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International Conference on Control, Automation and Systems ( ICCAS)

作者： Myeongjin Kim Jingon Yoon Donghyun Kim Dongwon Yun Department of Robotics and Mechatronics Engineering DGIST Daegu Korea

Owing to an increase in diverse disaster incidents instigated by natural catastrophes and human error, extensive research on mobile robots, designed for disaster environment comprehension and human rescue operations, and has become more prevalent. Specifically, endeavors are being undertaken to equip mobile robots with grippers, enabling confined space navigation and life-saving activities within disastrous circumstances. Prominent among these are investigations into hybrid grippers and actuation systems, utilizing both hard and soft materials for obstacle removal. Nevertheless, for effective deployment of mobile robots within real disaster scenarios, the necessity for a compact hybrid gripper and drive system is indispensable. This paper elucidates the design and realization of a small-scale pneumatic pump module, developed to operate the jamming module of the previously compactly implemented hybrid gripper, also incorporating discussions on performance validation experiments. The verification experiments confirmed the capability of the proposed small-scale pneumatic pump to adequately operate the jamming module, thus reinforcing the hybrid gripper's grasping performance.

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Multi-Class Machine Learning Based Approach for SleepDisorder Identification Using EEG Signals 7

Multi-Class Machine Learning Based Approach for SleepDisorde...

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7th IET Smart Cities Symposium, SCS 2023

作者： Murugesan, Rajalakshmi Chandran, Karthik Dutta, P.K. Department of Mechatronics Thiagarajar College of Engineering Tamilnadu Madurai India Department of Robotics & Automation Jyothi Engineering College Kerala India Department of Electronics and Communication School of Engineering and Technology Amity University Kolkata India

ISBN: (纸本)9781839539831

Accurately identifying sleep stages is crucial for the analysis and management of sleep disorders. Electroencephalogram (EEG) signals have shown promise in sleep stage identification. This research presents a novel method for classifying different sleep stages using EEG data based on a multi-class support vector machine (SVM) approach. The proposed method involves preprocessing the EEG signals to remove noise and artifacts, followed by feature extraction using wavelet transform and statistical methods. A multi-class SVM classifier is then trained to identify four sleep stages: waking, non-rapid eye movement (NREM) stage 1, non-rapid eye movement (NREM) stage 2, and rapid eye movement (REM) stage. The method is evaluated on a dataset comprising EEG signals collected from 20 healthy individuals during a full-night sleep study. The results demonstrate that the proposed technique outperforms existing EEG-based approaches, achieving anaverage classification accuracy of 86.2 for sleep stage identification. The suggested method has potential applications in clinical settings for sleep disorder evaluation and management. Additionally, it can be extended to other applications requiring the identification of various classes from EEG data. © The Institution of engineering & Technology 2023.

关键词： Support vector machines

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Data-Driven Iterative optimization of TDOF Controller with Rational Model

Data-Driven Iterative optimization of TDOF Controller with R...

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2023 IEEE International Conference on mechatronics (ICM)

作者： Taejune Kong Hanul Jung Sehoon Oh Department of Robotics and Mechatronics Engineering DGIST Daegu Korea

For precise control, it can be achieved through a feedforward controller with a feedback control scheme. The feedforward controller can be automatically tuned with the datadriven methods through experimental results. However, these data-driven methods are interrupted when the input disturbance is applied. This problem can be solved using Disturbance Observer (DOB) in the control loop. In addition, a data-driven method derives accurate control performance by simultaneously tuning the feedforward controller and DOB model. Furthermore, a tuning algorithm with a rational model is proposed for the corresponding modeling to a flexible system. The simulation results verify that the proposed method accurately tunes the controller for an unknown plant under an input disturbance.

关键词： mechatronics Simulation Resonant frequency Iterative algorithms Disturbance observers Feedback control Feedforward systems

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Evaluation of modal parameters and static characteristics for composite mono leaf spring

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Noise and Vibration Worldwide 2021年第3期52卷 33-47页

作者： Samir Mohamed Mohamed Soliman, Ehab Mechatronics and Robotics Department Faculty of Engineering Egyptian Russian University Badr CityCairo11829 Egypt

The objective of this paper was to investigate and evaluate modal parameters and static characteristics for designed carbon/epoxy composite CFRP leaf spring made from carbon ﬁber-reinforced epoxy by comparing its finite element analysis FEA modal, harmonic and static results to that of conventional steel one under the same maximum full bump load. In ANSYS software, finite element modal, harmonic and static analysis for 3D models of conventional steel and designed composite mono leaf springs is carried out. FEA displacements for the steel and designed composite CFRP mono leaf springs at the maximum full bump load were 92.9 mm and 93.1 mm respectively. Compared to the steel mono leaf spring, the composite mono leaf spring has advantages;lower stresses, higher natural frequencies and higher safety in stresses of front and rear eyes. All these advantages let the composite leaf spring can be carried maximum full bump load with enough higher performance over steel one to avoid resonance and satisfy maximum stress failure criterion. © The Author(s) 2020.

关键词： Carbon

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Prediction of directivity characteristics of piezoelectric macro-fiber composite interdigital transducers: a semi-analytical approach

e-Journal of Nondestructive Testing

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e-Journal of Nondestructive Testing 2024年第7期29卷

作者： Raorane, Siddhesh Ambrozinski, Lukasz Stepinski, Tadeusz Packo, Pawel Department of Robotics and Mechatronics Faculty of Mechanical Engineering and Robotics AGH University of Science and Technology Krakow30-059 Poland

The employment of guided waves for structural health monitoring applications has attracted substantial attention in recent years. Piezoelectric transducers are frequently employed in these systems for elastic wave generation and acquisition. Their dynamic properties, i.e., direction-dependent frequency characteristics such as directivity patterns, are an important feature for designing a reliable monitoring strategy. Although analysis of the transducer directivity pattern can be performed using finite element models, these usually tend to be computationally very expensive, especially for parametric and optimization studies. In this paper, a semi-analytical framework is proposed that can predict the far-field directivity patterns of complex transducers of arbitrary shape and structure mounted on a substrate. The framework described in this paper integrates analytical far-field predictions with a local finite element model evaluating the traction field produced by the transducer. Consequently, the FE model accurately captures near-field responses, whereas the analytical component predicts the far-field responses. The implementation of this semi-analytical framework requires the development of analytical and numerical approaches. The former includes computation of dispersion curves of the inspected plate, the stress and displacement profiles of wave modes, and ultimately, the excitability relations, while the latter includes estimation of the traction field between the transducer and the plate using the FE model. Owing to the popularity of piezoelectric macro-fiber composite (MFC) transducers, in this paper, we apply the proposed method to predict the directivity patterns of MFC M2807-P2 transducer. To validate the reliability of the proposed method, the predicted patterns are compared with experimental results obtained using Laser Doppler Vibrometer. © 2024, NDT. net GmbH and Co. KG. All rights reserved.

关键词： Forecasting

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High-Performance Admittance Control of An Industrial Robot Via Disturbance Observer 48

High-Performance Admittance Control of An Industrial Robot V...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Samuel, Kangwagye Haninger, Kevin Oh, Sehoon Dgist Department of Robotics and Mechatronics Engineering Daegu Korea Republic of Fraunhofer Ipk Department of Automation Berlin Germany

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Safe physical interaction using admittance control on an industrial robot with inner-loop motion control remains challenging. This is partly due to the low intrinsic admittance and stability issues from inner-loop motion control limitations (e.g. bandwidth). To increase the admittance at an interaction point with the user/environment, this paper proposes a robust admittance control architecture. A disturbance observer (DOB) is used to improve effective inner-loop motion control, suppressing the effects of velocity disturbances. The DOB uses the robot's closed-loop task space velocity control as the nominal model, compensating disturbances between the commanded robot velocity and realized robot velocity output. An admittance controller uses measured force to generate robot velocity commands. Detailed analyses are carried out to theoretically evaluate the proposed control system. Experiments conducted on a COMAU RACER-7-1.4 industrial robot verify the effectiveness of the proposed admittance control scheme and stability in environmental contact. Moreover, the proposed method is simple to implement on the existing robot system. © 2022 IEEE.

关键词： Industrial robots

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Representing Objects in Map Building Using A Semantic SLAM Scheme

Representing Objects in Map Building Using A Semantic SLAM S...

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2022 Chinese Automation Congress, CAC 2022

作者： Yang, Yu Wang, Di Zheng, Weiqi Shanghai University Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronics Engineering and Automation Shanghai China

ISBN: (纸本)9781665465335

In order to make the robot understand the scene better and the human-robot interaction more friendly, a lightweight semantic SLAM system is proposed in this paper. Our system uses a LIDAR combined with a camera to obtain object location in SLAM and determines object information such as category with a CNN-based object detector. The object identities are represented in the form of joint probability distributions that are updated by Bayes' theorem. The FPA algorithm is used to obtain parameter valuation with maximum posterior probability so as to determine the modeling feature parameters of objects on the map. The similarity between objects is measured with the Bhattacharyya coefficient to avoid duplicate modeling of identified objects in map updating. The proposed system is simulated in Gazebo and an effective scheme balancing accuracy and computation cost is obtained. Finally, experiments on a small indoor robot demonstrate the effectiveness of our method. © 2022 IEEE.

关键词： Semantics

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Minimalistic Soft Exosuit for Assisting the Shoulder via Biomechanics-Aware Optimization 21

Minimalistic Soft Exosuit for Assisting the Shoulder via Bio...

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2022 IEEE-RAS 21st International Conference on Humanoid Robots, Humanoids 2022

作者： Joshi, Sagar Beck, Irene Seth, Ajay Santina, Cosimo Della Mechanical Maritime and Materials Engineering Faculty TU Delft Delft Netherlands Robotics and Mechatronics Department Munich Germany

ISBN: (纸本)9798350309799

Soft exosuits can help to prevent work-related musculoskeletal disorders by offloading human muscles through the application of external forces across the human joints. Many exosuits achieve this through tension producing elements called as exotendons. However, the design of these devices is based on intuition and experience. This leads to potentially sub-optimal or even harmful designs that could cause discomfort or injury to the wearer. This paper deals with automatically finding appropriate attachments and routing locations for exotendons. We propose to do that by accurate musculoskeletal modeling and design parameter optimization of soft exosuits. We focus here on a soft exosuit with a single passive exotendon to assist the shoulder. Using three arm raising-lowering and internal-external rotation motions as examples, we optimize the attachment point and rest-length of the exotendon to reduce overall muscle effort. We then fabricate the exosuit and validate the model predictions by testing with six participants. Supporting the predictions from simulations, measured muscle activity shows reductions in the deltoid and trapezius muscles. This work represents the first instance of explicitly optimizing functional and geometric parameters of exotendons in wearable assistive devices for minimizing human effort. © 2022 IEEE.

关键词： Muscle

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Applying CNN Models with Stereo Camera for Object Detection and Distance Estimation in Automatic Micro Electric Vehicle (MEVi) 1

Applying CNN Models with Stereo Camera for Object Detection ...

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1st International Conference on Smart mechatronics, ICSMech 2024

作者： Harimanto, Farrell Putra Hasanah, Nurul Pradana, Muhammad Khristamto Aditya Puji, Muhammad Nurul Binus Aso School of Engineering Bina Nusantara University Automotive & Robotics Program Computer Engineering Department Jakarta11480 Indonesia National Research and Innovation Agency Kst Samaun Samadikun Research Center for Smart Mechatronics Bandung40135 Indonesia

ISBN: (纸本)9798350368918

Technological advancements in the automotive industry are currently focused on autonomous driving systems or driver assistance systems. Depth estimation is also an important feature of the autonomous driving system as it can accurately estimate the distance to the detected objects. In current developments technologies such as stereo cameras are more popular for a more accurate depth estimation. The process for object detection consists of a combination of classification, localization, and segmentation. Deep convolutional neural networks are the basis of object detection. The methods for object detection are divided into two categories namely the Region Proposal-based method such as RCNN, and the Classification-based method which provides for methods such as YOLO and MobileNet. As a part of the research conducted by the National Research and Innovation Agency (BRIN) on Automatic Micro Electric Vehicles or MEVi, this paper analyzed and compared the performance of several CNN models for object detection as well as the performance of depth sensing using ZED stereo camera which was used for the autonomous system in MEVi. Among several models that had been compared, YOLO v8 model demonstrates high accuracy and moderate speed as indicated by its high mAP (0.50) and FPS (38.46). Based on depth sensing results, the accuracy of distance measurement also depended on brightness and environment with the average of accuracy achieved indoors was 98.2% and outdoors was 96.3%. © 2024 IEEE.

关键词： Automotive industry

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Optimum Design of a Wire-Driven Redundant Spherical Parallel Manipulator for Foot Drop Rehabilitation System 48

Optimum Design of a Wire-Driven Redundant Spherical Parallel...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Gamal, Ahmed Mohamed, Abdelfatah Iwata, Hiroyasu Assal, Samy F. M. Department of Mechatronics and Robotics Engineering Egypt Waseda University Japan Tanta University Faculty of Engineering Department of Production Engineering and Mechanical Design Egypt

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Post stroke patients may suffer from a gait disorder which is called foot drop. They may experience a toedrag, slow walking speed and a high risk of tripping. Although physical therapy exercises can be a remedy to such cases, their high cost and the lack of professional caregivers arise the need for robotic rehabilitation systems. On the other hand, the existing robotic systems for foot-drop rehabilitation are heavy and bulky. Therefore, in this paper, a lightweight foot-drop rehabilitation system based on a wire-driven redundant spherical parallel manipulator (RSPM) is developed. The proposed RSPM can provide 3-DOF movements to exercise the dorsiflexion, plantarflexion, supination, lateral and medial rotations. Kinematic and static analyses are performed for the proposed rob RSPM. In order to guarantee permissible positive tensions for the wires of the RSPM, a new performance index is introduced, called permissible tension index. Optimum design based on optimizing certain performance indices is carried out to obtain the proper dimensions of the movable platform design parameters of the proposed RSPM. A new index called permissible tension index is introduced to guarantee permissible positive tensions for the wires of the RSPM. A weighted sum of the global kinematic dexterity, global minimized transmission and the global permissible tension indices is considered for the objective function of the optimization process. The RSPM performance is evaluated over its range of motion and the reaction forces exerted on the ankle are shown to be tolerable by an injured ankle. Additionally, the Jacobian-based kinematic controller is shown to be capable of tracking the normal gait trajectories with acceptable tracking errors. © 2022 IEEE.

关键词： Kinematics

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