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70th International Astronautical Congress, IAC 2019

作者： Moser, Joshua Cooper, John Neilan, James Chapin, Liam Glassner, Samantha Komendera, Erik Department of Mechanical Engineering Virginia Polytechnic Institute State University United States Autonomous Integrated Systems Research Branch NASA Langley Research Center Flight Software System Branch NASA Langley Research Center Department of Computer Science Virginia Polytechnic Institute State University United States Department of Mechanical Engineering Northeastern University

NASA Langley Research Center has developed a robotic unit, the Assembler, for use in the domain of autonomous in-space assembly, capable of reconfiguration and collaboration. A known part of space mission scheduling and the autonomous collaborative assembly problem is task assignment. This problem can be thought of as a Markov decision process framing of a job shop scheduling problem which allows for a reinforcement learning solution to approximate the optimal policies for task assignment. The research presented here initiates the evaluation of reinforcement learning to that end. Simulation results show that the policies determined through reinforcement learning performed better than random and greedy policies. These promising results will support further research into the applicability and optimality of reinforcement learning in increasingly complex scenarios. Copyright © 2019 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Reinforcement learning

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RobotDrlSim: A Real Time Robot Simulation Platform for Reinforcement Learning and Human Interactive Demonstration Learning

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Journal of Physics: Conference Series 2021年第1期1746卷

作者： Te Sun Liang Gong Xvdong Li Shenghan Xie Zhaorun Chen Qizi Hu David Filliat School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Computer Science and System Engineering Department and Inria Flowers Team ENSTA Institut Polytechnique Paris Palaiseau France

Deep reinforcement learning (DRL) techniques give robotics research an AI boost in many applications. In order to simultaneously accommodate the complex robotic behaviour simulation and DRL algorithm verification, a new simulation platform, namely the RobotDrlSim, is proposed. First, we design a standardized API interfacing mechanism for coordinating diverse environments on RobotDrlSim platform, where PyBullet simulator is equipped with an API to form a physical engine for robotics simulation. Second, benchmark DRL models are included in the baseline library for evaluation. Third, real-time human-robot interactions can be captured and imported to drive the RobotDrlSim tasks, which provide big data-stream for reinforcement learning. Experimentations show that cutting-edge DRL algorithms developed in python can be seamlessly deployed to the robots, and human interactions can be availed in training the robots. RobotDrlSim is valid for efficiently developing DRL algorithms for artificial intelligence models of robots, and it is especially suitable for the robot educational purposes.

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Apple Vision Pro for Healthcare: "The Ultimate Display"? Entering the Wonderland of Precision Medicine

arXiv

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arXiv 2023年

作者： Egger, Jan Gsaxner, Christina Chen, Xiaojun Bian, Jiang Kleesiek, Jens Puladi, Behrus Girardetstraße 2 Essen45131 Germany Hufelandstraße 55 Essen45147 Germany Hufelandstraße 55 Essen45147 Germany Department of Oral and Maxillofacial Surgery University Hospital RWTH Aachen Pauwelsstraße 30 Aachen52074 Germany Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16/II Graz8010 Austria Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Dongchuan Road 800 Minhang District Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Dongchuan Road 800 Minhang District Shanghai200240 China Health Outcomes & Biomedical Informatics College of Medicine University of Florida GainesvilleFL32610 United States Partner Site Essen Hufelandstraße 55 Essen45147 Germany Department of Physics TU Dortmund University August-Schmidt-Str. 4 Dortmund44227 Germany Institute of Medical Informatics University Hospital RWTH Aachen Pauwelsstraße 30 Aachen52074 Germany

At the Worldwide Developers Conference (WWDC) in June 2023, Apple introduced the Vision Pro. The Vision Pro is a Mixed Reality (MR) headset, more specifically it is a Virtual Reality (VR) device with an additional Video See-Through (VST) capability. The VST capability turns the Vision Pro also into an Augmented Reality (AR) device. The AR feature is enabled by streaming the real world via cameras to the (VR) screens in front of the user’s eyes. This is of course not unique and similar to other devices, like the Varjo XR-3. Nevertheless, the Vision Pro has some interesting features, like an inside-out screen that can show the headset wearers’ eyes to "outsiders" or a button on the top, called "Digital Crown", that allows you to seamlessly blend digital content with your physical space by turning it. In addition, it is untethered, except for the cable to the battery, which makes the headset more agile, compared to the Varjo XR-3. This could actually come closer to the "Ultimate Display", which Ivan Sutherland had already sketched in 1965. Not available to the public yet, like the Ultimate Display, we want to take a look into the crystal ball in this perspective to see if it can overcome some clinical challenges that - especially - AR still faces in the medical domain, but also go beyond and discuss if the Vision Pro could support clinicians in essential tasks to spend more time with their patients. © 2023, CC BY.

关键词： Augmented reality

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Collaborative Perception Datasets for Autonomous Driving: A Review

arXiv

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arXiv 2025年

作者： Wang, Naibang Shang, Deyong Gong, Yan Hu, Xiaoxi Song, Ziying Yang, Lei Huang, Yuhan Wang, Xiaoyu Lu, Jianli Beijing100083 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China State Key Laboratory of Intelligent Green Vehicle and Mobility Tsinghua University Beijing100084 China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence School of Computer Science and Technology Beijing Jiaotong University China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore School of Mechatronics Engineering Harbin Institute of Technology 92 West Dazhi St. Harbin China Department of Electronic & Electrical Engineering University of Bath United Kingdom

Collaborative perception has attracted growing interest from academia and industry due to its potential to enhance perception accuracy, safety, and robustness in autonomous driving through multi-agent information fusion. With the advancement of Vehicle-to-Everything (V2X) communication, numerous collaborative perception datasets have emerged, varying in cooperation paradigms, sensor configurations, data sources, and application scenarios. However, the absence of systematic summarization and comparative analysis hinders effective resource utilization and standardization of model evaluation. As the first comprehensive review focused on collaborative perception datasets, this work reviews and compares existing resources from a multi-dimensional perspective. We categorize datasets based on cooperation paradigms, examine their data sources and scenarios, and analyze sensor modalities and supported tasks. A detailed comparative analysis is conducted across multiple dimensions. We also outline key challenges and future directions, including dataset scalability, diversity, domain adaptation, standardization, privacy, and the integration of large language models. To support ongoing research, we provide a continuously updated online repository of collaborative perception datasets and related literature: https://***/frankwnb/Collaborative-Perceptio n-Datasets-for-Autonomous-Driving. © 2025, CC BY-SA.

关键词： Sensor data fusion

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BPS2025 - Rapid and accurate transepithelial resistance measurement of bronchiolar epithelium using impedance spectroscopy and RCRC model

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Biophysical Journal 2025年第3期124卷 296a-297a页

作者： Athena Chien Colby Lewallen Guiying Cui Analia Vazquez Cegla Erica Lull Hanna Khor Nael A. McCarty Craig Forest Walter H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University Atlanta GA USA National Eye Institute National Institutes of Health Bethesda MD USA Department of Pediatrics Emory University Atlanta GA USA Department of Pharmacology Emory University Atlanta GA USA School of Mechanical Engineering Georgia Institute of Technology Atlanta GA USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Picture Archives Communication System Emory University School of Medicine Atlanta GA USA

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Semg-Based Hand Gesture Recognition Via Dilated Convolutional Neural Networks

Semg-Based Hand Gesture Recognition Via Dilated Convolutiona...

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IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Elahe Rahimian Soheil Zabihi S. Farokh Atashzar Amir Asif Arash Mohammadi Concordia Institute for Information System Engineering Montreal Canada Electrical and Computer Engineering Concordia University Montreal QC Canada Electrical Computer Engineering & Mechanical Aerospace Engineering New York University USA

The recent evolution of Artificial Intelligence (AI) and deep learning models coupled with advancements of assistive robotic systems have shown great potential in significantly improving myoelectric control of prosthetic devices. In this regard, the paper proposes a novel deep-learning-based architecture for processing surface Electromyography (sEMG) signals to classify and recognize upper-limb hand gestures via incorporation of dilated causal convolutions. The proposed approach has the potential to significantly improve the overall recognition accuracy due to the specific design of the convolutional layers. By using dilated causal convolutions which gradually increases the receptive field of the network, and by applying Conv1D, the proposed architecture eliminates the need for readjustment of the input sequences and inherently takes into account the hidden temporal correlations existing among the available set of sEMG sequences. Contrary to recent hybrid (RNN-CNN) solutions, the proposed architecture neither uses recurrent units nor 2-dimensional convolutions. The publically-accessible NinaPro DB2 dataset is utilized for training and evaluation of the proposed network. Through an extensive set of experiments, it is observed that the proposed architecture significantly outperforms its state-of-the-art counterparts and provides an average 8.71% performance improvement.

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Control design considering magnetic flux characteristic uncertainty for magnetic levitation system with magnetic flux and current feedback

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2019年第10期139卷 1159-1166页

作者： Urasaki, Shinpachiro Kobayashi, Yoshimitsu Kami, Yasushi Mitani, Yuichiroh Nobuyama, Eitaku Department of Electronic Control Engineering National Institute of Technology Gifu College 2236-2. Kamimakuwa. Motosu. Gifu 501-0495 Japan Department of Electrical and Computer Engineering National Institute of Technology Akashi College 679-3. Nishioka. Uozumi Akashi. Hyogo674-8501 Japan Department of Mechanical Engineering National Institute of Technology Numazu College 3600. Ooka. Numazu Shizuoka410-8501 Japan Department of Systems Innovation and Informatics Faculty of Computer Science and System Engineering Kyushu Institute of Technology 680-4.Kawazu. Iizuka Fukuoka820-8502 Japan

This paper describes control design considering magnetic flux characteristic uncertainty for magnetic levitation system with magnetic flux and current feedback. The position of the levitated object is calculated from the hall voltage and current of the electromagnet. We show the uncertainty for magnetic flux characteristic and design the stabilizing controller by proportional feedback with the hall voltage and current. Moreover, we tried to design servo controller by two-degree-of-freedom control by using a finite number of frequency response (FNFR) model including the uncertainty and confirmed the validity of the proposed method by experiment results. © 2019 The Institute of Eleclrical Engineers of Japan.

关键词： Frequency response

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Texture evaluation of transparent acrylic resin using self-organizing maps

Texture evaluation of transparent acrylic resin using self-o...

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2018 International Conference on Artificial Intelligence, ICAI 2018 at 2018 World Congress in computer Science, computer engineering and Applied Computing, CSCE 2018

作者： Wakabayashi, Y. Chanthasen, K. Yoshida, S. Kawabata, Y. Ogawa, T. Sugibayashi, T. Department of Mechanical System Engineering Takushoku University Tokyo Japan Mechanical CAD Unit Polytechnic University of Japan Tokyo Japan Department of Electronics and Computer System Takushoku University Tokyo Japan

ISBN: (纸本)1601324804

This paper presents the evaluation method of transparent acrylic resin using Self-organizing maps. Surface texture of products needs the sensitive information for adding high-value to the products. It needs the technology method to convey sensitive information for designing and producing products. For this purpose, the method using a self-organizing map (an unsupervised learning neural network) was presented in this report. A transparent acrylic resin, shot blasted, was used for a specimen and its surface texture parameters were evaluated by self-organizing map. The surface texture parameters: arithmetic mean height;arithmetic mean roughness;transmittance;haze are measured and the relation of these parameters was shown on the self-organizing map. This map showed clearly the relation of these parameters and it indicates that the self-organizing map can be used for the evaluation of acrylic resin texture shot blasted. © 2018 CSREA Press. All rights reserved.

关键词： Surface roughness

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XceptionTime: A novel deep architecture based on depthwise separable convolutions for hand gesture classification

arXiv

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arXiv 2019年

作者： Rahimian, Elahe Zabihi, Soheil Atashzar, Seyed Farokh Asif, Amir Mohammadi, Arash Concordia Institute for Information System Engineering Concordia University MontrealQC Canada Electrical and Computer Engineering Concordia University MontrealQC Canada Electrical & Computer Engineering Mechanical & Aerospace Engineering New York University United States

Capitalizing on the need for addressing the existing challenges associated with gesture recognition via sparse multichannel surface Electromyography (sEMG) signals, the paper proposes a novel deep learning model, referred to as the XceptionTime architecture. The proposed innovative XceptionTime is designed by integration of depthwise separable convolutions, adaptive average pooling, and a novel non-linear normalization technique. At the heart of the proposed architecture is several XceptionTime modules concatenated in series fashion designed to capture both temporal and spatial information-bearing contents of the sparse multichannel sEMG signals without the need for data augmentation and/or manual design of feature extraction. In addition, through integration of adaptive average pooling, Conv1D, and the non-linear normalization approach, XceptionTime is less prone to overfitting, more robust to temporal translation of the input, and more importantly is independent from the input window size. Finally, by utilizing the depthwise separable convolutions, the XceptionTime network has far fewer parameters resulting in a less complex network. The performance of XceptionTime is tested on a sub Ninapro dataset, DB1, and the results showed a superior performance in comparison to any existing counterparts. In this regard, 5.71% accuracy improvement, on a window size 200ms, is reported in this paper, for the first time. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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Printing and Programming of In-Situ Actuators

Printing and Programming of In-Situ Actuators

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Arash Alex Mazhari Alan Zhang Randall Ticknor Sean Swei Elizabeth Hyde Mircea Teodorescu Electrical and Computer Engineering department University of California Santa Cruz CA USA Mechanical Engineering department University of California Berkeley CA USA Aeronautics and Astronautics department Stanford University CA USA Intelligent System Division NASA Ames Research Center Moffett Field CA USA Engineering Directorate at NASA Ames Research Center Moffett Field CA USA

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

This paper demonstrates a method for developing in-situ actuation retrofitted onto an additive manufacturing platform. By printing and programming a cantilever beam automated to deploy and actuate without human intervention, one may extend the functionality of an additive manufacturing platform without installing additional hardware. The design leverages a common Fused Filament Fabrication (FFF) platform to physically validate the concept through experiment. Kinetics are repeatedly measured and referenced to develop over two dozen generations of in-situ actuator designs, each a function of programming the specific manipulation of the FFF platform's gantry-code. The resulting lever fabricated onto the build plate can be embedded into the machine's design envelope to deploy other fabricated objects off of the build plate. As a proof of concept to demonstrate the actuator's utility, a Prusa MK3S3D printer is used to fabricate an in-situ actuator which is automated to deploy a simultaneously printed spherical 3D printed structure.

关键词： Actuators Springs Three-dimensional printing Programming Fabrication

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