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Droplet Probe for Characterization of Advancing and Receding Contact Angles of Single Fibers

Droplet Probe for Characterization of Advancing and Receding...

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international conference on Manipulation, automation and robotics at Small Scales (MARSS)

作者： Arthur Vieira Maja Vuckovac Inge Schlapp-Hackl Michael Hummel Quan Zhou Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Department of Applied Physics School of Science Aalto University Espoo Finland Department of Bioproducts and Biosystems School of Chemical Engineering Aalto University Aalto Finland

Characterizing the wetting properties of fibers is crucial for many research and industry applications, including textiles for water-oil separation and composite materials. Those fibers are often soft, typically tens of micrometers in diameter but millimeters in length, making manipulation and characterization difficult. Contact angles of single fibers are usually determined by droplet shape analysis or force-based Wilhelmy method. However, these methods are unable to accurately measure contact angles above $60^{\circ}$ or ensure reliable control of the liquid-fiber interaction process, especially for soft fibers prone to bending. Consequently, reliable characterization of the advancing and receding contact angles of single fibers remains a challenge. Here we report a novel method for characterizing the advancing and receding contact angles of both soft and rigid single fibers using a millimeter-sized droplet probe affixed to a disk and a numerical model of the system. By analyzing side-view images, we extract key geometrical parameters of the disk-droplet-fiber system, which, when used in detailed simulations, allows estimating the contact angle of fibers ranging from $20^\circ$ to $140^\circ$ . We applied this method to characterize three distinct micro-fibers: a highly hydrophilic rigid borosilicate glass fiber, a mildly hydrophilic soft PET fiber, and a rigid hydrophobic tungsten wire coated with a commercial super-repellent coating.

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Human-in-the-loop Optimization for Adaptive Assist-as-Needed Rehabilitation 12

Human-in-the-loop Optimization for Adaptive Assist-as-Needed...

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12th international conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2022

作者： Li, Ning Yang, Yang Yang, Tie Chen, Wenyuan Wang, Yihan Yu, Peng Wang, Wenxue Xi, Ning Liu, Lianqing Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of the Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang10016 China School of Electrical and Electronics Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Emerging Technologies Institute Department of Industrial and Manufacturing Systems Engineering University of Hong Kong Pokfulam999077 Hong Kong

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Active voluntary participation in robot-assisted rehabilitation promote the recovery of stroke hemiplegia. However, different patients have personalized recovery states, which needs the adaptive control strategy to improve the patient's participation. Therefore, in this study, a Mirror Adaptive Assist-As-Needed (MAAN) scheme is proposed to encourage subjects to participate actively and avoid the slack of the subjects including two modules: Functional Capability Estimation (FCE) and Mirror Adaptation (MAD). Specifically, the first MSFE module can obtain joint functional capability based on fused biological and motional signals. The second MAD module acquires the needed assistance via the estimated torques of FCE based on healthy limb. Through the iteration and collaboration of these two modules, a human-in-the-loop adaptive training is achieved to promote the patient participation. The results demonstrate that the MAAN strategy can generate suitable assistance for the patients to correct abnormal muscle activation patterns and promote active voluntary participation. Compared with other methods, the proposed strategy improves motion stability and consistency with the natural physiological state of healthy limbs. This research can be expected to greatly enhance the rehabilitation and power assistance outcome of exoskeleton robot in more practical applications. © 2022 IEEE.

关键词： Mirrors

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Reinforcement Learning-based Fault-Tolerant Control for Unmanned Aerial Vehicles

Reinforcement Learning-based Fault-Tolerant Control for Unma...

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Youth Academic Annual conference of Chinese Association of automation (YAC)

作者： Guoqi Wang Xudong Wang Yang Li Ziwei Gao Zhaoke Ning National Engineering Research Center of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle Engineering Hunan University Changsha China School of Aeronautics and Astronautics Sichuan University Chengdu China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This paper focuses on the fault-tolerant control (FTC) problem for unmanned aerial vehicles (UAVs) subject to possible multiple actuator failures, which is a tough problem to solve with traditional FTC methods since they usually require accurate mathematical models. To address the limitation of traditional FTC methods, a model-free FTC approach is proposed based on reinforcement learning (RL). Subsequently, the proposed approach is applied to construct fault-tolerant controller for UAVs without any knowledge of the quadrotor dynamic information. Then, an end-to-end control policy that can tolerant actuator failures is obtained, which can map the state of the UAVs directly to the control commands of the four rotors after learning. Finally, the effectiveness of the proposed fault-tolerant approach is demonstrated by using the flexible modular quadrotor simulator.

关键词： Fault tolerance Actuators Trajectory tracking Fault tolerant systems Autonomous aerial vehicles Stability analysis Mathematical models

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An Embedding Algorithm Based on Neighborhood Aggregation for Attribute Graphs

An Embedding Algorithm Based on Neighborhood Aggregation for...

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international conference on Computer and automation Engineering, ICCAE

作者： Dongming Chen Shuyue Zhang Yumeng Zhao Mingzhao Xie Dongqi Wang Software College Northeastern University Shenyang China

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Attribute graph embedding algorithms based on graph neural networks aggregate information about the neighborhood of a target node to update the embedding of the target node, i.e. neighborhood aggregation. The shallow network architecture of existing models limits the generalization ability of the models on large datasets, and the number of nodes increases exponentially with the number of graph neural network layers, resulting in gradient explosion and the need for faster model training methods. In addition, the multiple iteration process of neighborhood aggregation changes the original attribute information of the nodes, especially when the attributes of the target node and the attribute information of the neighborhood nodes are extremely different, which makes the target node attributes deviate significantly from the original attributes. Excessive neighborhood aggregation mixes different clustered nodes, causing all nodes to converge to the same embedding, resulting in excessive smoothing. In order to solve the above problems while considering the supervised learning of the model and the utility of neighborhood aggregation, this paper proposes a subgraph sampling method based on graph sampling for attribute graph data pre-processing, and an attribute graph embedding algorithm for improving neighborhood aggregation using an adaptive learning strategy.

关键词： Training Adaptive learning Smoothing methods Supervised learning Network architecture Sampling methods Graph neural networks Explosions Data models Classification algorithms

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Synthetic Data Generation for the Enrichment of Civil Engineering Machine Data

Synthetic Data Generation for the Enrichment of Civil Engine...

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international conference on Construction Logistics, Equipment, and robotics, CLEaR 2023

作者： Krüger, Marius Vogel-Heuser, Birgit Hujo, Dominik Walch, Johanna Prinz, Theresa Pohl, Daniel Cha, Suhyun Kerausch, Cornelia Institute of Automation and Information Systems School of Engineering and Design Technical University of Munich Munich Germany Chair of Ergonomics School of Engineering and Design Technical University of Munich Munich Germany Sensor-Technik Wiedemann GmbH Am Bärenwald 6 Kaufbeuren87600 Germany HAWE Hydraulik SE Einsteinring 17 Munich Aschheim85609 Germany BAUER Maschinen GmbH BAUER-Straße 1 Schrobenhausen86529 Germany

ISBN: (纸本)9783031440205

Artificial Intelligence (AI) is one of the most auspicious technologies in the mobile machine domain. It promises to optimize the machine operation to reduce energy consumption or provide an assistant function to support the operator in challenging machine movements. A large amount of machine data is required to train and build AI models. These data sets are often not available due to missing or faulty sensors in the machine. However, construction machines are partly equipped with temporary sensors for data collection so that small data sets are available. Nevertheless, these data sets are very small and must be extended with more realistic data. Generating synthetic data to enrich real data is a promising approach to overcome the obstacle of small data sets. This paper presents a data generator to produce synthetic, physically-informed data for the pendulum trajectory of a flexible attachment tool on a construction machine. The data generator calculates a reference trajectory based on a physical model of the machine. This reference trajectory is generated by solving an optimization problem to cover the machine movement that an experienced machine operator would drive. Reasonable deviations of these trajectories are generated by varying machine characteristics and adding external forces to the physical model to simulate rough environmental conditions. The data generator is implemented for the grab system movement of a civil engineering machine. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Trajectories

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Development of robotic fingers using jamming effect

Development of robotic fingers using jamming effect

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international conference on Control, automation and Systems ( ICCAS)

作者： Naoya Sugishita Shunji Hanakago Hideo Furuhashi Department of Electrical Engineering Aichi Institute of Technology Toyota Japan

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

This study focused on jamming transition, one of the methods in soft robotics, and developed a robotic finger that imitates a human finger. By combining wire drive and jamming transition, we developed a highly versatile robotic finger that can grasp objects with a wider range of shapes than can be grasped with the conventional gripper type. Wire drive has the property of deforming when an external force is applied, while jamming transition has the property of hardening. By combining these, it is possible to grasp objects without changing their shape even when an external force is applied. The developed jamming finger can hold thin objects such as pens with a single finger. Operation verification of movements such as pushing a small object against the fingertip and picking it up was also carried out, showing the developed finger to have sufficient grasping ability. Differences in retention between different film thicknesses and filling rates are also shown. To achieve improved versatility, several of these jamming fingers were combined to fabricate a multi-finger system. Experiments showed that the multi-finger system can hold objects of shapes that cannot be grasped by the general gripper type, which validates the versatility of the system.

关键词： Shape Fingers Force Grasping Soft robotics Control systems Filling Wire Jamming Grippers

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Robust and Remote Center of Cyclic Motion Control for Redundant Robots with Partially Unknown Structure

Robust and Remote Center of Cyclic Motion Control for Redund...

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IEEE international conference on robotics and automation (ICRA)

作者： Long Jin Kun Liu Mei Liu School of Information Science and Engineering Lanzhou University Lanzhou China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Remote center of motion (RCM) describes a robot with a rod-like end-effector operating through a hole in the interface separating the internal space from the external space. Considering that the control of RCM may be influenced by perturbations (noises) and that the end-effector is frequently replaced to complete different tasks, the structural information related to the robot manipulator and its rod-like end-effector may contain errors. This paper proposes an acceleration-level remote center of cyclic motion (ARC 2 M) control scheme, which takes into account the cyclic motion index and the physical limitations of robot manipulators to achieve repetitive motion planning and RCM control at the acceleration level. Additionally, a parameter calculation method is proposed to compute unknown parameters of the end-effector under the influence of noise. Kalman filter and a neural dynamics-based method are employed to address noises effects, and related theoretical analyses are given. To validate the proposed ARC 2 M scheme, simulations and physical experiments are carried out. The source code is available at https://***/LongJin-lab/ARCM.

关键词： Trajectory tracking Source coding Perturbation methods Noise Aerospace electronics End effectors Real-time systems

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Semantic Interpretation and Validation of Graph Attention-Based Explanations for GNN models

Semantic Interpretation and Validation of Graph Attention-Ba...

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international conference on Advanced robotics (ICAR)

作者： Efimia Panagiotaki Daniele De Martini Lars Kunze Department of Engineering Science Cognitive Robotics Group University of Oxford UK Department of Engineering Science Mobile Robotics Group Oxford Robotics Institute University of Oxford UK

In this work, we propose a methodology for investigating the use of semantic attention to enhance the explainability of Graph Neural Network (GNN)-based models. Graph Deep Learning (GDL) has emerged as a promising field for tasks like scene interpretation, leveraging flexible graph structures to concisely describe complex features and relationships. As traditional explainability methods used in eXplainable AI (XAI) cannot be directly applied to such structures, graph-specific approaches are introduced. Attention has been previously employed to estimate the importance of input features in GDL, however, the fidelity of this method in generating accurate and consistent explanations has been questioned. To evaluate the validity of using attention weights as feature importance indicators, we introduce semantically-informed perturbations and correlate predicted attention weights with the accuracy of the model. Our work extends existing attention-based graph explainability methods by analysing the divergence in the attention distributions in relation to semantically sorted feature sets and the behaviour of a GNN model, efficiently estimating feature importance. We apply our methodology on a lidar pointcloud estimation model successfully identifying key semantic classes that contribute to enhanced performance, effectively generating reliable post-hoc semantic explanations.

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BERT and Word Segmentation Algorithm Research on the Relationship Between Diabetes Symptoms and Traditional Chinese Medicine 6

BERT and Word Segmentation Algorithm Research on the Relatio...

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6th international conference on Intelligent Autonomous Systems, ICoIAS 2023

作者： Tang, Zhiming Lu, Yu'e Yang, Bo Li, Lei Hao, Jianghao Shanxi University Department of Automation Taiyuan030006 China Shanxi Acupuncture Hospital Taiyuan030006 China School of Mathematical Sciences Shanxi University Taiyuan030006 China

ISBN: (纸本)9798350371253

Diabetes is a common chronic Metabolic disorder, and its treatment has always been a hot research topic in the medical field. Traditional Chinese medicine has a significant therapeutic effect on it and has become one of the important ways for patients to choose treatment methods. In recent years, with the development and application of deep learning technology, more and more research has begun to attempt to use deep learning models to process traditional Chinese medicine case data, achieving automated information extraction and analysis. This study shows that BERT model and jieba segmentation algorithm have good effect in the study of the relationship between symptoms and Medication in diabetes medical record data, which is expected to provide scientific basis and reference for clinical treatment of traditional Chinese medicine. © 2023 IEEE.

关键词： Deep learning

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Predictive World models from Real-World Partial Observations 1

Predictive World Models from Real-World Partial Observations

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1st IEEE international conference on Mobility, Operations, Services and Technologies, MOST 2023

作者： Karlsson, Robin Carballo, Alexander Fujii, Keisuke Ohtani, Kento Takeda, Kazuya Nagoya University Graduate School of Informatics Nagoya Japan Gifu University Department of Electrical Electronic and Computer Engineering Gifu Japan Nagoya University Tier Iv Graduate School of Informatics Nagoya Japan

ISBN: (纸本)9798350319958

Cognitive scientists believe adaptable intelligent agents like humans perform reasoning through learned causal mental simulations of agents and environments. The problem of learning such simulations is called predictive world modeling. Recently, reinforcement learning (RL) agents leveraging world models have achieved SOTA performance in game environments. However, understanding how to apply the world modeling approach in complex real-world environments relevant to mobile robots remains an open question. In this paper, we present a framework for learning a probabilistic predictive world model for real-world road environments. We implement the model using a hierarchical VAE (HVAE) capable of predicting a diverse set of fully observed plausible worlds from accumulated sensor observations. While prior HVAE methods require complete states as ground truth for learning, we present a novel sequential training method to allow HVAEs to learn to predict complete states from partially observed states only. We experimentally demonstrate accurate spatial structure prediction of deterministic regions achieving 96.21 IoU, and close the gap to perfect prediction by 62 % for stochastic regions using the best prediction. By extending HVAEs to cases where complete ground truth states do not exist, we facilitate continual learning of spatial prediction as a step towards realizing explainable and comprehensive predictive world models for real-world mobile robotics applications. Code is available at https://***/robin-karlsson0/predictive-world-models. © 2023 IEEE.

关键词： Mobile robots

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