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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Li, Guotao Cheng, Long Gao, Zhengrun Lu, Jilong Zhao, Yanjiang Institute of Automation State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China Fudan University The National Clinical Research Center for Aging and Medicine Shanghai China Harbin University of Science and Technology Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education Harbin150080 China Intelligent Machine Institute Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9781665465366

Hand paralysis caused by stroke, spinal cord injury, or neurological trauma has a significant impact on the independence and quality of life of the patients. The hand exoskeleton can provide hand assistance and improve the patient's grasp. In this article, a novel wearable flexible hand exoskeleton is proposed to assist users in activities of daily life. To create a simple and lightweight assistive hand exoskeleton, 3D printing is used to manufacture most of the components, and a remote drive system is designed to reduce the weight of the hand exoskeleton. A ball pair device and a slider device are used to ensure the freedom of the thumb and the adaptability of the hand exoskeleton to different hand sizes. Finally, the experiments are conducted to verify that the hand exoskeleton can achieve motion assistance. © 2022 IEEE.

关键词： 3D printing

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Notice of Retraction: Cascade Network with Boundary-Adaptive Filter for Surgical Gesture Recognition

Notice of Retraction: Cascade Network with Boundary-Adaptive...

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2021 International Joint Conference on Neural Networks, IJCNN 2021

作者： Chen, Hua-Bin Ni, Zhen-Liang Li, Zhen Bian, Gui-Bin Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9780738133669

Automatically recognizing surgical gestures plays a key role in computer-assisted surgery and automatic skill assessment. Solutions to this task that only rely on video without additional sensor hardware have attracted great attention since they are economical and feasible. However, it is still a challenging problem due to the variable gesture durations and the oversegmentation problem that occurred in a long gesture duration. To address the above problems, we propose a cascade network with boundary-adaptive filter to dynamically capture the long-range temporal dependencies and reduce the over-segmentation errors obviously. A cascade module is designed to enhance the temporal modeling abilities in different timescales, in which multiple similar temporal convolution networks are divided into cascade stages and a fusion stage. Every cascade stage with different temporal receptive fields has a frame-specific weight to adjust the distribution of each stage and they will be aggregated into the fusion stage to refine the predictions. The frame-specific model architecture may incur a more serious over-segmentation problem. A boundary-adaptive filter is designed to leverage gesture boundary information to smooth the prediction results adaptively, which maintains consistency within a gesture duration while keeping the gesture-crossing discrepancy. We evaluate our model on the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). The proposed method reaches the state-of-the-art frame-wise accuracy (83.79%) and comparable segment-level performance (83.35% F1@50). © 2021 IEEE.

关键词： Gesture recognition

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Modeling and analysis of an underwater biomimetic vehicle-manipulator system

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Science China(Information Sciences) 2022年第3期65卷 255-257页

作者： Xuejian BAI Yu WANG Shuo WANG Rui WANG Min TAN Wei WANG School of Artificial Intelligence University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology School of Control Science and Engineering Shandong University

In recent years, underwater vehicle-manipulator systems(UVMSs) have captured researchers' attention. Inspired by fish, bionic mechanisms were introduced into a *** underwater biomimetic vehicle-manipulator system(UBVMS) is actuated by two symmetrically arranged biomimetic undulatory fin propulsors [1]. Based on the UBVMS, some topics have been researched, such as freefloating autonomous operation [2, 3], three-dimensional helical path following [4], and underwater video processing [5].

关键词： underwater biomimetic vehicle underwater robots underwater vehicle-manipulator system biomimetic robots hydrodynamic modeling

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Autonomous Mobile Robot Navigation using Adaptive Neuro Fuzzy Inference System

Autonomous Mobile Robot Navigation using Adaptive Neuro Fuzz...

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2022 International Conference on Innovations and Development of Information Technologies and Robotics, IDITR 2022

作者： Haider, Muhammad Husnain Ali, Hub Khan, Abdullah Aman Zheng, Hao Bhutta, M. Usman Maqbool Usman, Shaban Zhi, Pengpeng Wang, Zhonglai University of Electronic Science and Technology of China Chengdu China Yangtze Delta Region Institute Huzhou China Casia State Key Laboratory for Management & Control of Complex Systems Beijing China Sichuan Artificial Intelligence Research Institute Yibin China The Chinese University of Hong Kong Robotics Lab Mae Hong Kong

ISBN: (纸本)9781665409322

Navigation of autonomous robots in unknown and cluttered environments lies among the marked trends in robotics. Unlike animals and humans, the collision-free movement of a robot is challenging and requires processing complex information. An autonomous robot needs to cope with a large amount of uncertainty while navigating. The previous methods have limitations, such as lacking obstacle avoidance behaviour, having a large number of governing rules, designing a separate controller for each navigation and obstacle avoidance, not considering the robot's dynamics, computationally expensive training, and poor performance in a cluttered environment. This paper proposes a method that comprises a single adaptive neuro fuzzy inference system (ANFIS) based controller with 16 rules compared to hundred of rules used by previous methods to address such problems. Our method takes heading angle along with distance sensors data as input. AU the inputs are fuzzified into linguistic variables such as near-far and left-right. Additionally, a fuzzy inference system (FIS) is designed and trained using the generated dataset for optimum performance of ANFIS. The proposed method efficiently provides collision-free navigation of the mobile robot in densely cluttered environments. Comprehensive experiments are performed to prove the robustness and potency of the proposed ANFIS controller. Moreover, the performance of the proposed method is compared with various previous methods. The results of these comparisons indicate our proposed method's superiority in finding a near-optimal path. © 2022 IEEE.

关键词： Navigation

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Parallel medical imaging: An ACP-based approach for intelligent medical image recognition with small samples 1

Parallel medical imaging: An ACP-based approach for intellig...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Shen, Tianyu Gou, Chao Wang, Jiangong Huang, Jun He, Yonglan Xue, Huadan Jin, Zhengyu Wang, Fei-Yue Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China Shenzhen Campus of Sun Yat-sen University Sun Yat-sen University Guangzhou China Peking Union Medical College Hospital Chinese Academy of Medical Sciences Peking Union Medical College Department of Radiology Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China

ISBN: (纸本)9781665433372

The deep learning methods trained with large-scale manually annotated datasets have led to significant breakthroughs in medical image community. However, obtaining such datasets remains a challenging work in medical domain. In this paper, we propose an ACP-based approach named parallel medical imaging (PMI) for analyzing and addressing the small sample problem of medical image recognition. Firstly, we present the basic framework and key techniques of PMI, in which the Artificial imaging systems are constructed to effectively augment the 'medical small sample', the Computational experiments are carried out for designing and evaluating the visual models trained with small samples, and the Parallel execution is conducted to achieve closed-loop interaction and optimization in the domain-knowledge guidance. Furthermore, we illustrate a preliminary implementation and application of PMI approach through the case study of mammogram analysis. © 2021 IEEE.

关键词： Medical imaging

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Accident prediction in mesoscopic view: A CPSS-based social transportation approach 1

Accident prediction in mesoscopic view: A CPSS-based social ...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Sun, Chen Tan, Ruichen Deng, Jie Zhou, Rui Chen, Long Wang, Fei-Yue Cao, Dongpu University of Waterloo Dept. of MME Waterloo Canada Waytous Inc. Qingdao China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China

ISBN: (纸本)9781665433372

In modern society, traffic accidents are becoming an essential social safety issue that cannot be ignored. Along with the convenience of the high-speed development of modernization, the prosperity of vehicles has also led to severe traffic accidents, which cause massive loss of lives and properties every year. Accident analysis and prediction based on big data have become increasingly important in social transportation. However, the traditional data analysis and prediction methods only focused on either macroscope(transportation) or microscope(vehicle), and the relationships between the two layers are missing. This paper proposes a novel analysis framework by introducing the mesoscopic factors to bridge the transportation and vehicle operation domain under the parallel driving setting. The feature aggregation and mesoscopic factors extraction are included in the proposed prediction strategy to improve the performance of various commonly used decision tree-based classifiers. Methods are compared under the most recent 5-year accident database, which stores over three million records. Insights are discussed based on the future application of connected autonomous vehicles and intelligent road infrastructures. © 2021 IEEE.

关键词： Forecasting

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Line-of-sight based three-dimensional path following control for an underactuated robotic dolphin

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Science China(Information Sciences) 2021年第1期64卷 215-226页

作者： Jincun LIU Zhenna LIU Junzhi YU State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering ScienceBIC-ESATCollege of EngineeringPeking University Department of Electrical and Automation Shandong Labor Vocational and Technical College State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

This paper investigates the three-dimensional(3-D) path following control problem for an underactuated robotic dolphin. With a comprehensive consideration of the mechanical constraint and swimming principle of the robotic dolphin, a decoupling motion strategy is proposed to produce yaw and pitch maneuvers simultaneously. Then, kinematics and dynamics models for 3-D dolphin-like swimming are established,followed by simulations of the path following control. Furthermore, a novel lookahead based 3-D line-ofsight(LOS) guidance law is developed and implemented to obtain desired attitude angles with its simplicity,intuitiveness, and small computational footprint. Finally, simulation results illustrate the feasibility and effectiveness of the proposed path following control methods.

关键词： robotic dolphin path following 3-D LOS guidance law decoupling motion

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A Spatial-Temporal Attention Model for Human Trajectory Prediction

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 965-974页

作者： Xiaodong Zhao Yaran Chen Jin Guo Dongbin Zhao the School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083 the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing 100083China

Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory(LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention(ST-Attention) model,which studies spatial and temporal affinities jointly. Specifically,we introduce an attention mechanism to extract temporal affinity,learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.

关键词： Attention mechanism long-short term memory(LSTM) spatial-temporal model trajectory prediction

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DSNRNet: A Network of LiDAR Data Denoising by Differential Stability in Underground Mine

DSNRNet: A Network of LiDAR Data Denoising by Differential S...

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International Conference on Intelligent Transportation

作者： Dejiang Meng Bin Tian Ziyu Pan Junhui Wang Long Chen The Waytous Inc. Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The School of Data and Computer Science Sun Yat-sen University China

Some sprinklers in underground mines spray water mist to lessen the amount of dust in the air. But water mist causes LiDAR to provide inaccurate point measurements, which are referred to as water mist noise that undermines the effectiveness of LiDAR-based localization and object recognition. Therefore, in order to reduce water mist noise, we have developed a new noise segmentation network that can operate on a CPU in real time-differential stability noise removal network (DSNRNet). This network consists of two sub-networks. The first sub-network is aimed at extracting differential stability features, so we named it the differential stability feature extraction network (sub-network 1). The second sub-network: a fully connected neural network (sub-network 2), is used to segment noise. To evaluate the DSNRNet's performance, we built a LiDAR semantic segmentation dataset of underground mines and run the DSNRNet in an Intel i7-11800H CPU. The experimental results demonstrate that this method is able to strike a better balance between speed (26.3 milliseconds) and accuracy (97.8 % ) compared with the other two most possible methods-DSOR (24.2 milliseconds, 3.2 % ) and WeatherNet (436.8ms, 98.5 % ).

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Investigating the dynamic memory effect of human drivers via ON-LSTM

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Science China(Information Sciences) 2020年第9期63卷 83-93页

作者： Shengzhe DAI Zhiheng LI Li LI Dongpu CAO Xingyuan DAI Yilun LIN Department of Automation Tsinghua University Shenzhen International Graduate School Tsinghua University Department of Mechanical and Mechatronics Engineering University of Waterloo State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

It is a widely accepted view that considering the memory effects of historical information(driving operations) is beneficial for vehicle trajectory prediction models to improve prediction accuracy. However,many commonly used models(e.g., long short-term memory, LSTM) can only implicitly simulate memory effects, but lack effective mechanisms to capture memory effects from sequence data and estimate their effective time range(ETR). This shortage makes it hard to dynamically configure the most suitable length of used historical information according to the current driving behavior, which harms the good understanding of vehicle motion. To address this problem, we propose a modified trajectory prediction model based on ordered neuron LSTM(ON-LSTM). We demonstrate the feasibility of ETR estimation based on ON-LSTM and propose an ETR estimation method. We estimate the ETR of driving fluctuations and lane change operations on the NGSIM I-80 dataset. The experiment results prove that the proposed method can well capture the memory effects during trajectory prediction. Moreover, the estimated ETR values are in agreement with our intuitions.

关键词： driving behavior memory effect trajectory prediction historical information ON-LSTM

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