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Design and Implementation of a Behavioral Sequence Framework for Human-Robot Interaction Utilizing Brain-Computer Interface and Haptic Feedback

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Journal of engineering and Science in Medical Diagnostics and Therapy 2023年第4期6卷 041003页

作者： Hazra, Sudip Whitaker, Shane Shiakolas, Panos S. Mars Lab Mechanical & Aerospace Engineering The University of Texas at Arlington ArlingtonTX76019 United States Micro Medical Manufacturing Automation Robotics Systems Laboratory Mechanical & Aerospace Engineering The University of Texas at Arlington ArlingtonTX76019 United States

In assistive robotics, research in Brain Computer Interface aims to understand human intent to enhance Human-Robot interaction and augment human performance. In this research, a framework to enable a person with an upper limb disability to use an assistive system toward maintaining self-reliance is introduced and its implementation and evaluation are discussed. The framework interlinks functional components and establishes a behavioral sequence to operate the assistive system in three stages;action classification, verification, and execution. An action is classified based on identified human intent and verified through haptic and/or visual feedback before execution. The human intent is conveyed through facial expressions and verified through head movements. The interlinked functional components are an electro-encephalogram (EEG) sensing device, a head movement recorder, a dual-purpose glove, a visual feedback environment, and a robotic arm. Five volunteers are used to evaluate the ability of the system to recognize a facial expression, the time required to respond using head movements, convey information through vibrotactile feedback effects, and the ability to follow the established behavioral sequence. Based on the evaluation, a personalized training dataset should be used to calibrate facial expression recognition and define the time required to respond during verification. Custom vibrotactile effects were effective in conveying system information to the user. The volunteers were able to follow the behavioral sequence and control the system with a success rate of 80.00%, thus providing confidence to recruit more volunteers to identify and address improvements and expand the operational capability of the framework. © 2023 by ASME.

关键词： Human robot interaction

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Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and meta-learning convolutional shrinkage neural network

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Petroleum Science 2023年第2期20卷 1142-1154页

作者： Yun-Peng He Chuan-Zhi Zang Peng Zeng Ming-Xin Wang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyangLiaoning 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyangLiaoning 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyangLiaoning 110169China University of Chinese Academy of Sciences Beijing100049China Shenyang University of Technology ShenyangLiaoning 110870China School of Automation and Electrical Engineering Shenyang Ligong UniversityShenyangLiaoning 110159China

The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.

关键词： Few-shot learning Indicator diagram Meta-learning Soft thresholding Sucker-rod pumping system Time–frequency signature Working condition recognition

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Modeling of the Constant-Current Stimuli Response of a Bio-Robot for Long-Term Motion Control

Modeling of the Constant-Current Stimuli Response of a Bio-R...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Zhou, Jie Fang, Yuan Chen, Yang Li, Yao Li, Bing Key University Laboratory of Mechanism & Machine Theory and Intelligent Unmanned Systems of Guangdong School of Mechanical Engineering and Automation Harbin Institute of Technology Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518055 China

ISBN: (纸本)9781665481090

Bio-robots continue to receive attention because of their small size and low power consumption. Unfortunately, it currently faces two major obstacles before practical application. The first is the habituation issue, insects are difficult to be effectively controlled for a long time by electrical stimuli signals. The other one is the lack of model for motion control. In this research, we established a stimuli-response model for cockroach bio-robot based on long time current. A more convenient optic lobe implantation method has been proposed, which has lower attenuation and better stimuli effect. Constant electrical stimuli also significantly increased the number of effective controls for crawling. A cockroach bio-robot stimuli distance response model was established and validated with experiment data. Five parameters were including in this model, namely sex, length, weight, current amplitude and equivalent stiumated-number. This model can predict the movement distance of biological robots well. © 2024 IEEE.

关键词： Microrobots

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Saliency-SLAM: Saliency Prediction for Visual SLAM

Saliency-SLAM: Saliency Prediction for Visual SLAM

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2023 Tianjin University-IET Electrical and Information engineering Doctoral Forum, TJU IET 2023

作者： Jin, Sheng Meng, Qinghao Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9781839539824

This paper proposes a novel approach for visual simultaneous localization and mapping (vSLAM), called Saliency-SLAM. This method effectively emulates the visual attention mechanism by integrating a saliency prediction model. In contrast to conventional vSLAM methods, which treat all feature points as equally significant, salient feature points are given greater importance in Saliency-SLAM. Therefore, we construct a novel saliency dataset specifically for vSLAM task to train a saliency prediction model by integrating multi-level visual information. Then, we propose to use the salient value of predicted result as the weight of significance for the feature points during the optimization process. Exhaustive experiments have been conducted on the KITTI dataset, demonstrating that our proposed method outperforms existing approaches in terms of localization accuracy. © The Institution of engineering & Technology 2023.

关键词： Forecasting

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Adaptive observer for a LTV system with partially unknown state matrix and delayed measurements 14

Adaptive observer for a LTV system with partially unknown st...

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14th International Congress on Ultra Modern Telecommunications and Control systems and Workshops, ICUMT 2022

作者： Bobtsov, Alexey Nikolaev, Nikolay Slita, Olga Kozachek, Olga Oskina, Olga Itmo University Institute of Problems of Mechanical Engineering Laboratory 'Control of Complex Systems' Department of Control Systems and Robotics Saint-Petersburg Russia Itmo University Department of Control Systems and Robotics Saint-Petersburg Russia

ISBN: (数字)9798350398663

ISBN: (纸本)9798350398663

Problem of adaptive state observer synthesis for linear time-varying (LTV) system with unknown time-varying parameter and delayed output measurements is considered. State observation problem has attracted the attention of many researchers [1]. In this paper the results proposed in the [2]-[4] are developed. It is supposed that the state matrix can be represented as sum of known and unknown parts. Output vector is measured with known constant delay. An adaptive identification algorithm which reconstructs unknown state and unknown time-varying parameter is proposed. © 2022 IEEE.

关键词： Time varying control systems

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A new active rehabilitation training mode for upper limbs based on Tai Chi Pushing Hands

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Biomimetic Intelligence & robotics 2024年第3期4卷 82-92页

作者： Xiangpan Li Liaoyuan Li Jianhai Han Bingjing Guo Ganqin Du School of Mechatronics Engineering Henan University of Science and TechnologyLuoyang 471003China Henan Provincial Key Laboratory of Robotics and Intelligent Systems Luoyang 471003China School of Automotive Engineering Hubei University of Automotive TechnologyShiyan 442002China Hubei Key Laboratory of Automotive Power Train and Electronic Control Shiyan 442002China The First Affiliated Hospital of Henan University of Science and Technology Luoyang 471003China

Robot-assisted rehabilitation is a crucial approach to restoring motor function in the ***,the current training trajectory lacks sufficient theoretical or practical support,and the monotony of single-mode training is a *** Chi Pushing Hands,a beneficial and effective daily exercise,has been shown to improve balance function,psychological state,and motor function of the upper extremities in patients recovering from *** address these issues,we propose a new active rehabilitation training that incorporates Tai Chi Pushing Hands movements and yin-yang balance *** training trajectory and direction are encoded by the velocity field and consist of two processes:yang(push)and yin(return).During yang,the limb actively pushes the robot to move,while during yin,the limb actively follows the robot’s *** provide necessary assistance,an admittance controller with self-adaptive parameters is *** addition,we introduce two indexes,the‘Intention Angle’(ϖ)and the time ratio(Γ),to evaluate motion perception *** experiment was conducted on a 4-degree-of-freedom upper limb rehabilitation robot platform,and the subjects were separated into a familiar group and an unfamiliar *** experiment results show that the training could be completed well no matter whether the subject is familiar with Tai Chi Pushing Hands or *** parameters and the movement of the robot can be adjusted based on the interactive force to adapt to the ability of the subject.

关键词： Tai Chi Push Hands Velocity field Self-adaptive admittance Upper limb rehabilitation Active training

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A bionic approach for the mechanical and electrical decoupling of an MEMS capacitive sensor in ultralow force measurement

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Frontiers of Mechanical engineering 2023年第2期18卷 297-317页

作者： Wendi GAO Bian TIAN Cunlang LIU Yingbiao MI Chen JIA Libo ZHAO Tao LIU Nan ZHU Ping YANG Qijing LIN Zhuangde JIANG Dong SUN State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesOverseas Expertise Introduction Center for Micro/Nano Manufacturing and Nano Measurement Technologies Discipline InnovationXi’an Jiaotong University(Yantai)Research Institute for Intelligent Sensing Technology and SystemSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049China Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing Yantai 265599China State Key Laboratory of Robotics and Systems(HIT) Harbin 150006China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China Department of Biomedical Engineering City University of Hong KongHong KongChina

Capacitive sensors are efficient tools for biophysical force measurement,which is essential for the exploration of cellular ***,attention has been rarely given on the influences of external mechanical and internal electrical interferences on capacitive *** this work,a bionic swallow structure design norm was developed for mechanical decoupling,and the influences of structural parameters on mechanical behavior were fully analyzed and optimized.A bionic feather comb distribution strategy and a portable readout circuit were proposed for eliminating electrostatic *** instability was evaluated,and electrostatic decoupling performance was verified on the basis of a novel measurement method utilizing four complementary comb arrays and applicationspecific integrated circuit *** electrostatic pulling experiment showed that the bionic swallow structure hardly moved by 0.770 nm,and the measurement error was less than 0.009% for the area-variant sensor and 1.118% for the gap-variant sensor,which can be easily compensated in *** proposed sensor also exhibited high resistance against electrostatic rotation,and the resulting measurement error dropped below 0.751%.The rotation interferences were less than 0.330 nm and(1.829×10^(-7))°,which were 35 times smaller than those of the traditional differential *** on the proposed bionic decoupling method,the fabricated sensor exhibited overwhelming capacitive sensitivity values of 7.078 and 1.473 pF/μm for gap-variant and area-variant devices,respectively,which were the highest among the current *** immunity to mechanical disturbances was maintained simultaneously,i.e.,less than 0.369% and 0.058% of the sensor outputs for the gap-variant and area-variant devices,respectively,indicating its great performance improvements over existing devices and feasibility in ultralow biomedical force measurement.

关键词： micro-electro-mechanical system capacitive sensor bionics operation instability mechanical and electrical decoupling biomedical force measurement

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Design of a Miniature Flapping-Wing Robot Capable of Quick and Energy-Efficient Takeoff

Design of a Miniature Flapping-Wing Robot Capable of Quick a...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Xu, Yi Shen, Yishi Peng, Liang Li, Qi Yu, Jin Zhang, Shi Shi, Qing Beijing Institute of Technology Ministry of Education Key Laboratory of Biomimetic Robots and Systems Beijing100081 China Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781665481090

Micro areal vehicles are widely used in civilian and military applications, but it is still challenging for them to take off in place with both high speed and energy utilization efficiency. Inspired by locusts, one solution is to integrate the flying with the jumping motion, utilizing the speed generated by jumping to provide sufficient aerodynamic forces for the flight. In this paper, we develop a miniature locust-inspired robot capable of jumping and flying. By coupling the jumpingleg mechanism and flapping-wing mechanism, the robot has a compacted size (with a body length of 15 cm and a weight of 23 g). Furthermore, we design a cascaded jump-flying controller to achieve stable transition and maneuverable flight. A series of experimental results reveal that the robot achieves a maximum jumping height of 50 cm and a minimum flying turning radius of 0.8 m. Remarkably, the robot is capable of taking off in place through controlling the hindleg and wings coordinately. © 2024 IEEE.

关键词： Microrobots

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Multi-Class Gait Phase Recognition using Machine Learning Models with Two Training Methods 15

Multi-Class Gait Phase Recognition using Machine Learning Mo...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mekni, Amal Narayan, Jyotindra Gritli, Hassene Borj Cedria Tunis1164 Tunisia Indian Institute of Technology Guwahati Mechtronics and Robotics Laboratory Department of Mechanical Engineering Assam Guwahati781039 India University of Tunis El Manar le Belvédère Laboratory of Robotics Informatics and Complex Systems RISC Lab-LR16ES07 National Engineering School of Tunis Tunis1002 Tunisia

ISBN: (纸本)9798350370249

Walking is a fundamental human activity, yet understanding its intricacies is crucial for diagnosing and treating various gait abnormalities and musculoskeletal disorders. This study investigates the machine learning-based classification of gait phases into five subphases with two training methods. Using data from 100 individuals obtained from an open-source platform. The gait cycle was divided into the following subphases: Initial Contact, Loading Response, Mid Stance, Terminal Stance, and Pre-Swing. In the first approach, a stratified random sampling method was employed, allocating 80% of data from each subphase for training and 20% for testing. The second method involved training with data from 80% participants and testing with data from 20% participants. Out of different ML algorithms, the Random Forest (RF) achieves the highest performance (99.2%) with similar results across both methods. The k-Nearest Neighbors (k-NN), the Logistic Regression (LR), the Decision Tree (DT), and the Support Vector Machine (SVM) show comparable performance ( 91%) with marginal improvement with the second method. Naive Bayes (NB) has a significantly lower performance ( 77.7%). These findings suggest that RF is a robust and effective algorithm for this task. Furthermore, the study indicates that applying machine learning techniques to datasets divided by subjects presents promising opportunities for creating precise gait analysis systems. This can assist clinicians and researchers in enhancing patient care and gaining deeper insights into human locomotion. © 2024 IEEE.

关键词： Adversarial machine learning

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Modeling and Simulation of an Elastic Passive Joint for Non-flipping Jumping Robot

Modeling and Simulation of an Elastic Passive Joint for Non-...

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2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Li, Qi Peng, Liang Wu, Zhiyuan Ye, Pengda Zhang, Weitao Xu, Yi Shi, Qing Beijing Institute of Technology Intelligent Robotics Institute School of Mechatronical Engineering Beijing100081 China Beijing Institute of Technology Ministry of Education Key Laboratory of Biomimetic Robots and Systems Beijing100081 China

ISBN: (纸本)9798350372601

To enhance the environmental adaptability of small-sized robots, jumping is commonly employed to achieve high mobility and obstacle-clearing capabilities. However, the prevalent problem of flipping in jumping robots limits their practical applications. Instead of adding extra control mechanisms and actuators, we propose an elastic passive joint (EPJ) to mitigate flipping. By incorporating a revolute joint, a switch, and a spring at the base of the hindleg, the leg can rotate around the body during the take-off phase, with the spring absorbing angular kinetic energy to reduce excessive angular velocity. We conducted dynamic modeling and a series of simulations to optimize the EPJ's position and stiffness. The simulation results indicate that with the EPJ in operation, adjusting the axis position slightly results in a zero-point for angular velocity. Additionally, the optimal spring stiffness of 1566 N/m ensures a non-flipping jump, which decreases the jumping height but improves the jumping distance. © 2024 IEEE.

关键词： Kinetic energy

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