Radiance Field (NeRF) has sparked a trend because of the huge representational advantages, such as simplified mathematical models, compact environment storage, and continuous scene representations. Apart from computer vision, NeRF has also shown tremendous potential in the field of robotics. Thus, we create this survey to provide a comprehensive understanding of NeRF in the field of robotics. By exploring the advantages and limitations of NeRF, as well as its current applications and future potential, we hope to shed light on this promising area of research. Our survey is divided into two main sections: The Application of NeRF in robotics and The Advance of NeRF in robotics, from the perspective of how NeRF enters the field of robotics. In the first section, we introduce and analyze some works that have been or could be used in the field of robotics from the perception and interaction perspectives. In the second section, we show some works related to improving NeRF's own properties, which are essential for deploying NeRF in the field of robotics. In the discussion section of the review, we summarize the existing challenges and provide some valuable future research directions for reference. © 2024, CC BY-NC-ND.

关键词： robotics

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Enhancing student learning: On-line interactive laboratory for modelling of real world control system applications

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IFAC Proceedings Volumes 2011年第1期44卷 7268-7273页

作者： Joshué Pérez Sebastián Dormido Ljubo Vlacic Centre of Automation and Robotics (CAR) University Polytechnic of Madrid (UPM) – Spanish National Research Council (CSIC) Madrid Spain Dep. Informática y Automática Escuela Técnica Superior de Ingeniería Informática Universidad Nacional de Educación a Distancia Madrid Spain Intelligent Control Systems Laboratory Griffith University Brisbane Australia

Abstract Teaching an introductory course on control systems to undergraduate students often seeks elaborations on real engineering world control system applications to assist students in grasping theoretical concepts. In this paper, the newly developed animated and interactive control systems tutorials are presented. The paper shows that this teaching approach helps students in bridging the gap between the classical control techniques and the real world applications. The course evaluations undertaken since the time of the inclusion of the animated tutorials into the teaching process clearly show the learning improvement gained, reflected in increased average scores, and reduced course fail grade.

关键词： Interactive control system tutorials Interactive teaching tools Animation Distance Education

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Design and implementation of FPGA-based hardware acceleration system for target detection

Design and implementation of FPGA-based hardware acceleratio...

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Chinese Automation Congress (CAC)

作者： Canyu Zhan Bingjie Zhao Huangliang Zhang Zhongpeng Yan Jie Wu Da Yuan Shenyang University of Chemical Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China Shenyang University of Technology Shenyang China

Since the application of target detection in convolutional neural networks is becoming more and more widespread, and the computational volume is increasing, the complexity of the network hierarchy and the memory requirements are gradually increasing, so according to the Field Programmable Gate Array (FPGA) parallel computing characteristics, using FPGA to accelerate the convolutional neural network hardware, can design a CPU+FPGA target detection hardware acceleration system. The system adopts the YOLOv2 target detection algorithm and divides the tasks according to the network structure of the algorithm, and at the same time, it quantifies the 32-bit floating-point numbers of the algorithm with 16-bit fixed points, optimizes the convolutional, pooling, and reordering layers of the algorithm, and finally designs the algorithm by using the HLS high-level synthesis. The system uses PYNQ-Z2 platform to implement the convolutional neural network YOLOv2, and the final system performance reaches 27.06GOPs (Giga Operations Per Second, 1 billion operations/s), and the power consumption is only 2.609W, compared to CPU (E5-2620V4), its performance is 6.58 times that of CPU, and the power consumption is only 3% of the CPU, which has certain advantages compared with current target detection systems.

关键词： Power demand System performance Software algorithms Object detection Software System-on-chip Convolutional neural networks

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Identifiability Analysis of Noise Covariances for LTI Stochastic systems with Unknown Inputs

arXiv

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arXiv 2022年

作者： Kong, He Sukkarieh, Salah Arnold, Travis J. Chen, Tianshi Mu, Biqiang Zheng, Wei Xing Shenzhen518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities SUSTech Shenzhen518055 China Sydney Institute for Robotics and Intelligent Systems The University of Sydney NSW2006 Australia Madison WI53704 United States School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Computer Data and Mathematical Sciences Western Sydney University SydneyNSW2751 Australia

Most existing works on optimal filtering of linear time-invariant (LTI) stochastic systems with arbitrary unknown inputs assume perfect knowledge of the covariances of the noises in the filter design. This is impractical and raises the question of whether and under what conditions one can identify the process and measurement noise covariances (denoted as Q and R, respectively) of systems with unknown inputs. This paper considers the identifiability of Q/R using the correlation-based measurement difference approach. More specifically, we establish (i) necessary conditions under which Q and R can be uniquely jointly identified;(ii) necessary and sufficient conditions under which Q can be uniquely identified, when R is known;(iii) necessary conditions under which R can be uniquely identified, when Q is known. It will also be shown that for achieving the results mentioned above, the measurement difference approach requires some decoupling conditions for constructing a stationary time series, which are proved to be sufficient for the well-known strong detectability requirements established by Hautus. © 2022, CC BY.

关键词： Stochastic systems

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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Research on fault feature extraction of rotating machinery based on adaptive VMD and optimized CYCBD

Research on fault feature extraction of rotating machinery b...

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Chinese control and Decision Conference, CCDC

作者： Ran Xie Ling Xiong Binbin Dan Zeyu Ren Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China Key Laboratory of Metallurgical Equipment and Control of Ministry of Education Wuhan China School of Physics and Electronic Science Hunan Institute of Science and Technology Yueyang China

ISBN: (纸本)9781665478977

To solve the problem that early fault features of rotating machinery are difficult to extract, an adaptive k-value hierarchical variational mode decomposition (H-VMD) combined with optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) fault feature extraction method is proposed in this paper. Mode decomposition of the vibration signal is performed with H-VMD. and then the noise dominant component is denoised by wavelet threshold denoising (WTD). Furthermore, the improved autocorrelation-weighted correlated kurtosis (ACK) when CYCBD enhances the periodic shock component of the denoised signal is the fitness function of ChOA, and the envelope demodulation analysis of the feature-enhanced signal is performed using the teager energy operator (TEO). Simulation analysis and experimental results show that the interference of background noise can be effectively removed and the periodic shock component of the vibration signal be enhanced by the proposed method, which is a new feature extraction method for the fault diagnosis of rotating machinery.

关键词： Vibrations Fault diagnosis Electric shock Noise reduction Interference Feature extraction Linear programming

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Introduction

Advances in Industrial Control

引用

Advances in Industrial control 2018年第9783319610207期 1-18页

作者： Orsag, Matko Korpela, Christopher Oh, Paul Bogdan, Stjepan Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Electrical Engineering and Computer Science United States Military Academy West PointNY United States Department of Mechanical Engineering University of Nevada Las Vegas Las VegasNV United States

Aerial robotics has reached significant maturity in the last few years. Thus, the number of new unmanned aerial vehicles (UAV) and unmanned aerial systems (UAS), applications, and companies producing them has increased in a very important way. The number of publications and presentations in conferences has also experienced a large increase. Moreover, almost every day we can find articles, videos, and news in the general media related to drones. © Springer International Publishing AG 2018.

关键词： Antennas

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A registration-aided domain adaptation network for 3D Point cloud based place recognition

arXiv

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arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

引用

Annual Conference of Industrial Electronics Society

作者： Haibo Cheng Yunpeng He Peng Zeng Shichao Li Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction.

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