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A soft manipulator for efficient delicate grasping in shallow water: Modeling, control, and real-world experiments

INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2021年第1期40卷 449-469页

作者： Gong, Zheyuan Fang, Xi Chen, Xingyu Cheng, Jiahui Xie, Zhexin Liu, Jiaqi Chen, Bohan Yang, Hui Kong, Shihan Hao, Yufei Wang, Tianmiao Yu, Junzhi Wen, Li Beihang Univ Sch Mech Engn & Automat 37 Xueyuan Rd Beijing 100191 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Beijing Peoples R China

Collecting in shallow water (water depth: similar to 30 m) is an emerging field that requires robotics for replacing human divers. Soft robots have several promising features (e.g., safe interaction with the environments, lightweight, etc.) for performing such tasks. In this article, we developed an underwater robotic system with a three-degree-of-freedom (3-DoF) soft manipulator for spatial delicate grasping in shallow water. First, we present the design and fabrication of the soft manipulator with an opposite-bending-and-stretching structure (OBSS). Then, we proposed a simple and efficient kinematics method for controlling the spatial location and trajectory of the soft manipulator's end effector. The inverse kinematics of the OBSS manipulator can be solved efficiently (computation time: 8.2 ms). According to this inverse kinematics method, we demonstrated that the OBSS soft manipulator could track complex two-dimensional and three-dimensional trajectories, including star, helix, etc. Further, we performed real-time closed-loop pick-and-place experiments of the manipulator with binocular and on-hand cameras in a lab aquarium. Hydrodynamic experiments showed that the OBSS soft manipulator produced little force (less than 0.459 N) and torque (less than 0.228 N center dot m), which suggested its low-inertia feature during the underwater operation. Finally, we demonstrated that the underwater robotic system with the OBSS soft manipulator successfully collected seafood animals at the bottom of the natural oceanic environment. The robot successfully collected eight sea echini and one sea cucumber within 20 minutes at a water depth of around 10 m.

关键词： Soft robotics underwater grasping inverse kinematics hydrodynamics

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Mode-Dependent Event-Triggered Fault Detection for Nonlinear Semi-Markov Jump systems With Quantization: Application to Robotic Manipulator

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IEEE ACCESS 2021年 9卷 21832-21842页

作者： Ji, Yidao Wang, Chenan Wu, Wei Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper is studied with fault detection issue for nonlinear semi-Markov jump systems. In particular, the mode-dependent mechanism of event-triggered transmission is developed for improving communication efficiency. Furthermore, the mode-dependent quantization method is utilized to cope with the limited network bandwidth. These strategies are chosen for more effective utilization of system mode information with less conservatism. Mode-dependent filter type fault detectors are constructed and sufficient conditions are established by Lyapunov-Krasovskii functionals, such that the filter error system can achieve the desired dissipative performance. In the end, the designed fault detection method is verified through a practical example of robotic manipulator.

关键词： Fault detection Quantization (signal) Manipulators Symmetric matrices Licenses Bandwidth Markov processes Mode-dependent quantization mode-dependent event-triggered fault detection semi-Markov jump system robotic manipulator

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A Comparison of Wearable Sensor Configuration Methods for Human Activity Recognition Using CNN 8

A Comparison of Wearable Sensor Configuration Methods for Hu...

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IEEE International Conference on Progress in Informatics and Computing (IEEE PIC)

作者： Tong, Lina Lin, Qianzhi Qin, Chuanlei Peng, Liang China Univ Min & Technol Being Sch Mech Elect & Informat Engn Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

ISBN: (纸本)9781665426558

The number and location configuration methods of wearable sensors for human activity recognition (HAR) are analytically discussed. Based on the publicly available Daily and Sports Activities data set, a convolutional neural network (CNN) was built to recognize 19 kinds of daily and sports activities, and then the model was optimized for better performance. The results of numerous comparative experiments show that deep learning-based HAR is better than machine learning-based HAR in terms of accuracy, and its improvement in accuracy is not directly related to the increase of sensor quantity. Due to its strong capability of feature extraction, deep learning extracts not only activity-related features but also individual differences, therefore, the location with less individual randomness should be selected according to practical engineering. Moreover, the results are also influenced by the limb symmetry in the data set. Finally, the feasibility of achieving higher accuracy with fewer sensors is proved.

关键词： convolutional neural network (CNN) human activity recognition (HAR) sensor configuration methods wearable sensors

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A Hierarchical Architecture for Multisymptom Assessment of Early Parkinson's Disease via Wearable Sensors

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL systems 2022年第4期14卷 1553-1563页

作者： Wang, Chen Peng, Liang Hou, Zeng-Guang Li, Yanfeng Tan, Ying Hao, Honglin Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci CAS Ctr Excellence Brain Sci & Intelligence Techno Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Med Sci Peking Union Med Coll Hosp Peking Union Med Coll Beijing 100730 Peoples R China

Parkinson's disease (PD) is the second most common neurodegenerative disorder and the heterogeneity of early PD leads to interrater and intrarater variability in observation based clinical assessment. Thus, objective monitoring of PD induced motor abnormalities has attracted significant attention to manage disease progression. Here, we proposed a hierarchical architecture to reliably detect abnormal characteristics and comprehensively quantify the multisymptom severity in patients with PD. A novel wearable device was designed to measure motor features in 15 PD patients and 15 age-matched healthy subjects, while performing five types of motor tasks. The abnormality classes of multimodal measurements were recognized by hidden Markov models (HMMs) in the first layer of the proposed architecture, aiming at motivating the evaluation of specific motor manifestations. Subsequently, in the second layer, three single symptom models differentiated PD motor characteristics from normal motion patterns and quantified the severity of cardinal PD symptoms in parallel. In order to further analyze the disease status, the multilevel severity quantification was fused in the third layer, where machine learning algorithms were adopted to develop a multisymptom severity score. The experimental results demonstrated that the quantification of three cardinal symptoms was highly accurate to distinguish PD patients from healthy controls. Furthermore, strong correlations were observed between the Unified PD Rating Scale (UPDRS) scores and the predicted subscores for tremor (R = 0.75, P = 1.40e - 3), bradykinesia (R = 0.71, P = 2.80e - 3), and coordination impairments (R = 0.69, P = 4.20e - 3), and the correlation coefficient can be enhanced to 0.88 (P = 1.26e - 5) based on the fusion schemes. In conclusion, the proposed assessment architecture holds great promise to push forward the in-home monitoring of clinical manifestations, thus enabling the self-assessment of disease progression.

关键词： Diseases Machine learning Hidden Markov models Accelerometers Monitoring Gyroscopes Parkinson's disease Wearable computing Sensor systems multilevel fusion multisymptom assessment Parkinson's disease (PD) wearable sensor system

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Autonomous Free-Floating Underwater Assembly control for an Underwater Biomimetic Vehicle-Manipulator System

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Bai, Xuejian Wang, Yu Lv, Jiaqi Cheng, Long Wang, Shuo Tan, Min Liaoning Univ Technol Jinzhou 121001 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Idriverplus Technol Co Ltd Beijing 100192 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China

In this article, an improved coordinated control method based on the tracking differentiator with a median filter and dynamic surface control was proposed for an autonomous free-floating underwater assembly manipulation. In addition, the disturbances caused by the manipulator's motion were considered and compensated by a feedforward controller of the manipulator. Furthermore, the inward counter-propagating sinusoidal waves of the biomimetic undulatory-finned propulsors were adopted and a fuzzy logic controller was designed to realize the transformation between the control parameters and the demanded propulsive forces. The stability of the proposed control scheme was proven by the Lyapunov theory. Finally, practical experiments verified that the control scheme can complete the autonomous free-floating underwater assembly manipulation task, which indicated the good feasibility and robustness of the proposed methods.

关键词： Propulsion Manipulators Biomimetics Assembly Vehicle dynamics Vectors Mathematical models Grasping Codes Hydrodynamics Autonomous free-floating control dynamic surface control (DSC) underwater assembly control underwater biomimetic vehicle-manipulator system (UBVMS)

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Stealthy False Data Injection Attacks against Extended Kalman Filter Detection in Power Grids

Stealthy False Data Injection Attacks against Extended Kalma...

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2021 International Conference on Information, Cybernetics, and Computational Social systems, ICCSS 2021

作者： Liu, Yifa Xue, Wenchao He, Shuping Cheng, Long Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100190 China Chinese Academy of Sciences Key Laboratory of System and Control Academy of Mathematics and System Science Beijing100190 China Anhui University School of Electrical Engineering and Automation Hefei230601 China

ISBN: (纸本)9781665402453

The power grid is a kind of national critical infrastructure directly affiliated to human daily life. Because of the vital functions and potentially significant losses, the power grid becomes an excellent target for many malicious attacks. Due to the special nonlinear measurements, many detection methods do not match the grid very well. The extended Kalman filter based detection is one of the few methods suitable for nonlinear system detection, and therefore can be used in power system to spot malicious attacks. However, the reliability and effectiveness of the extended Kalman filter based detection have not been fully studied and adequately guaranteed. By proposing a two-step false data injection attack strategy, this paper gives a stealthy way to inject false data of increasing magnitude into the power grid, which can eventually cause a certain degree of deviation of the grid state without being detected. In the simulation, the method proposed in this paper caused a voltage deviation of more than 25% before being discovered in the power system. © 2021 IEEE.

关键词： Extended Kalman filters

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A learning-based control approach for blind quadrupedal locomotion with guided-DRL and hierarchical-DRL

A learning-based control approach for blind quadrupedal loco...

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IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO)

作者： Ren, Liang Wang, Chunlei Yang, Ya Cao, Zhiqiang China Elect Technol Grp Corp Res Inst 21 Shanghai 200030 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781665405355

control parameters play an important role on the locomotion performance of quadruped robot system. In this paper, a learning-based control method is proposed, where the parameters of controller are learned by deep reinforcement learning (DRL). The proposed control system consists of a hierarchical controller and an agent, in which the agent learns the parameters in the upper layer of the controller. In the learning process, the guided-DRL and the hierarchical-DRL were used to solve the exploration problem and reward sparse problem, respectively. The former learns good expert trajectories with controller, which can realize the transition from supervised learning to self-learning, whereas the latter divides the task into a series of sub-goals to ensure that the difficulty of each subtask matches the decision-making ability of the agent in the learning stage. Finally, the controller with the trained policy is deployed to a real robot without manual tuning, and experimental results prove the effectiveness of the proposed method.

关键词： Quadruped robot robot locomotion deep reinforcement learning guided-DRL hierarchical-DRL

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Manifestations of 9754 gout patients in a Chinese clinical center: A 10-year observational study

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Revue du Rhumatisme (Edition Francaise) 2022年第1期89卷 65-71页

作者： Gao, Qianhui Cheng, Xiaoyu Merriman, Tony R. Wang, Can Cui, Lingling Zhang, Hui Sun, Wenyan Wang, Jing Wang, Feiyue Li, Changgui Lu, Jie Shandong Provincial Key Laboratory of Metabolic Diseases and Qingdao Key Laboratory of Gout Department of Endocrinology and Metabolism the Affiliated Hospital of Qingdao University Qingdao 266003 China Institute of Metabolic Diseases Qingdao University Qingdao 266003 China Department of Biochemistry University of Otago Dunedin New Zealand Division of Clinical Immunology and Rheumatology University of Alabama Birmingham Alabama United States Institute of Smart Healthcare Systems Qingdao Academy of Intelligent Industries Qingdao 266109 China The State Key Laboratory for Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Objectif: La prévalence de la goutte augmente, mais les études analysant ses caractéristiques cliniques dans de grands échantillons sont insuffisantes. Cette étude s'attache à clarifier les changements dans les manifestations cliniques de la goutte en Chine au cours des dix dernières années et à analyser les caractéristiques cliniques et les facteurs de risque de la goutte précoce. Méthodes: Les manifestations cliniques de la goutte ont été comparées chez 9754 patients reçus pour la première fois dans notre établissement entre 2008 et 2012 (premier groupe) ou entre 2013 et 2018 (dernier groupe). La goutte apparue ≤ 30 ans a été définie comme goutte précoce et > 30 ans, comme goutte tardive. Les caractéristiques cliniques et les facteurs de risque ont été comparés entre les groupes. Résultats: La goutte est apparue 4,14 ans plus tôt dans le dernier groupe (2013-2018, 5979 patients) et le pourcentage de goutte précoce (3827 patients) y était plus élevé. La durée de la maladie était significativement plus courte (5,72 ± 0,09 contre 6,01 ± 0,11, p < 0,05) et la proportion de patients présentant des tophi était plus faible dans le dernier groupe (20,0% contre 22,0%, p<0,05). Le dernier groupe comptait davantage de patients obèses (37,7% contre 32,6%, p < 0,001) et l'uricémie y était significativement plus élevée (485,5 ± 1,5 Μmol/l contre 465,0 ± 1,9 Μmol/l). Comme la goutte survenait à un âge plus jeune, nous avons appliqué une régression logistique multivariée et déterminé que des antécédents familiaux positifs, le sexe masculin, l'obésité, une uricémie élevée et la consommation de sodas sucrés étaient des facteurs prédictifs indépendants de goutte précoce. Conclusion: Les manifestations de la goutte en Chine ont évolué au cours des dix dernières années, avec notamment une tendance à une apparition plus précoce. © 2021

关键词： Apparition précoce Drop Early onset Facteurs de risque Goutte Risk factors

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Learning Representation with Q-irrelevance Abstraction for Reinforcement Learning 11

Learning Representation with Q-irrelevance Abstraction for R...

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11th International Conference on Intelligent control and Information Processing, ICICIP 2021

作者： Hao, Shuai Li, Luntong Liu, Minsong Zhu, Yuanheng Zhao, Dongbin Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China School of Software Beihang University Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665425155

In order to improve the performance of deep reinforcement learning (DRL) algorithm in high-dimensional observation environments, we propose a new auxiliary task to learn representations to aggregate task-relevant information of observations. Inspired by Q-irrelevance abstraction, our auxiliary task trains a deep Q-network (DQN) to predict the true Q value distribution over all discrete actions. Then we use the output of DQN to train the encoder to discriminate states with different Q values. The encoder is used as the representation of proximal policy optimization (PPO). The resulting algorithm is called as Q-irrelevance Abstraction for Reinforcement Learning (QIARL). After training, the encoder can aggregate states with similar Q value distributions together for any policy and any action. Thus the encoder can encode the important information that is relevant to reinforcement learning task. We test QIARL in four Procgen environments compare with PPO, A2C and Rainbow. The experimental results show QIARL outperforms the other three algorithms. © 2021 IEEE.

关键词： Reinforcement learning

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CSR: Cascade Conditional Variational Auto Encoder with Socially-aware Regression for Pedestrian Trajectory Prediction

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PATTERN RECOGNITION 2023年 133卷

作者： Zhou, Hao Ren, Dongchun Yang, Xu Fan, Mingyu Huang, Hai Harbin Engn Univ Natl Key Lab Sci & Technol Underwater Vehicle Harbin Peoples R China Meituan Res Ctr Autonomous Vehicles Beijing Peoples R China Wenzhou Univ Coll Comp Sci Wenzhou Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

Pedestrian trajectory prediction is a key technology in many real applications such as video surveillance, social robot navigation, and autonomous driving, and significant progress has been made in this research topic. However, there remain two limitations of previous studies. First, the losses of the last time steps are heavier weighted than that of the beginning time steps in the objective function at the learning stage, causing the prediction errors generated at the beginning to accumulate to large errors at the last time steps at the inference stage. Second, the prediction results of multiple pedestrians in the prediction horizon might be socially incompatible with the interactions modeled by past trajectories. To overcome these limitations, this work proposes a novel trajectory prediction method called CSR, which consists of a cascaded conditional variational autoencoder (CVAE) module and a socially-aware regression module. The CVAE module estimates the future trajectories in a cascaded sequential manner. Specifically, each CVAE concatenates the past trajectories and the predicted location points so far as the input and predicts the adjacent location at the following time step. The socially-aware regression module generates offsets from the estimated future trajectories to produce the corrected predictions, which are more reasonable and accurate than the estimated trajectories. Experiments results demonstrate that the proposed method exhibits significant improvements over state-of-the-art methods on the Stanford Drone Dataset (SDD) and the ETH/UCY dataset of approximately 38.0% and 22.2%, respectively. The code is available at https: //***/zhouhao94/CSR . (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Pedestrian trajectory prediction Socially -aware model Conditional variational autoencoder (CVAE)

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