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Cyber Science and Technology Congress (CyberSciTech), IEEE

作者： Riya Mahajan Yegang Du Alexander Schmitz Gabriele Trovato Yasuyuki Taki Shigeki Sugano Future Robotics Organization Waseda University Tokyo Japan Smart Ageing International Research Center Tohoku University Sendai Japan College of Engineering Innovative Global Program Shibaura Institute of Technology Tokyo Japan

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

Sleep analysis is pivotal in maintaining physical and mental health, as well as enhancing the quality of life, particularly for the elderly. Despite significant advancements in wearable sleep analysis devices in recent years, their intrusive nature often causes discomfort and reduces their acceptability among older adults. This study introduces a non-invasive method for sleep analysis utilizing tactile sensors, termed the uSkin pillow. By integrating uSkin sensors into a standard pillow, we collected data on the user's movements during sleep. This data was processed using a Savitzky-Golay digital filter and algorithmic analysis, resulting in comprehensive sleep-related information, including movement, sleep duration, respiratory rate, and sleep positions. The study also conducted experiments to evaluate both the satisfaction of elderly users with the uSkin pillow and the accuracy of its sleep analysis capabilities. The findings confirmed that the uSkin pillow is not only user-friendly but also capable of delivering sleep analysis results that are comparable to those obtained from commercially available smart bracelets. This suggests that the uSkin pillow holds significant potential as a comfortable and effective alternative for non-invasive sleep monitoring, particularly suited for the elderly population.

关键词： Accuracy Tactile sensors Mental health Sensors Older adults Digital filters Biomedical monitoring Standards Monitoring

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Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

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Operations Research Forum 2024年第3期5卷 58页

作者： Wei, Li Solihin, Mahmud Iwan Saruchi, Sarah ‘Atifah Astuti, Winda Hong, Lim Wei Kit, Ang Chun School of Computer Science and Technology (School of Software) Guangxi University of Science and Technology Guangxi 545006 China Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur 56000 Malaysia Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan 26600 Malaysia Computer Engineering Department Automotive and Robotics Engineering Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Anomaly detection Computer vision Deep learning Defect detection High-precision cylindrical parts Image processing Optical illumination

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Preliminary Characterization of a Plastic Piezoelectric Motor Stator Using High-Speed Digital Holographic Interferometry 1

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SEM Annual Conference and Exposition on Experimental and Applied Mechanics, 2020

作者： Zhao, Z. Carvalho, P.A. Tang, H. Pooladvand, K. Gandomi, K.Y. Nycz, C.J. Furlong, C. Fischer, G.S. Robotics Engineering Program Worcester Polytechnic Institute WorcesterMA United States Mechanical Engineering Department Worcester Polytechnic Institute WorcesterMA United States

ISBN: (数字)9783030597733

ISBN: (纸本)9783030597726

Precise surgical procedures such as deep brain tumor ablation may benefit from intra-operative image guidance using magnetic resonance imaging (MRI). However, the MRI’s strong magnetic fields and constrained space pose the need for robotic devices to assist the surgeon. Piezoelectric motors are often used to actuate these robots. The piezoelectric resonant motor (PRM) is a class of such motors that consist of a bonded piezoelectric ring stator and a frictionally coupled rotor. Steady-state excitation at certain frequencies leads to specific mode shapes on the stator with surface waves having both in-plane and out-of-plane displacement components that cause the coupled rotor to spin. High-speed digital holography (HDH) can be used to measure time variant displacements with nanometer and microsecond resolution. We present initial measurements acquired at 67 k frame per second (fps) of two custom-designed plastic stator components operating at drive frequencies of 6.788 kHz and 6.980 kHz, respectively. Circumferential motion of the traveling surface waves with out-of-plane peak-to-peak displacement of approximately 100 nm peak-to-peak amplitude and a settling time of 2.99 ms was observed. Results demonstrate that plastic stators may be a promising alternative to metallic stators for use in the MRI environment. © 2021, The Society for Experimental Mechanics, Inc.

关键词： Stators

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Road Redesign Technique Achieving Enhanced Road Safety by Inpainting with a Diffusion Model

arXiv

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arXiv 2023年

作者： Mishra, Sumit Mishra, Medhavi Kim, Taeyoung Har, Dongsoo The Robotics Program Korea Advanced Institute of Science and Technology Daejeon Korea Republic of The CCS Graduate School of Mobility Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

Road infrastructure can affect the occurrence of road accidents. Therefore, identifying roadway features with high accident probability is crucial. Here, we introduce image inpainting that can assist authorities in achieving safe roadway design with minimal intervention in the current roadway structure. Image inpainting is based on inpainting safe roadway elements in a roadway image, replacing accident-prone (AP) features by using a diffusion model. After object-level segmentation, the AP features identified by the properties of accident hotspots are masked by a human operator and safe roadway elements are inpainted. With only an average time of 2 min for image inpainting, the likelihood of an image being classified as an accident hotspot drops by an average of 11.85%. In addition, safe urban spaces can be designed considering human factors of commuters such as gaze saliency. Considering this, we introduce saliency enhancement that suggests chrominance alteration for a safe road view. © 2023, CC BY.

关键词： Roads and streets

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Analytical reference compensation for tracking dynamic target signals with linear robust MPC strategies

Analytical reference compensation for tracking dynamic targe...

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American Control Conference (ACC)

作者： Tito L. M. Santos Bruno S. S. Pereira Department of Electrical and Computer Engineering Federal University of Bahia Salvador Bahia Brazil Graduate Program on Electrical and Engineering Federal University of Bahia Salvador Bahia Brazil Department of Robotics SENAI-CIMATEC Salvador Bahia Brazil

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper proposes an analytical target modifi-cation for linear robust model predictive control strategies in order to deal with time-varying references defined by dynamic signal targets. The new approach can be directly integrated to linear robust model predictive control algorithms that achieve piecewise constant reference tracking if recursive feasibility is ensured for any set-point. The main contribution is to present a direct analytical approach that provides a potentially improved steady-state tracking error performance with the same computation complexity of the original MPC for tracking piecewise constant reference. A simulation case study based on the trajectory tracking control of a quadrotor is used to illustrate the usefulness of the new analytical target modification layer.

关键词： Sufficient conditions Target tracking Trajectory tracking Heuristic algorithms Prediction algorithms Steady-state Computational complexity

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Deep Reinforcement Learning with Pedestrian Trajectory Prediction Model for Service Robot Navigation in Crowded Environments

Deep Reinforcement Learning with Pedestrian Trajectory Predi...

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International Conference on Advanced robotics and Intelligent Systems (ARIS)

作者： Shih-Hao Wang Yu-Hsiung Wu Tzuu-Hseng S. Li aiRobots Laboratory MS Degree Program on AI Robotics National Cheng Kung University Tainan Taiwan Dept. of Electrical Engineering aiRobots Laboratory NCKU Tainan Taiwan

Safety and effectiveness are crucial considerations in the navigation of robots within crowded environments. Previous research in this field has predominantly focused on an omniscient approach, designing environments with exceptional obstacle avoidance capabilities. However, this approach fails to address the constraints imposed by real-world applications’ limited field of view. Additionally, constructing a robot with a complete field of view can be prohibitively expensive. To tackle these challenges, this paper presents a novel approach that combines a social spatial-temporal graph convolutional neural network (Social-STGCN) with reinforcement learning (RL) to enhance crowd avoidance navigation. This approach offers a more practical and cost-effective solution. Simulations conducted to evaluate our proposed method demonstrate its effectiveness by achieving higher success rates and lower collision rates compared to existing approaches. These results confirm the efficiency and safety of our algorithm for crowd avoidance navigation. In practical applications, we employ the Intel RealSense L515 camera to capture depth and RGB image data, enabling the utilization of the Deep SORT algorithm for pedestrian tracking and recognition. This facilitates the acquisition of accurate position and speed information. To validate the performance of our system, we conducted robot experiments in real-world scenarios, including indoor corridors and elevator lobbies. The results obtained from these experiments clearly indicate that our proposed system successfully accomplishes navigation tasks with improved safety and effectiveness. In summary, this paper introduces an effective crowd avoidance navigation algorithm. By leveraging the Social-STGCN and RL techniques, our algorithm enables robots to navigate tasks with enhanced safety and efficiency, even when their field of view is limited.

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Accessing Gender Bias in Speech Processing Using Machine Learning and Deep Learning with Gender Balanced Audio Deepfake Dataset

Accessing Gender Bias in Speech Processing Using Machine Lea...

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International Conference on Informatics and Computing (ICIC)

作者： Tricia Estella Amalia Zahra Wai-Keung Fung Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia Department of Engineering Cardiff School of Technologies EUREKA Robotics Centre Cardiff Metropolitan University United Kingdom

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The rapid advancement of AI has led to the rise of Audio Deepfakes (AD), which pose serious ethical and security concerns by accurately mimicking human speech. This research addresses the urgent need for effective AD detection, with a focus on gender bias that can reduce the effectiveness of detection models. We examined how gender affects the performance of both Machine Learning (Support Vector Machine, Random Forest, Logistic Regression, XGBoost) and Deep Learning (Deep Neural Networks, Convolutional Neural Networks) models using the GBAD dataset. Our findings show that models trained on female audio outperform those trained on male audio, likely due to the expressive nature of female voice features and high-pitched artifacts in FAKE audio. This highlights the need for more robust, gender-sensitive detection systems. Future work should focus on developing adaptive models to reduce gender bias, improving security, and creating lightweight models for wider public use.

关键词： Deep learning Support vector machines Deepfakes Adaptation models Logistic regression Computational modeling Security Speech processing Informatics Random forests

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Optimal Vision-Based Orientation Steering Control for a 3D Printed Dexterous Snake-Like Manipulator to Assist Teleoperation

TechRxiv

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TechRxiv 2023年

作者： Razjigaev, Andrew Pandey, Ajay K. Howard, David Roberts, Jonathan Jaiprakash, Anjali Crawford, Ross Wu, Liao The School of Electrical Engineering and Robotics The QUT Centre for Robotics Brisbane Australia The Cyber Physical Systems Program Data61 CSIRO Australia The Department of Orthopaedic Surgery Prince Charles Hospital ChermsideQLD Australia The School of Mechanical and Manufacturing Engineering University of New South Wales Sydney Australia

Endoscopic cameras attached to miniaturised snakelike continuum robotic arms can improve dexterity, accessibility, and visibility in minimally invasive surgical tasks. This steerable camera can expand the field of view and enhance the surgical experience with additional degrees of freedom. However, it also creates more control options that complicate human-machine interaction. This challenge presents an opportunity to develop novel human-machine control strategies using visual sensing to complement the dexterous actuation of a steerable surgical manipulator. This study presents a semi-autonomous controller to assist teleoperation by steering a camera such that it keeps an arbitrary target in the centre of the field of view, thus assisting in surveying different orientations about the target with image-based information. Two controllers are proposed using classical image-based visual servoing techniques and optimal visual predictive control techniques. These techniques are simulated and validated on our robot SnakeRaven: a 3D-printed patient-specific end-effector attached to the RAVEN II surgical platform. Both systems, most notably the visual predictive approach, operated successfully with robustness to a lack of information about the target. A video demonstrating the main results of this paper can be found via https://***/fiUM9qYdl1U © 2023, CC BY.

关键词： Remote control

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Towards A COLREGs Compliant Autonomous Surface Vessel in a Constrained Channel

arXiv

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arXiv 2022年

作者： Meyers, James Connor McCord, Thomas Sayre Zhang, Zhiyong Singh, Hanumant Field Robotics Laboratory Northeastern University United States Coast Guard United States MIT - WHOI Joint Program Lilium GmbH United States Field Robotics Laboratory Northeastern University United States

In this paper, we look at the role of autonomous navigation in the maritime domain. Specifically, we examine how an Autonomous Surface Vessel(ASV) can achieve obstacle avoidance based on the Convention on the International Regulations for Preventing Collisions at Sea (1972), or COLREGs, in real-world environments. Our ASV is equipped with a broadband marine radar, an Inertial Navigation System (INS), and uses official Electronic Navigational Charts (ENC). These sensors are used to provide situational awareness and, in series of well-defined steps, we can exclude land objects from the radar data, extract tracks associated with moving vessels within range of the radar, and then use a Kalman Filter to track and predict the motion of other moving vessels in the vicinity. A Constant Velocity model for the Kalman Filter allows us to solve the data association to build a consistent model between successive radar scans. We account for multiple COLREGs situations based on the predicted relative motion. Finally, an efficient path planning algorithm is presented to find a path and publish waypoints to perform real-time COLREGs compliant autonomous navigation within highly constrained environments. We demonstrate the results of our framework with operational results collected over the course of a 3.4 nautical mile mission on the Charles River in Boston in which the ASV encountered and successfully navigated multiple scenarios and encounters with other moving vessels at close quarters. Copyright © 2022, The Authors. All rights reserved.

关键词： Kalman filters

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Soft Sensing Brace for Monitoring Knee Joint Kinetics and Kinematics During Squatting

Soft Sensing Brace for Monitoring Knee Joint Kinetics and Ki...

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International Conference on Ubiquitous Robots (UR)

作者： Junhwan Choi Eunseok Song Hyunkyu Park Kyoungchul Kong Jung Kim Department of Mechanical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Robotics Program Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea

Exoskeletons have been developed to increase joint performance by providing assistance torque. Soft exosuits, which use light and soft materials for human-robot interaction, are a growing trend in the orthosis. However, wearable motion capture technology for biomechanical data collection remains a challenge. Traditional measurement systems such as motion capture camera (Mocap), inertial measurement units (IMUs), and ground reaction force and moment (GRF&M) are computationally intensive and collected in restricted laboratory environments. To address this challenge, we propose a sensing brace to monitor knee joint kinetics using an elastic strain sensor and a pressure-based mechanomyography (pMMG) sensor. The pMMG sensor improves comfort, reliability, linearity, and durability, and does not need to attach directly to the skin. Linear regression model of knee angle and moment are conducted with high reliability which showed $p-value < 0.05$ and $R^{2}$ is over 0.98. We used a linear regression model to estimate knee angle and torque during squatting with $\text{RMSE} =4.4\ deg$ in knee angle estimation and $\text{RMSE}=3.4\ Nmm/kg$ in knee moment estimation.

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