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Single-step precision programming of decoupled multiresponsive soft millirobots

Proceedings of the National Academy of Sciences of the United states of America 2024年第13期121卷 e2320386121页

作者： Zheng, Zhiqiang Han, Jie Shi, Qing Demir, Sinan Ozgun Jiang, Weitao Sitti, Metin Physical Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart 70569 Germany State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Xi’an 710054 China School of Mechanical Engineering Xi’an Jiaotong University Xi’an 710054 China Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing 100081 China Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology) Ministry of Education Beijing 100081 China Institute for Biomedical Engineering ETH Zurich Zurich 8092 Switzerland School of Medicine College of Engineering Koç University Istanbul 34450 Turkey

Stimuli-responsive soft robots offer new capabilities for the fields of medical and rehabilitation robotics, artificial intelligence, and soft electronics. Precisely programming the shape morphing and decoupling the multiresponsiveness of such robots is crucial to enable them with ample degrees of freedom and multifunctionality, while ensuring high fabrication accuracy. However, current designs featuring coupled multiresponsiveness or intricate assembly processes face limitations in executing complex transformations and suffer from a lack of precision. Therefore, we propose a one-stepped strategy to program multistep shape-morphing soft millirobots (MSSMs) in response to decoupled environmental stimuli. Our approach involves employing a multilayered elastomer and laser scanning technology to selectively process the structure of MSSMs, achieving a minimum machining precision of 30 µm. The resulting MSSMs are capable of imitating the shape morphing of plants and hand gestures and resemble kirigami, pop-up, and bistable structures. The decoupled multistimuli responsiveness of the MSSMs allows them to conduct shape morphing during locomotion, perform logic circuit control, and remotely repair circuits in response to humidity, temperature, and magnetic field. This strategy presents a paradigm for the effective design and fabrication of untethered soft miniature robots with physical intelligence, advancing the decoupled multiresponsive materials through modular tailoring of robotic body structures and properties to suit specific applications. Copyright © 2024 the Author(s).

关键词： environmental response miniature robot soft electronics soft robotics

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An improved knowledge graph model based on fuzzy theory and TransR 9

An improved knowledge graph model based on fuzzy theory and ...

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9th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2020

作者： Cai, Yingkai Wang, Chu Wang, Zhongfeng Song, Chunhe Zou, Yunfeng State Grid Liaoning Marketing Service Center Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China State Grid Jiangsu Marketing Service Nanjing China

Thispaper relates to the field of knowledge management and information retrieval technology, and specifically provides a method for constructing a knowledge graph. The method for constructing the knowledge graph includes: acquiring triple data, converting the triple data into a normalized triple vector, using the triple vector to construct a knowledge graph based on fuzzy relations, and The minimization objective optimization function of the triple vector is acquired, and the knowledge graph is optimized. This paper reduces the complexity of the knowledge map training process and the time required for the training process. © 2020 IEEE.

关键词： Knowledge graph

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Exploring Event-based Human Pose Estimation with 3D Event Representations

arXiv

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arXiv 2023年

作者： Yin, Xiaoting Shi, Hao Chen, Jiaan Wang, Ze Ye, Yaozu Yang, Kailun Wang, Kaiwei State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China School of Robotics Hunan University Changsha410012 China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Shanghai SUPREMIND Technology Company Ltd. Shanghai201210 China

Human pose estimation is a fundamental and appealing task in computer vision. Although traditional cameras are commonly applied, their reliability decreases in scenarios under high dynamic range or heavy motion blur, where event cameras offer a robust solution. Predominant event-based methods accumulate events into frames, ignoring the asynchronous and high temporal resolution that is crucial for distinguishing distinct actions. To address this issue and to unlock the 3D potential of event information, we introduce two 3D event representations: the Rasterized Event Point Cloud (RasEPC) and the Decoupled Event Voxel (DEV). The RasEPC aggregates events within concise temporal slices at identical positions, preserving their 3D attributes along with statistical information, thereby significantly reducing memory and computational demands. Meanwhile, the DEV representation discretizes events into voxels and projects them across three orthogonal planes, utilizing decoupled event attention to retrieve 3D cues from the 2D planes. Furthermore, we develop and release EV-3DPW, a synthetic event-based dataset crafted to facilitate training and quantitative analysis in outdoor scenes. Our methods are tested on the DHP19 public dataset, MMHPSD dataset, and our EV-3DPW dataset, with further qualitative validation via a derived driving scene dataset EV–JAAD and an outdoor collection vehicle. Our code and dataset have been made publicly available at https://***/MasterHow/EventPointPose. Copyright © 2023, The Authors. All rights reserved.

关键词： 3D modeling

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FocusFlow: Boosting key-Points Optical Flow Estimation for Autonomous Driving

arXiv

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arXiv 2023年

作者： Yi, Zhonghua Shi, Hao Yang, Kailun Jiang, Qi Ye, Yaozu Wang, Ze Ni, Huajian Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics Hunan University Changsha410012 China The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Shanghai SUPREMIND Technology Company Ltd. Shanghai201210 China

key-point-based scene understanding is fundamental for autonomous driving applications. At the same time, optical flow plays an important role in many vision tasks. However, due to the implicit bias of equal attention on all points, classic data-driven optical flow estimation methods yield less satisfactory performance on key points, limiting their implementations in key-point-critical safety-relevant scenarios. To address these issues, we introduce a points-based modeling method that requires the model to learn key-point-related priors explicitly. Based on the modeling method, we present FocusFlow, a framework consisting of 1) a mix loss function combined with a classic photometric loss function and our proposed Conditional Point control Loss (CPCL) function for diverse point-wise supervision;2) a conditioned controlling model which substitutes the conventional feature encoder by our proposed Condition control Encoder (CCE). CCE incorporates a Frame Feature Encoder (FFE) that extracts features from frames, a Condition Feature Encoder (CFE) that learns to control the feature extraction behavior of FFE from input masks containing information of key points, and fusion modules that transfer the controlling information between FFE and CFE. Our FocusFlow framework shows outstanding performance with up to +44.5% precision improvement on various key points such as ORB, SIFT, and even learning-based SiLK, along with exceptional scalability for most existing data-driven optical flow methods like PWC-Net, RAFT, and FlowFormer. Notably, FocusFlow yields competitive or superior performances rivaling the original models on the whole frame. The source code will be available at https://***/ZhonghuaYi/FocusFlow_official. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Analysis of Multi-ray Propagation for DSRC Communication on Street with RISs on Sidewalls

Analysis of Multi-ray Propagation for DSRC Communication on ...

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National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS)

作者： Guilu Wu State Key Laboratory of Millimeter Waves Southeast University Nanjing China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China School of Internet of Things Engineering Jiangnan University Wuxi China

ISBN: (纸本)9781665410021

Each element in the Reconfigurable Intelligent Sur-faces (RISs) can independently change the phase (or/and) amplitude of the incident signal, so the RISs can be used to enable users to better receive the signal transmitted by the transmitter. In this paper, a Multi-ray propagation model for RISs-assisted dedicated short-range communication (DSRC) system is presented and analyzed on street. The 3-D geometry and image techniques in ray tracing are combined to build the proposed model. This method could enhance the ray-tracing algorithm in RISs-assisted communication networks. In addition, the envelope strength of the received signal is analyzed and verified in our experiments by regulating phase of RISs in two cases. Different reflection times on transmitted signal wave bring different envelope strength of the received signal. The simulation results show that different reflection times and adjustment angles directly affect the received signal strength.

关键词： Geometry Solid modeling Analytical models Transmitters Simulation Ray tracing Reflection

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Joint moment estimation for hip exoskeleton control: A generalized moment feature generation method

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Biomimetic Intelligence and robotics 2025年

作者： Yuanwen Zhang Jingfeng Xiong Haolan Xian Chuheng Chen Xinxing Chen Haipeng Liang Chenglong Fu Yuquan Leng State Key Laboratory of Robotics and Systems School of Biomedical Engineering Harbin Institute of Technology Shenzhen 518055 China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen 518055 China School of Engineering and Materials Science Queen Mary University of London London E1 4NS UK

Hip joint moments during walking are the key foundation for hip exoskeleton assistance control. Most recent studies have shown estimating hip joint moments instantaneously offers a lot of advantages compared to generating assistive torque profiles based on gait estimation, such as simple sensor requirements and adaptability to variable walking speeds. However, existing joint moment estimation methods still suffer from a lack of personalization, leading to estimation accuracy degradation for new users. To address the challenges, this paper proposes a hip joint moment estimation method based on generalized moment features (GMF). A GMF generator is constructed to learn GMF of the joint moment which is invariant to individual variations while remaining decodable into joint moments through a dedicated decoder. Utilizing this well-featured representation, a GRU-based neural network is used to predict GMF with joint kinematics data, which can easily be acquired by hip exoskeleton encoders. The proposed estimation method achieves a root mean square error of 0.1180 ± 0.0021 Nm/kg under 28 walking speed conditions on a treadmill dataset, improved by 6.5% compared to the model without body parameter fusion, and by 8.3% for the conventional fusion model with body parameter. Furthermore, the proposed method was employed on a hip exoskeleton with only encoder sensors and achieved an average 20.5% metabolic reduction ( p < 0 . 01 ) for users compared to assist-off condition in level-ground walking.

关键词： Lower-limb exoskeleton Hip joint moment estimation Deep learning

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Realization of a Data-based Adaptive networked Tracking control in NetCon System

Realization of a Data-based Adaptive Networked Tracking Cont...

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第40届中国控制会议

作者： Ye Zhao Shiwen Tong College of Urban Rail Transit and Logistics Beijing Union University College of Robotics Beijing Union University State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of Sciences

It is very difficult to realize the networked tracking control of a controlled process with unclear mechanism. An adaptive networked tracking control method that we proposed can solve such a problem. The method is a data-based control scheme started from the input-output data of the controlled process. We have realized it in the NetCon system. Firstly, the T-S fuzzy model of the controlled object is established according to input-output data by using fuzzy cluster modeling *** the fuzzy prediction model is obtained by transforming it into the fuzzy singleton model through equivalent transformation.A series of control actions are derived by inverting the fuzzy singleton model according to the reversibility condition. These predicted values can compensate the data dropout and time delay in the network communication channels. The influence of external disturbance and uncertainty on system performance is eliminated by using internal model structure and adaptive control method. Finally, experiment results in NetCon system show the effectiveness of the proposed method.

关键词： Fuzzy singleton model inverse model adaptive strategy NetCon system

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An Anomaly Detection Method of Industrial Data Based on Stacking Integration

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Journal on Artificial Intelligence 2021年第1期3卷 9-19页

作者： Kunkun Wang Xianda Liu College of Automation and Electrical Engineering Shenyang Ligong UniversityShenyang110159China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang110016China Shenyang Institute of Automation Chinese Academy of SciencesShenyang110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang110169China

With the development of Internet technology,the computing power of data has increased,and the development of machine learning has become faster and *** the industrial production of industrial control systems,quality inspection and safety production of process products have always been our *** at the low accuracy of anomaly detection in process data in industrial control system,this paper proposes an anomaly detection method based on stacking integration using the machine learning *** are collected from the industrial site and processed by feature *** component analysis(PCA)and integrated rule tree method are adopted to reduce the dimension of the process data,which can restore the original feature information of the data to the maximum *** forest(RF),Adaboost,XGboost,SVM were selected as the first layer of basic *** regression(LR)was used as the secondary learner to build the exception detection model based on stacking integrated *** data was used to train the base learner model and the integrated *** comparing and analyzing the experimental results of between integrated model and each basic learning *** comparing and analyzing the experimental results of the constructed anomaly detection model and the basic learning model,the accuracy of process data anomaly detection is effectively improved,and the false alarm rate of process data anomaly detection is effectively reduced.

关键词： Industrial control system anomaly detection random forest SVM stacking

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Collaborative turning and jumping control of a cyborg locust via sensory stimulation

Collaborative turning and jumping control of a cyborg locust...

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International Conference on Electrical, control and Instrumentation Engineering (ICECIE)

作者： Peng Liu Songsong Ma Shen Liu Yao Li Bing Li School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9781665449670

In this study, we describe the first-ever demonstration of the turning and jumping control of locusts based on electrical stimulation, which can realize a cyborg jumper. In terms of perception, walking distance, speed, payload, maneuverability, and adaptability to complex environments, cyborgs can reveal unmatchable advantages to insect-scale artificial robots. Herein, live locusts (Locusta migratoria) were selected as the study platform. Antennae and cercus stimulations using electrical pulses were applied to elicit the insect to turn and jump. We examined both the turning angle and the angular velocity by tuning the stimulus amplitude or frequency. It is found the turning rate induced by antenna stimulation can be graded. Besides, the stimulation of the cercus at the distal abdomen triggered the jump reliably. Moreover, the directional jumping control of the locust was achieved by the collaboration of turning and jumping control. Such results provide the potential to establish a more precise and more efficient model to develop a direction-controllable cyborg jumper.

关键词： Legged locomotion Wireless communication Protocols Insects Collaboration Electrical stimulation Turning

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Augmented System Observer-based Robust Fault-estimation Scheme for Nonlinear systems

Augmented System Observer-based Robust Fault-estimation Sche...

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第40届中国控制会议

作者： Sheng Gao Guangfu Ma Yanning Guo Wei Zhang Department of Control Science and Engineering Harbin Institute of Technology State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences

For the problem of previous fault-estimation studies that the so-called observer-matching condition should be satisfied,as well as the difficulty to estimate actuator and sensor faults simultaneously,an augmented system fault-estimation observer is proposed for a sort of nonlinear systems subject to process and sensor *** essential idea is to construct an augmented system using a filter and to further create an augmented state vector by considering the system states and simultaneous actuator and sensor *** nonlinearity,process,and sensor disturbances and multiple-fault problem of the actuator and sensor are all taken into ***,an augmented observer is designed for the augmented system by attenuating the disturbances and simultaneously estimating the system states and actuator and sensor *** asymptotic stability is confirmed via the Lyapunov stability theory,and the linear matrix inequality optimization technique is further utilized to solve the designed *** simulations of a single-link flexible-joint robot are performed to illustrate the effectiveness of the proposed robust fault-estimation scheme.

关键词： Augmented observer Fault diagnosis Fault estimation Nonlinear system Linear matrix inequality(LMI)

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