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共 1220 条记录，以下是131-140 订阅

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Study of DNN Network Architecture Search for Robot Vision 8

Study of DNN Network Architecture Search for Robot Vision

引用

8th IEEE international conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Fu, Jian Wang, Qifeng School of Automation Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9798350300178

Robot vision, which integrates measurement and perception, plays an important role in robot manipulation applications. Although the current deep learning-based visual neural network models have high perception capabilities, their network structures are usually too large to be implemented on embedded devices for robot vision. In this paper, we propose neural network architecture search (NAS) that combines pre-training and pruning operations to simplify deep neural network architectures. It not only solves this problem without losing network accuracy, but also significantly alleviates the difficulties of long network computation time and redundant search space in traditional NAS methods. Finally, the experimental results show that the neural network generated by the proposed algorithm outperforms the artificially designed neural network, which demonstrates the effectiveness of the method. At the end of the paper, the rationality of the method is proved by experiments and comparisons. the performance of the new algorithm and the generated neural network is better than that of the artificially designed neural network. © 2023 IEEE.

关键词： Network architecture

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An Overview of Digital Twins methods Applied to Lithium-Ion Batteries 25

An Overview of Digital Twins Methods Applied to Lithium-Ion ...

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2022 IEEE international conference on automation/25th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2022

作者： Bugueno, Valentina Barbosa, Karina A. Rajendran, Saravanakumar Diaz, Matias Universidad de Santiago de Chile Departamento de Ingeníeria Eléctrica Santiago Chile

ISBN: (纸本)9781665494083

the continuous monitoring of the energy storage system in electric vehicles is essential for safe operation. In particular, most electric vehicles use Lithium-ion batteries due to their power and energy density, which can introduce instability in their performance. Recently, the use of digital twins has been able to provide more information to the battery management system for real-time decision-making. this paper presents an overview of the main methods for modeling lithium-ion batteries and the identification of parameters of these models, summarizing their advantages and disadvantages. In addition, the application of the linear parameter-varying representation and their contributions are discussed. Finally, digital twin methods applied to battery management systems are described, and their main functionalities are analysed. © 2022 IEEE.

关键词： Lithium-ion batteries

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FDNet: Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Fourier Series and DenseNet 5

FDNet: Remaining Useful Life Prediction for Lithium-Ion Batt...

引用

5th international conference on Machine Learning and Computer Application, ICMLCA 2024

作者： Chen, Siwen Zheng, Xiujuan Shanghai United Imaging Healthcare Co. Ltd. Shanghai China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Engineering Research Center for Metallurgical Automation and Measurement Technology Ministry of Education Wuhan China

ISBN: (纸本)9798331530334

To enhance the prediction accuracy of the remaining useful life (RUL) of lithium-ion batteries, this study proposes a novel RUL prediction model, termed FDNet, which integrates Discrete Fourier Series (DFS) and Densely Connected Convolutional Networks (DenseNet). In contrast to traditional methods that rely on one-dimensional (1D) numerical representations of time-series data, this model utilizes a two-dimensional (2D) visual representation for direct RUL prediction. First, the DFS is employed to convert 1D time-series signals into a 2D time-frequency domain, enabling the extraction of high-dimensional features from the original data. then, these 2D images are subsequently input into DenseNet, which learns the mapping between the image features and the RUL targets. Finally, the proposed model is validated using the NASA lithium-ion battery aging dataset, and the results indicate that FDNet achieves superior prediction accuracy and exhibits notable robustness. © 2024 IEEE.

关键词： Prediction models

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Simulation and Optimization of Industrial Disassembly Paths using Position Based Dynamics 26

Simulation and Optimization of Industrial Disassembly Paths ...

引用

26th international conference on methods and models in automation and robotics

作者： Hegewald, Robert Wolpert, Nicola Schoemer, Elmar Mercedes Benz AG Digital Factory & Tool Management Sindelfingen Germany Univ Appl Sci Dept Geomat Comp Sci & Math Stuttgart Germany Johannes Gutenberg Univ Mainz Dept Phys Math & Comp Sci Mainz Germany

ISBN: (数字)9781665468589

ISBN: (纸本)9781665468596;9781665468589

the finding of plausible assembly paths of industrial components is a relevant, actual, but not yet fully resolved research topic. For a fast and robust computation of such paths, rigid body sampling-based motion planning is used. To tackle the problem of unavoidable collisions due to overpressure of components or due to flexible fastening elements, the tolerance of minor collisions is necessary. Tolerating minor collisions always means the manipulation of the configuration space or the direct manipulation of objects in the workspace. these actions enable the computation of assembly paths on industrial data, but they affect (explicitly or implicitly) the shape of the objects and thus the physical plausibility of the computed paths. this makes it important to further analyze the assembly paths. In this paper, we propose a postprocessing method for industrial assembly paths that is based on Position Based Dynamics (PBD) and is able to simulate and optimize given assembly paths. We use the PBD framework to simulate and measure the object deformation along the assembly path. For the optimization of the path, we compare the deformed object to the original rigid object and we apply small corrections to the path that decrease the overall deformation. We show the effectiveness of our approach on an academic dataset that provides industrial disassembly scenarios.

关键词： automation Shape Manuals Product design Planning Joining processes Optimization

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Accessibility Framework for Determining Collisions and Coverage for Radiation Scanning 25th

Accessibility Framework for Determining Collisions and Cov...

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25th Annual conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Bettles, Joshua West, Andrew Andrew, Jeremy Darby, Iain Lennox, Barry Manchester Centre for Robotics and AI The University of Manchester ManchesterM13 9PL United Kingdom Nuclear Restoration Services Ltd. Dounreay Thurso CaithnessKW14 7TZ United Kingdom National Nuclear Laboratory Chadwick House Birchwood Park Warrington CheshireWA3 6AE United Kingdom School of Physics and Astronomy University of Glasgow GlasgowG12 8QQ United Kingdom

ISBN: (纸本)9783031720581

Radioactive contamination monitoring is an important part of radiological protection. automation of surface monitoring poses difficulties, with a major challenge being determining the coverage of a radiation probe over an object in close proximity without collision. We propose a new accessibility framework to determine if radiation probes, modelled as convex hulls, collide with 3D point clouds representing the objects. We explain how to structure and analyze point clouds to extract properties such as the surface normal for each point. Our method for approximating radiation probes is demonstrated using the BP4 probe. this approach models both the probe and the sensor’s effective scan volume with geometric primitives, providing a computationally efficient way to detect collisions with flat surfaces. the accessibility assessment builds on common methods within computer science for determining intersections. A laptop in various positions was used to demonstrate that the framework can efficiently categorise points as accessible or inaccessible, identifying unscannable regions. the output of this framework can then be used to plan collision-free paths over objects and will be the foundation of a robotic survey assistant. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Probes

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Machine Learning models for Gait Phases Detection Using Surface Electromyography Signals 17th

Machine Learning Models for Gait Phases Detection Using Surf...

引用

17th international conference on Intelligent robotics and Applications

作者： Feng, Zhiyang Jiang, Zhengxuan Liu, Huayue Wang, Wenkong Wang, Yiqi Lu, Chang Ma, Xin Li, Yibin Song, Rui Zhang, Huanghe Shandong Univ Sch Control Sci & Engn Ctr Robot Jinan Shandong Peoples R China Univ Warwick Coventry W Midlands England Heidelberg Univ Fac Med Heidelberg Germany Heidelberg Univ Hosp Inst Computat Biomed Heidelberg Germany

ISBN: (纸本)9789819607853;9789819607860

Accurate gait phase detection is a fundamental technology for wearable rehabilitation robots, enabling them to deliver effective, safe, and personalized assistance that closely aligns with the therapeutic needs and safety requirements of users. Unlike force sensors or inertial measurement units, surface electromyography (sEMG) signals establish a direct link to the wearer's motion intentions, making them ideally suited for real-time applications. this study investigates the utilization of sEMG signals to identify gait phases using three types of machine learning models: k-nearest neighbors (KNN), support vector machines (SVM), and artificial neural networks (ANN). the models are trained on a publicly available dataset (i.e., SIAT-LLMD), which consists of data from 40 healthy individuals and utilizes three training methods: holdout, 10-fold cross-validation, and leave-one-subject-out cross-validation. ANN achieved the best results, demonstrating an overall accuracy of 92.6%+/- 2.6% for detecting stance and swing phases, and 94.4%+/- 4.2% for detecting five gait sub-phases (i.e., stance flexion phase, stance extension phase, preswing phase, swing flexion phase, and swing extension phase).

关键词： EMG signals Gait phase detection Machine learning Artificial neural networks

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PLAN-SEQ-LEARN: LANGUAGE MODEL GUIDED RL FOR SOLVING LONG HORIZON robotics TASKS 12

PLAN-SEQ-LEARN: LANGUAGE MODEL GUIDED RL FOR SOLVING LONG HO...

引用

12th international conference on Learning Representations, ICLR 2024

作者： Dalal, Murtaza Chiruvolu, Tarun Chaplot, Devendra Singh Salakhutdinov, Ruslan Carnegie Mellon University United States Mistral AI France

Large Language models (LLMs) have been shown to be capable of performing high-level planning for long-horizon robotics tasks, yet existing methods require access to a pre-defined skill library (e.g. picking, placing, pulling, pushing, navigating). However, LLM planning does not address how to design or learn those behaviors, which remains challenging particularly in long-horizon settings. Furthermore, for many tasks of interest, the robot needs to be able to adjust its behavior in a fine-grained manner, requiring the agent to be capable of modifying low-level control actions. Can we instead use the internet-scale knowledge from LLMs for high-level policies, guiding reinforcement learning (RL) policies to efficiently solve robotic control tasks online without requiring a pre-determined set of skills? In this paper, we propose Plan-Seq-Learn (PSL): a modular approach that uses motion planning to bridge the gap between abstract language and learned low-level control for solving long-horizon robotics tasks from scratch. We demonstrate that PSL achieves state-of-the-art results on over 25 challenging robotics tasks with up to 10 stages. PSL solves long-horizon tasks from raw visual input spanning four benchmarks at success rates of over 85%, out-performing language-based, classical, and end-to-end approaches. Video results and code at https://***/. © 2024 12th international conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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Simulated Annealing-based Energy Efficient Route Planning for Urban Service Robots 26

Simulated Annealing-based Energy Efficient Route Planning fo...

引用

26th international conference on methods and models in automation and robotics

作者： Braeutigam, Jacob Gupta, Abhishek Goehlich, Dietmar Tech Univ Berlin Fac Elect Engn & Comp Sci Berlin Germany Tech Univ Berlin Fac Methods Prod Dev & Mechatron Berlin Germany

ISBN: (数字)9781665468589

ISBN: (纸本)9781665468596;9781665468589

Product development and operational methods of autonomous robot MURMEL (Mobiler Urbaner Roboter zur MullEimer Leerung) for smart waste management focuses on all three aspects of sustainability. the robot autonomously empties the dustbins on the streets of Berlin, and concentrates on social sustainability by reducing the impact on the health of the workers. As one of the approaches towards efficient ecological and economical sustainability, MURMEL has its own route planning method delivered from an operation management model. For the operation management, the dustbins are allocated waypoints to the robot. In this paper, a cost function considering energy consumption is optimized in order to improve the robot's global route planning. By conducting numerous experiments with varying algorithm parameter values, the specific weightage of route permutation operators and the initial temperature for a simulated annealing method with nearest neighbour approach was narrowed down to a well-performing set. For an implementation in an actual existing park in Berlin for operation scenario, our simulation results show an average overall reduction of 11% energy consumption as compared to results from a nearest neighbour heuristic.

关键词： Urban service robots Robot-operation management model Robot route planning Simulated annealing Autonomous sustainable waste management model

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Machine Learning for Self-Calibration Parameters of Data-Driven models: Case Study of an Integrated Maintenance Digital Platform 26

Machine Learning for Self-Calibration Parameters of Data-Dri...

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26th international conference on methods and models in automation and robotics

作者： Szpytko, Janusz Salgado Duarte, Yorlandys Millares Barthelemy, Lazar Ramon AGH Univ Sci & Technol Fac Mech Engn & Robot Krakow Poland CUJAE Univ Tecnol La Habana Jose Antonio Echeverr Fac Elect Engn Havana Cuba

ISBN: (数字)9781665468589

ISBN: (纸本)9781665468596;9781665468589

A well-established problem always in the process of methodological improvement is the coordination of generator maintenance scheduling in Power Systems. Usually, the sources of changes and improvements are the wide range of conditions and singularities of Power Systems, such as structure, needs, technologies, resources, information, etc. the risk-oriented approach is one of the most accepted criteria to address this well-defined problem. However, there are hidden challenges of data-calibration-modeling and technology-integration when applying this approach. One of the most controversial is the calibration of model parameters and variables in the evaluated scenario, as they depend on historical data. In this paper, to address this controversial hidden challenge, we propose to use machine learning for on-line calibration by introducing smart layers based on comprehensive human diagrams that filter and analyze historical data collected through monitoring systems and make all variables and parameters of the risk model ready for use. In particular, we focus the attention on one of the model components, the load modeling. this proposed organic connection ensures a feasible and practical solution to be used in a real system because it addresses a potential challenge in technology integration.

关键词： Data-driven model Monte Carlo Simulation Machine Learning Power Systems

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Satellite Remote Sensing Application Model Parallel Training Technology Research

Satellite Remote Sensing Application Model Parallel Training...

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robotics, Control and automation (ICRCA), international conference on

作者： Xuebo Zhang National Key Laboratory of Intelligent Spatial Information College of Aerospace Information Aerospace Engineering University Beijing China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

In response to the long training time of satellite remote sensing application models, this paper first introduces the main methods of parallel training, as well as the support provided by PyTorch for parallel training. Based on this, the target detection model YOLOv4 and the U-Net-based land-sea segmentation model were trained in parallel, and the experiments showed that the training time of both types of models was significantly reduced.

关键词： Training Satellites automation Training data Object detection Parallel processing Data models Remote sensing Robots

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