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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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A Slices Perspective for Incremental Nonparametric Inference in High Dimensional State Spaces

A Slices Perspective for Incremental Nonparametric Inference...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Moshe Shienman Ohad Levy-Or Michael Kaess Vadim Indelman Technion Autonomous Systems Program (TASP) Technion - Israel Institute of Technology Haifa Israel Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Aerospace Engineering Technion - Israel Institute of Technology Haifa Israel

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

We introduce an innovative method for incremental nonparametric probabilistic inference in high-dimensional state spaces. Our approach leverages slices from highdimensional surfaces to efficiently approximate posterior distributions of any shape. Unlike many existing graph-based methods, our slices perspective eliminates the need for additional intermediate reconstructions, maintaining a more accurate representation of posterior distributions. Additionally, we propose a novel heuristic to balance between accuracy and efficiency, enabling real-time operation in nonparametric scenarios. In empirical evaluations on synthetic and real-world datasets, our slices approach consistently outperforms other state-of-the-art methods. It demonstrates superior accuracy and achieves a significant reduction in computational complexity, often by an order of magnitude.

关键词： Surface reconstruction Accuracy Shape Probabilistic logic Real-time systems Iterative methods Computational complexity Intelligent robots

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High Performance Supervision Systems

High Performance Supervision Systems

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Alexandre Acácio De Andrade Guilherme Desfeti Postgraduate Program in Engineering and Management of Innovation Federal University of ABC (UFABC) Santo André Brazil Automation Instrumentation and Robotics Engineering Federal University of ABC (UFABC) Santo André Brazil

This research contextualizes the problem of lack of usability and situational awareness provided by conventional supervision systems (SCADA), its relationship with synoptic screens, alarm systems, and industrial accidents. A standard is introduced to lead conventional systems to the “high performance” concept. All basic knowledge of this concept is shown to demonstrate the bases of the standard. And, finally, how to apply the standard to an industrial plant supervision project.

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MOSFET Based Active Rectifier Design in Wireless Capacitive Power Transfer for Electric Vehicles

MOSFET Based Active Rectifier Design in Wireless Capacitive ...

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Smart Mechatronics (ICSMech), IEEE International Conference on

作者： Klemens Raphael Dragon Zener Sukra Lie Aam Muharam Juan Luciano Ika Muhamad Arjuna Perdana Amin Abdul Hapid Computer Engineering Department Automotive and Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Research Center for Transportation Technology Badan Riset dan Inovasi Nasional Bandung Jawa Barat Indonesia

ISBN: (数字)9798350368918

ISBN: (纸本)9798350368925

While the rise of electric vehicles reflects a push for clean energy as envisioned by international agreements like the Paris Agreement, wireless charging's convenience can overcome limitations and hasten their adoption, making it a strategic step alongside regulations such Permenhub No. 15/2022 and SDG 9 “Industry, Innovation, and Infrastructure” for cleaner air and sustainable transportation. The aim is to convert the high frequency AC voltage from the coupling capacitor into a stable DC voltage. Further increase the system sensitivity to the load variations, by designing a MOSFET based active rectifier. By changing load parameters, the accuracy of PFC adaptation to the battery State of Charge (SOC) obtained is at 98% with 2% tolerance for errors. It is concluded that the research is said to be successful because it has met the parameters of general research hypothesis where the system is able to convert high frequency AC voltage into a stable DC voltage.

关键词： Wireless communication MOSFET Wireless sensor networks Rectifiers High-voltage techniques Electric vehicles Batteries State of charge High frequency Voltage control

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Online 3D Bin Packing for Novel Objects Based on Deep Reinforcement Learning

Online 3D Bin Packing for Novel Objects Based on Deep Reinfo...

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International Conference on Advanced robotics and Intelligent Systems (ARIS)

作者： Shao-Yu Chien Ching-Chang Wong Doctoral Program in Robotics College of Engineering Tamkang University New Taipei City 25137 Taiwan Electrical and Computer Engineering Department Tamkang University New Taipei City 25137 Taiwan

In this paper, a method using deep reinforcement learning is proposed to deal with the 3D online bin packing problem. The packing objects are not limited to several specific or fixed cuboid objects, but are composed of more than a thousand objects and randomly generated cuboids, which make the trained policy network can handle novel unknown objects. In addition, the posture of the object in the box can be any angle, not limited to horizontal and vertical. In the proposed method, four voxel maps are used as inputs, and a Soft Actor-Critic (SAC) algorithm is used to train a policy network. On the other hand, in order to deal with various objects with irregular shapes, a packing task simulator with physics engine enable the policy network to learn the state of falling and stacking objects. In terms of training environment of deep reinforcement learning, the proposed method can be applied to boxes of different sizes because of the scalable image information. Moreover, a reward function and a training strategy with gradually increasing difficulty are proposed to effectively improve the learning of policy network. In terms of experimental results, the results on a random object bin packing task in a simulator illustrate the effectiveness of the proposed method.

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

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

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Smart Mechatronics (ICSMech), IEEE International Conference on

作者： Farrell Putra Harimanto Nurul Hasanah Muhammad Khristamto Aditya Pradana Muhammad Nurul Puji Computer Engineering Department Automotive & Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Research Center for Smart Mechatronics National Research and Innovation Agency KST Samaun Samadikun Bandung Indonesia

ISBN: (数字)9798350368918

ISBN: (纸本)9798350368925

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%.

关键词： YOLO Accuracy Depth measurement Brightness Cameras Electric vehicles Distance measurement Sensors Convolutional neural networks Autonomous vehicles

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Adaptive Robot-Mediated Assessment using LLM for Enhanced Survey Quality in Older Adults Care programs 25

Adaptive Robot-Mediated Assessment using LLM for Enhanced Su...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Cheonshu Park Miyoung Cho Minjung Shin Jeh-Kwang Ryu Minsu Jang Social Robotics Research Lab Electronics and Telecommunications Research Institute (ETRI) Daejeon Republic of Korea Interdisciplinary Program in Cognitive Science Seoul National University Seoul Republic of Korea Department of Physical Education Dongguk University Seoul Republic of Korea

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

This study presents an adaptive human-robot interaction (HRI) system that evaluates older adult participants' satisfaction with personalized health care programs. By integrating the CLOi robot with a large language model (LLM), the system conducts satisfaction surveys that adapt in real-time to participant responses. The system was applied to evaluate healthcare programs that include physical health measurements, exercise assessments, and virtual reality (VR) experiences. The system utilizes the CLOi robot and Claude API to analyze response clarity in real-time, automatically generating contextually appropriate follow-up questions when responses are deemed ambiguous. This adaptive questioning strategy ensures comprehensive response quality before proceeding to subsequent survey items. We conducted a preliminary feasibility study with five older adult participants to evaluate our approach. The system leverages LLM prompts to analyze gaps between question intent and participant responses, generating targeted follow-up questions as needed. Results demonstrate that our LLM-enhanced robotic interview system effectively reduced response ambiguity through dynamic follow-up questioning, achieving an 85% response resolution rate. This adaptive approach improved the clarity and specificity of healthcare satisfaction assessments for older adults.

关键词： Surveys Large language models Human-robot interaction Medical services Virtual reality Particle measurements Real-time systems Question generation Older adults Robots

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Spatiotemporal modeling of grip forces captures proficiency in manual robot control

arXiv

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arXiv 2023年

作者： Liu, Rongrong Wandeto, John Nageotte, Florent Zanne, Philippe de Mathelin, Michel Dresp-Langley, Birgitta University of Strasbourg IDEX Postdoctoral Program France Department of Information Technology Dedan Kimathi University of Technology Nyeri Kenya ICube UMR 7357 Robotics Department University of Strasbourg France

New technologies for monitoring grip forces during hand and finger movements in non-standard task contexts have provided unprecedented functional insights into somatosensory cognition. Somatosensory cognition is the basis of our ability to manipulate and transform objects of the physical world and to grasp them with the right amount of force. In previous work, the wireless tracking of grip force signals recorded from biosensors in the palm of the human hand has permitted us to unravel some of the functional synergies that underlies perceptual and motor learning under conditions of non-standard and essentially unreliable sensory input. This paper builds on this previous work and discusses further, functionally motivated, analyses of individual grip force data in manual robot control. Grip forces were recorded from various loci in the dominant and non-dominant hands of individuals by means of wearable wireless sensor technology. Statistical analyses bring to the fore skill-specific temporal variations in thousands of grip forces of a complete novice and a highly proficient expert in manual robot control. A brain inspired neural network model that uses the output metric of a Self-organizing Map with unsupervised winner-take-all learning was run on the sensor output from both hands of each user. The neural network metric expresses the difference between an input representation and its model representation at any given moment in time t and reliably captures the differences between novice and expert performance in terms of grip force *** motivated spatiotemporal analysis of individual average grip forces, computed for time windows of constant size in the output of a restricted amount of task-relevant sensors in the dominant (preferred) hand, reveal finger-specific synergies reflecting robotic task skill. The analyses lead the way towards grip force monitoring in real time. This will permit tracking task skill evolution in trainees, or identify individ

关键词： Laparoscopy

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Enhancing Performance of 3D Point Completion Network using Consistency Loss

arXiv

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arXiv 2024年

作者： Wijaya, Kevin Tirta Goenawan, Christofel Rio Kong, Seung-Hyun Computer Graphics Department Max Planck Institute for Informatics Saarbrücken66123 Germany Robotics Master Program KAIST Daejeon34051 Korea Republic of CCS Graduate School of Mobility KAIST Daejeon34051 Korea Republic of

Point cloud completion networks are conventionally trained to minimize the disparities between the completed point cloud and the ground-truth counterpart. However, an incomplete object-level point cloud can have multiple valid completion solutions when it is examined in isolation. This one-to-many mapping issue can cause contradictory supervision signals to the network because the loss function may produce different values for identical input-output pairs of the network. In many cases, this issue could adversely affect the network optimization process. In this work, we propose to enhance the conventional learning objective using a novel completion consistency loss to mitigate the one-to-many mapping problem. Specifically, the proposed consistency loss ensure that a point cloud completion network generates a coherent completion solution for incomplete objects originating from the same source point cloud. Experimental results across multiple well-established datasets and benchmarks demonstrated the proposed completion consistency loss have excellent capability to enhance the completion performance of various existing networks without any modification to the design of the networks. The proposed consistency loss enhances the performance of the point completion network without affecting the inference speed, thereby increasing the accuracy of point cloud completion. Notably, a state-of-the-art point completion network trained with the proposed consistency loss can achieve state-of-the-art accuracy on the challenging new MVP dataset. The code and result of experiment various point completion models using proposed consistency loss will be available at: https://***/kaist-avelab/ConsistencyLoss . © 2024, CC BY.

关键词： Cloud platforms

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Local-Nonlinearity-Enabled Deep Subdiffraction Control of Acoustic Waves

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Physical Review Letters 2023年第23期131卷 234001-234001页

作者： Jiaxin Zhong Chengbo Hu Kangkang Wang Jun Ji Tao Zhuang Haishan Zou Jing Lu Hyeonu Heo Bin Liang Yun Jing Jian-Chun Cheng Key Laboratory of Modern Acoustics and Institute of Acoustics Nanjing University Nanjing 210093 China Graduate Program in Acoustics The Pennsylvania State University University Park Pennsylvania 16802 USA NJU-Horizon Intelligent Audio Lab Horizon Robotics Beijing 100094 China

Diffraction sets a natural limit for the spatial resolution of acoustic wave fields, hindering the generation and recording of object details and manipulation of sound at subwavelength scales. We propose to overcome this physical limit by utilizing nonlinear acoustics. Our findings indicate that, contrary to the commonly utilized cumulative nonlinear effect, it is in fact the local nonlinear effect that is crucial in achieving subdiffraction control of acoustic waves. We theoretically and experimentally demonstrate a deep subwavelength spatial resolution up to λ/38 in the far field at a distance 4.4 times the Rayleigh distance. This Letter represents a new avenue towards deep subdiffraction control of sound, and may have far-reaching impacts on various applications such as acoustic holograms, imaging, communication, and sound zone control.

关键词： Acoustic measurements Acoustic modeling Acoustic wave phenomena Acoustics Nonlinear acoustics Ultrasonics

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