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Novel Acceleration Estimation for Improving Stability and Performance in Impedance Control

Journal of Institute of Control, robotics and Systems 2024年第11期30卷 1260-1265页

作者： Lee, Hyunwook Cheon, Dasol Oh, Sehoon Department of Mechatronics Engineering Gyeongsang National University Korea Republic of Department of Robotics and Mechatronics Engineering DGIST Korea Republic of

This article presents a novel approach to enhancing the stability and performance of impedance control in robotic systems through improved acceleration estimation. The focus is on Series Elastic Actuators (SEAs), which are vital for achieving precise force control and ensuring safe and efficient human-robot collaboration. Traditional cascade impedance control is limited by the passive nature of the system, particularly when rendering high stiffness values. To address these limitations, we introduce Elastic Structure Preserving (ESP) control, which retains the inherent elastic structure of the SEA while enabling high-performance impedance rendering. A key aspect of ESP is the accurate estimation of load-side acceleration, which is crucial for ensuring stability and maintaining control performance. To achieve this, we propose a new acceleration estimation technique that enhances impedance rendering. Experimental results demonstrate the effectiveness of the proposed method, which shows improved stability and precise impedance control. This approach offers a robust solution for advanced robotic applications requiring high stiffness and precise force control. © ICROS 2024.

关键词： Robot applications

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Deep Learning-Based Hash Function Cryptanalysis 15

Deep Learning-Based Hash Function Cryptanalysis

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Jeong, Ongee Moon, Inkyu Department of Robotics and Mechatronics Engineering Daegu Korea Republic of

ISBN: (纸本)9798350364637

This paper analyzes the strength of Message Digest Algorithm (MD5) by performing deep learning-based Encryption Emulation (EE) and Plaintext Recovery (PR) attacks. We convert randomly generated S12-bit arrays, messages, into 128-bit arrays, digests, with MD5 in different numbers of steps. Furthermore, two different structures of deep learning models, fully-connected neural network and Bidirectional Long Short-Term Memory (BiLSTM), are used in attacks and trained to analyze MD5 automatically. As a result, the BiLSTM shows better prediction accuracy than the fully-connected neural network. Moreover, the PR attack is more challenging than the EE attack. © 2024 IEEE.

关键词： Ciphertext

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Privacy-Preserving Classification of Photon-Limited Images Using Deep Learning 15

Privacy-Preserving Classification of Photon-Limited Images U...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Jeong, Ongee Moon, Inkyu Department of Robotics and Mechatronics Engineering Daegu Korea Republic of

ISBN: (纸本)9798350364637

This paper presents a privacy-preserving image classification scheme based on deep learning and photon counting imaging. The vision transformer-based deep learning model can classify the photon-limited color images, which represents the images sparsely by using a few photons. As a result, over 90 % of photon-limited images can be classified when at least 50,000 photons are used. Since some information from the original images is lost in the photon-limited images, it is challenging to recognize the personal information in the photon-limited images. Therefore, the proposed scheme can classify the images while concealing the portion of the information in the images. © 2024 IEEE.

关键词： Differential privacy

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Optimization of Polymeric Microneedle Electrode for High-Quality EMG recording 46

Optimization of Polymeric Microneedle Electrode for High-Qua...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Jeong, Jinwoong Kim, Yongwoo Lee, Sanghoon Department of Robotics and Mechatronics Engineering Daegu Korea Republic of

ISBN: (纸本)9798350371499

This paper proposes the optimization of polyimide microneedle electrode using a 3D print double molding fabrication. Various shapes (circle, triangle, square, star, and hexagon) of microneedles are investigated by changing printing angles and double molding process. Aspect ratio, insertion force, and break point of the fabricated microneedles are investigated to maximize penetration capacity. Also, muscle signals were obtained using optimized microneedle electrodes to distinguish hand gesture. © 2024 IEEE.

关键词： Molding

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Generalized Dynamics of Non-Grounded Systems with Series Elastic Actuators and Effect of Actuator Base Fixation on Force Control Robustness

Generalized Dynamics of Non-Grounded Systems with Series Ela...

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2025 IEEE International Conference on mechatronics, ICM 2025

作者： Choi, Wonhyeok Oh, Sehoon DGIST Department of Robotics and Mechatronics Engineering Daegu Korea Republic of

ISBN: (纸本)9798331533892

This paper proposes a generalized dynamics model for a Non-Grounded System (NGS) mounted with a Reaction Force Sensing Series Elastic Actuator (RFSEA), focusing on scenarios where the actuator's base is unfixed and interacts with the ground. In RFSEA-based force control, the actuator base, which provides the necessary reaction force as dictated by Newton's third law, plays a critical role. This study examines how the interaction between the actuator base and the ground it is attached to influences the response of the series elasticity that generates force in the RFSEA. The investigation involves two steps. First, a generalized dynamics model that considers the dynamics between the actuator base and the ground is derived. Second, the Small Gain Theorem (SGT) is applied to evaluate the robustness of force control under load-side model uncertainties when a Force-based Disturbance Observer (FDOB), originally designed for GS, is adapted to NGS. The analysis reveals that force control utilizing FDOB can become unstable when the actuator base is unfixed, and simulations validate these findings. © 2025 IEEE.

关键词： Electric grounding

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Spectral and Morphological Classification of Celestial Objects using Physics Informed Machine Learning 3

Spectral and Morphological Classification of Celestial Objec...

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3rd IEEE International Conference on robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Siddiquee, Md Fairuz Hasan, Md Mehedi University of Dhaka Department of Robotics and Mechatronics Engineering Dhaka Bangladesh

ISBN: (纸本)9798331534400

The classification of celestial objects has traditionally relied on spectral analysis, which involves identifying unique patterns in the light emitted or absorbed by the objects. In this study, we present a novel approach to categorizing celestial objects based on their spectral properties and morphological structures, leveraging physics-informed machine learning (PIML) techniques to enhance the classification accuracy and efficiency. In the spectral analysis, using Sloan Digital Sky Survey (SDSS) dataset, we classified stars, galaxies, and quasars, focusing on redshift and near-infrared values with i and z filters. Our analysis employed three machine learning models (K-Neighbors, SVM, Random Forest), and the performances were verified to be resilient applying hyperparameter tuning and cross-validation. The best-performing model, Random Forest Classifier, achieved 98% accuracy. In the morphological analysis, the YOLO v5 model was utilized on a customized dataset of astronomical images to further classify the Galaxy Class into five distinct categories and the YOLOv5 model detected those with an average precision of 92.6% and recall of 68.9%. In addition, the interpretability of the model is enhanced by the incorporation of physical knowledge, allowing astronomers to gain deeper insights into the underlying processes. Integrating physical knowledge into these ML models enhances interpretability, facilitating automated real-time analysis of big astronomical data and advancing our understanding of the cosmos. © 2024 IEEE.

关键词： Random forests

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Development of an Accelerometer-based Data Acquisition System for Hand Gesture Recognition 3

Development of an Accelerometer-based Data Acquisition Syste...

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3rd IEEE International Conference on robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Tajwar, Atique Haque, Md. Ettashamul Hasan, Md Mehedi University of Dhaka Department of Robotics and Mechatronics Engineering Dhaka Bangladesh

ISBN: (纸本)9798331534400

This study presents a novel approach to Human-Robot Interaction (HRI) by employing gesture recognition to control robotic systems, such as bionic arms or manipulators, through accelerometer-based data collection. Using a low-cost glove equipped with an ESP32 microcontroller and accelerometer, data was gathered from 22 participants performing five distinct gestures: a clockwise circle, square, triangle, double tap, and the letter S. After filtering, these gestures were translated into features that trained a small neural network model designed for low-storage microcontrollers like the ESP32. With a performance accuracy of 90.4%, the neural network outperformed existing ensemble learning models, demonstrating its suitability for real-time gesture recognition tasks in embedded robotic systems. This highly efficient model enables seamless and intuitive control of robotic devices by facilitating responsive and natural HRI through gesture recognition. Designed specifically for compact and resource-limited environments, the model addresses the challenges associated with limited computational resources and restricted hardware capabilities. It provides a robust and scalable solution that can be easily integrated into various robotics applications, where gesture-based control is advantageous. © 2024 IEEE.

关键词： Accelerometers

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An Integrated Setup for Sequential Hybrid Laser Beam Micromachining (LBMM) and Micro-EDM for Machining Micro-hole Arrays with Automated Tool Localization

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International Journal of Precision engineering and Manufacturing 2025年 1-18页

作者： Azhar, Wan Ahmad Bin Wan Saleh, Tanveer Autonomous Systems and Robotics Research Unit Department of Mechatronics Engineering International Islamic University Malaysia Jalan Gombak Kuala Lumpur53100 Malaysia

Micro holes array has significant applications in the field of aerospace and turbo machinery, industrial filtration, microfluidic devices, biomedical engineering, and so on. There are various ways to manufacture micro holes arrays. In order to have a cost-effective, yet precise and efficient process for the fabrication of micro holes array, an integrated setup has been introduced in this paper to combine Laser Beam Micro Machining (LBMM) and micro-EDM (μEDM). This approach uses LBMM for quick material removal and µEDM for precise finishing. However, one of the main challenges in hybrid LBMM-µEDM is the localization of LBMM micro holes for accurate positioning of μEDM electrode. In this present study, an integrated setup with automated hole localization has been presented to improve the hybrid LBMM-μEDM process in terms of alignment accuracy and machining rate. The microscope camera that is mounted on the μEDM stage scans and captures images of LBMM holes. Then, the images were stitched and processed using image processing techniques to get the center coordinate of the LBMM micro holes. Finally, the operator validated the coordinates and G-code was automatically generated for the µEDM process to perform the fine finishing. The result indicated that the alignment accuracy is within 15 μm. Furthermore, the newly developed automation strategies for the hybrid LBMM-μEDM process show significant improvement in machining time with 5 × faster than the pure μEDM process, and better performances in overcut, taper angle, and electrode wear. Furthermore, the hybrid process was found to be 1.6 × more energy efficient as compared to the pure µEDM process. Overall, the newly developed integrated system was found to be highly effective for producing micro holes array. © The Author(s), under exclusive licence to Korean Society for Precision engineering 2025.

关键词： Precision engineering

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Path Tracing and Validation of Omni Wheeled Mini Robot Using Visual Feedback 11

Path Tracing and Validation of Omni Wheeled Mini Robot Using...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Ahamed, Aftaab Jayaraj, Karthik Fazil, Shibin Nikitha, S. Sudheer, P.A. National Institute of Technology Calicut Robotics Interest Group Mechatronics Robotics Laboratory India National Institute of Technology Calicut Mechatronics/Robotics Laboratory Mechanical Engineering Department India

ISBN: (纸本)9798350379136

Holonomic drives are gaining popularity in many fields due to their versatility in various industrial applications. The ability of the holonomic drive robots to move in all directions makes it distinct from other drive mechanisms. Open-loop control of omnidirectional bots can be achieved with relative ease whereas automating it is not as easy. When trying to create an automated system with lower cost, off the shelf components and hardware, other problems may arise as a result of the poor performance of the components. Here a three wheeled omnidirectional robot that can trace a predefined path without human intervention has successfully been developed. © 2024 IEEE.

关键词： Open loop control

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Radar Dead-reckoning Based EKF-SLAM Using Virtual Line Segment

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Journal of Institute of Control, robotics and Systems 2024年第9期30卷 996-1003页

作者： Lee, Min-Ho Seo, Kyeong-Wook Hwang, Dong-Yun Song, Jin-Woo Do, Hoang-Viet Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent Drone Sejong University Korea Republic of Department of Artificial Intelligence and Robotics and Convergence Engineering for Intelligent Drone Sejong University Korea Republic of

In this paper, we propose a dead reckoning–based radar simultaneous localization and mapping (SLAM) system utilizing constrained virtual line segments. In particular, we utilize Doppler velocity from radar for odometry while constructing a map using 3D radar point-cloud measurements. Unlike the conventional approach where accelerometers are employed, our system integrates radar velocity for pose calculation, thereby preventing double-integration and accelerometer bias and potentially improving the localization accuracy. Additionally, we analyze radar 3D point cloud characteristics and employ a recursive random sample consensus algorithm to eliminate outliers. The resulting set of inlier point clouds is then used to predict virtual line segments. Using virtual lines instead of feature points mitigates the sparse and noisy nature of radar point clouds. Moreover, considering the typical characteristics of indoor environments where walls intersect perpendicularly, we impose constraints on estimates to enhance performance further. We evaluate the performance of the proposed algorithm through experiments conducted in real indoor environments using an unmanned ground vehicle platform. Our results demonstrate significant improvements over traditional odometer-based systems, with reductions of approximately 41% in 2D trajectory error and 49.5% in heading angle error. © ICROS 2024.

关键词： Accelerometers

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