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International Conference on Information Fusion

作者： Markus Walker Hayk Amirkhanian Marco F. Huber Uwe D. Hanebeck Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany Department Cyber Cognitive Intelligence (CCI) Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany Institute of Industrial Manufacturing and Management IFF University of Stuttgart Germany

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Bayesian Neural Networks (BNNs) offer a sophisticated framework for extending classical neural network point estimates to encompass predictive distributions. Despite the high potential of BNNs, established BNN training methods such as Variational Inference (VI) and Markov Chain Monte Carlo (MCMC) grapple with issues such as scalability and hyperparameter dependence. In addressing these issues, our research focuses on the fundamental elements of BNNs, in particular perceptrons and their predictive capabilities. We introduce a new perspective on the closed-form solution for backward-pass computation for the Bayesian perceptron and prove that the state-of-the-art solution is equivalent to statistical linearization. To assess the efficacy of Bayesian perceptrons and provide insights into their performance in distinct input space regions, a novel methodology utilizing k-d trees as a space partitioning method is introduced to evaluate prediction quality within specific input space regions.

关键词： Training Systematics Monte Carlo methods Scalability Neural networks Estimation Predictive models Nonhomogeneous media Bayes methods Testing

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Ultrafast dynamics in perovskite-based optoelectronic devices

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Cell Reports Physical Science 2023年第9期4卷

作者： Gong, Shaokuan Huang, Yuling Yu, Xuemeng Hu, Qiushi Liu, Jingjing Meng, Jiazhi Wen, Yifan Chen, Xihan Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems SUSTech Energy Institute for Carbon Neutrality Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen 518055 China

Halide-perovskite-based materials are rising stars for optoelectronic applications. The key for optoelectronic device development is the photogenerated carrier dynamics, which directly describes the motion of photogenerated electrons and holes. A fundamental understanding of the carrier dynamics can help researchers gain deeper insight into the light-matter interaction. The generation and movement of charge carriers can be on the order of femto- and pico-second (ultrafast) timescales, which pose challenges for detection methods. Recent development in transient spectroscopic techniques enable the understanding of charge-carrier dynamics. In this perspective, we introduce several ultrafast spectroscopic methods that investigate total carrier dynamics, hot-carrier cooling dynamics, interfacial carrier transport dynamics, and carrier spin relaxation dynamics in perovskite optoelectronics. Through these investigations, we provide our perspective on the future development of perovskite-based optoelectronics. © 2023 The Author(s)

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Accessibility-Aware Reinforcement Learning for Inclusive Robotic Navigation

Accessibility-Aware Reinforcement Learning for Inclusive Rob...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Mengsha Hu Yunhuan Wu Yibei Guo Yi Wu Chao Shi Lei Xu Rui Liu Department of Computer Science Kent State University Kent OH USA Robotics Institute Carnegie Mellon University Pittsburgh PA USA Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Informatique et Sciences du Numérique (Computer Research Laboratory) CentraleSupélec Paris-Saclay University Gif-sur-Yvette France Department of Systems Science and Industrial Engineering Binghamton University Binghamton NY USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Robotic navigation aids humans in essential scenarios, including airport customer boarding, commercial meeting receptions, and disaster site navigation. While individuals benefit from robotic services, these services pose accessibility challenges for people with disabilities. Robots may face limitations in mobility, vision, hearing, and cognitive reasoning, hindering disabled individuals' access to these services. As robotic services become increasingly prevalent, ensuring they are inclusive and accessible is crucial, accommodating human disabilities with adaptations such as slower movements, standing support, and hazard detection. To address this issue, this study introduces a novel Accessibility-Aware Reinforcement Learning model (ARL). ARL extracts disability-related information from human behavioral observations using a contextembedding neural network. It then adjusts robot motions to provide inclusive assistance to individuals with disabilities. To assess the effectiveness of this approach, four assisting services in navigation scenarios ('lead', 'wait', 'assist', and 'protect') and three application scenarios involving navigating elderly individuals in a museum, through traffic, and across a building patio were designed. The results validate ARL's efficacy in enabling robots to make accessibility-aware decisions, thereby enhancing their support for individuals with disabilities.

关键词： Robot motion Navigation Neural networks Reinforcement learning Feature extraction People with disabilities Robot learning Data mining Older adults Robots

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A DDOS Attack Traffic Classification Model for Industrial Internet Based on CNN-LSTM

A DDOS Attack Traffic Classification Model for Industrial In...

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2022 Chinese Automation Congress, CAC 2022

作者： Wei, Weixuan Liu, Xianda Sheng, Chuan Feng, Ansong Shenyang University of Chemical Technology College of Information Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665465335

With the continuous development and application of industrial Internet, the weakness of the security defense capability of industrial Internet has become increasingly prominent. This paper presents an end-to-end classification model combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network for DDoS attacks over industrial Internet. By self-learning the spatial and temporal characteristics of the original attack traffic, the model can avoid subjective errors that may occur due to manual selection of features. This method is a feasible means, with an accuracy rate of 99%. © 2022 IEEE.

关键词： Long short-term memory

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Determination of the Characteristics of Gears of Robot-Like systems by Analytical Description of their Structure

Determination of the Characteristics of Gears of Robot-Like ...

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

作者： Stefan Landler Raúl Molina Blanco Michael Otto Birgit Vogel-Heuser Markus Zimmermann Karsten Stahl Technical University of Munich Germany TUM School of Engineering and Design Department of Mechanical Engineering Munich Institute of Robotics and Machine Intelligence (MIRMI) Institute of Machine Elements Gear Research Center (FZG) Institute of Automation and Information Systems (AIS) Laboratory for Product Development and Lightweight Design (LPL)

The axes of robots and robot-like systems (RLS) usually include e-motor-gearbox-arrangements for optimal connection of the elements. The characteristics of the drive system and thus also of the robot depend strongly on the gears. Different gearbox designs are available which differ in stiffness, efficiency and further properties. For an application-optimal design of RLS a uniform documentation and a comparability of gearbox concepts is a decisive factor. The application-optimal design is supported by an interdisciplinary approach between mechanical engineering and software design, guided by adequate product development methodology. The quite heterogeneous characterization of gearboxes for RLS which is currently the state of the art is a relevant obstacle in the flexible and optimal design of RLS. The paper shows the analysis of the gear structure with unified symbols for specific machine elements and contact types. The introduced method gives insight into the mechanical structure of the gearboxes. Similarities between gear types can thus be revealed. This also enables the classification of new developments in the state of the art. Moreover, the developed method for analyzing the gear structure can be used to determine the characteristics of gears. Examples for these characteristics are backlash, efficiency or stiffness. Specifically, the stiffness of gears can be synthesized by the force action of individual contacts and the individual phenomena that occur with them. The representation by individual phenomena also makes it possible to extend the calculation to include influencing parameters such as temperature that have not been sufficiently taken into account so far.

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Optical Tactile Sensing for Aerial Multi-Contact Interaction: Design, Integration, and Evaluation

arXiv

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arXiv 2024年

作者： Aucone, Emanuele Sferrazza, Carmelo Gregor, Manuel D'Andrea, Raffaello Mintchev, Stefano The Environmental Robotics Laboratory Dept. of Environmental Systems Science ETH Zürich Zürich8092 Switzerland Birmensdorf8903 Switzerland The Robot Learning Lab UC Berkeley BerkeleyCA94704 United States The Institute for Dynamic Systems and Control Dept. of Mechanical and Process Engineering ETH Zürich Zürich8092 Switzerland

Distributed tactile sensing for multi-force detection is crucial for various aerial robot interaction tasks. However, current contact sensing solutions on drones only exploit single end-effector sensors and cannot provide distributed multi-contact sensing. Designed to be easily mounted at the bottom of a drone, we propose an optical tactile sensor that features a large and curved soft sensing surface, a hollow structure and a new illumination system. Even when spaced only 2 cm apart, multiple contacts can be detected simultaneously using our software pipeline, which provides real-world quantities of 3D contact locations (mm) and 3D force vectors (N), with an accuracy of 1.5 mm and 0.17 N respectively. We demonstrate the sensor’s applicability and reliability onboard and in real-time with two demos related to i) the estimation of the compliance of different perches and subsequent re-alignment and landing on the stiffer one, and ii) the mapping of sparse obstacles. The implementation of our distributed tactile sensor represents a significant step towards attaining the full potential of drones as versatile robots capable of interacting with and navigating within complex environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Drones

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A new structure of end-effector traction upper limb rehabilitation robot

A new structure of end-effector traction upper limb rehabili...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Li, Liaoyuan Han, Jianhai Li, Xiangpan Guo, Bingjing Xia, Pengpeng Du, Ganqin Henan University of Science and Technology Key Laboratory of Robotics and Intelligent Systems School of Mechanical and Electrical Engineering Luoyang471000 China First Affiliated Hospital of Henan University of Science and Technology Department of Neurology Luoyang471000 China

ISBN: (纸本)9781665436786

Among the rehabilitation therapies for patients with stroke or paralysis, robot-assisted rehabilitation is a research hotspot now. However, there are not many cases of clinical application, mainly because of the complex structure, single function, or high cost. This research proposes a new type of 3-DOF (degrees of freedom) upper limb rehabilitation robot in series structure of with an end-effector, which has 2 degrees of freedom in horizontal rotation and 1 degree of freedom in the vertical movement. It has a compact structure, saves space, is easy to be moved and can realize rehabilitation training in three-dimensional space. It is mainly used for rehabilitation training of the shoulder and elbow joints of the upper limbs. The first two rotary joints are driven by AC servo motors, and the prismatic joint is driven by a single-acting cylinder, which increases the passive compliance of the robot. In addition, a three-dimensional force sensor is added to expand the human-machine interaction channel and increase the active safety of the robot. Through the trajectory tracking and force control test of the robot, it is proved that the device can meet the demand of passive and active upper limb rehabilitation. © 2021 IEEE.

关键词： End effectors

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A 3D Deep Learning Approach for Classification of Gait Abnormalities Using Microsoft Kinect V2 Sensor

A 3D Deep Learning Approach for Classification of Gait Abnor...

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International CSI Computer Conference, CSICC

作者： Milad Shoryabi Ali Foroutannia Alireza Rowhanimanesh Intelligent Systems and Robotics Laboratory University of Neyshabur Neyshabur Iran Neural Engineering Laboratory University of Neyshabur Neyshabur Iran

In this paper, a deep learning approach is proposed based on a 3D Convolutional Neural Network for the classification of gait abnormalities. Six gait classes are considered, including Trendelenburg, Steppage, Stiff-legged, Lurching, and Antalgic gait abnormalities as well as normal gait. The proposed scheme is applied to a recently-published dataset from the literature. This dataset consists of the gait data recorded by multiple Microsoft Kinect v2 sensor from 25 joints of a person during walking on a specified walkway. In this dataset, for each of the 6 gait classes, ten people have attended the data collection procedure; and for each participant, 120 walking instances have been recorded. Each instance includes the spatial and temporal information of the walking, and it is converted to two 3D images, which respectively display the changes of the Coronal (X-Z) and Sagittal (Y-Z) views of the originally captured data over time. These two 3D images are used as the input of the proposed 3D convolutional neural network. There are a total of 14400 3D images in this dataset. In order to demonstrate the accuracy of the proposed approach, it is compared with four well-known neural classifiers from the literature.

关键词： Legged locomotion Deep learning Neurology Musculoskeletal system Three-dimensional displays Data collection Genetics

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Numerical study on the flow field for deep-sea hydrothermal plume tracking simulation

Numerical study on the flow field for deep-sea hydrothermal ...

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17th Asian Congress of Fluid Mechanics (ACFM 2023)

作者： X. -H. Xu J. -C. Liu H. -R. Hou Q. -H. Meng Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin 300072 People's Republic of China Laboratory for High-speed Aerodynamics Department of Mechanics Tianjin University Tianjin 300072 People's Republic of China

In this paper, the distribution of hydrothermal buoyancy plume in the deep-sea environment is studied numerically. Submarine hydrothermal vents are prominent manifestations of hydrothermal activities and are of great scientific and engineering importance. It is an important basis to obtain the temporal and spatial distribution information of different hydrothermal plumes for tracking and locating the source of hydrothermal plumes. Based on Reynolds-averaged Navier-Stokes equations and Realizable k- ε turbulence model, the plumes of hydrothermal vents at depths of 800 m, 1200 m, and 1400 m were calculated and studied to obtain the spatial distribution of variables such as velocity field, temperature field and turbulent kinetic energy. The research results of this paper provide an important basis for the simulation of deep-sea hydrothermal plume tracking based on autonomous underwater vehicles.

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Stability Analysis and Performance Evaluation of Delayed Bilateral Telerobotic systems over a Lossy Communication Channel

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Journal of systems Science & Complexity 2021年第1期34卷 157-179页

作者： BAKHSHI Ali ALFI Alireza TALEBI Heidar Ali SOURATGAR Amir Aboulfazl YOUSEFI Mahdi Faculty of Electrical and Robotic Engineering Shahrood University of TechnologyShahrood 36199-95161Iran Power Plants Control Systems Technology Development Plan Department of monitoring and ControlNiroo Research InstituteTehran 14665-517Iran Real-Time and Robotics system Laboratory Faculty of Electrical EngineeringAmirkabir University of TechnologyHafez AvenueTehran 15914Iran Electrical and Computer Engineering Department The University of British Columbia2385East MallVan couverBC V6T 1Z4Canada

The focus of this paper is to address a novel control technique for stability and transparency analysis of bilateral telerobotic systems in the presence of data loss and time delay in the communication channel. Different control strategies have been reported to compensate the effects of time delay in the communication channel;however, most of them result in poor performance under data loss. First, a model for data loss is proposed using a finite series representation of a set of periodic continuous *** improve the performance and data reconstruction, a holder circuits is also introduced. The passivity of the overall system is provided via the wave variable technique based on the proposed model for the data loss. The stability analysis of the system is then derived using the Lyapunov theorem under the time delay and the data loss. Finally, experimental results are given to illustrate the capability of the proposed control technique.

关键词： Lossy channel passivity stability telerobotic transparency

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