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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

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Reinforcement Learning with Large Language Model for Hybrid Disassembly Lines in Remanufacturing Contexts

Reinforcement Learning with Large Language Model for Hybrid ...

引用

IEEE international conference on automation Science and Engineering (CASE)

作者： Peng Ji Xi Wang Guo Jiacun Wang Weitian Wang Shu Jin Qin Ying Tang Qi Kang College of Information and Control Engineering Liaoning Petrochemical University Fushun China Dept of Comp. Sci. and Soft. Eng. Monmouth University W. Long Branch NJ USA Collaborative Robotics and Smart Systems Laboratory (CRoSS Lab) Montclair State University Montclair NJ USA College of Economics and Management Shangqiu Normal University Shangqiu China Electrical and Computer Engineering Rowan University Glassboro New Jersey USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Large language models (LLM), ChatGPT is making substantial impact across various fields. This study for the first time presents a novel approach to the hybrid disassembly line balancing problem using LLM and reinforcement learning algorithms in remanufacturing contexts. The problem is divided into two sub-stages. LLM is innovatively used to capture a disassembly sequence well in the first stage, while reinforcement learning is utilized to address the problem in the second stage. Upon comparing the performance with and without LLM, the proposed approach significantly reduces the trial-and-error space and achieves faster convergence to achieve the desired solution. Future work of this study is also discussed.

关键词： Computer aided software engineering automation Large language models Reinforcement learning Chatbots Convergence

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A Container Cloud Elastic Scaling Method Based on GRU Attention Mechanism

A Container Cloud Elastic Scaling Method Based on GRU Attent...

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Computer Engineering and Intelligent Control (ICCEIC) international conference on

作者： Yue Zhang Yong Sun Chunhe Song Pengpei Gao State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

In modern container cloud computing environments, achieving efficient elastic scaling is crucial to optimizing resource utilization and improving system performance. This paper proposes a new prediction model based on gated recurrent unit (GRU) and attention mechanism for load prediction and elastic scaling strategy optimization. The model uses the time series processing capability of GRU combined with the optimization characteristics of the attention mechanism to improve the accuracy and real-time performance of load prediction. Experimental results show that compared with traditional methods, the model can more accurately capture the load change trend and achieve more agile and efficient resource scheduling and management, thereby effectively reducing resource waste and improving service quality. This study provides a novel and effective solution for elastic scaling in container cloud environments.

关键词： Cloud computing Attention mechanisms Computational modeling System performance Time series analysis Containers Predictive models Resource management Optimization Load modeling

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Multi-Confidence Guided Source-Free Domain Adaption Method for Point Cloud Primitive Segmentation

Multi-Confidence Guided Source-Free Domain Adaption Method f...

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IEEE international conference on robotics and automation (ICRA)

作者： Shaohu Wang Yuchuang Tong Xiuqin Shang Zhengtao Zhang Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Science Beijing China CAS Engineering Laboratory for Intelligent Industrial Vision Beijing China Binzhou Institute of Technology Binzhou City China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Point cloud primitive segmentation aims to segment the surface point cloud into various geometric types of primitives, which plays a vital role in robot operation and industrial automation. However, differences in object structures and shapes across industrial datasets create domain shift issues, compounded by privacy concerns preventing dataset sharing. To address these challenges, we propose a novel source-free domain adaptation method for point cloud primitive segmentation, which follows the popular pseudo-label based self-training framework. Unlike previous works using single-model uncertainty to refine pseudo labels, our method leverages multi-confidence, including transformation consistency, task confidence, and geometric saliency to provide more informative guidance. Specifically, the transformation consistency is first utilized to vote pseudo-labels and task confidences. Furthermore, to filter out high-confident noises and obtain more reliable pseudo-labels, we investigate the geometric curvature properties of primitives and propose a geometric saliency guided dynamic prototype matching and label graph aggregation strategies for pseudo-label reassignment with different task confidence. For this novel task, we construct several datasets and verify the effectiveness of the proposed methods through a series of experiments.

关键词： Point cloud compression Privacy automation Uncertainty Shape Service robots Noise

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A Feature Extraction Algorithm for Hybrid Manufacturing and Its Application in Robot-Based Additive and Subtractive Processes

A Feature Extraction Algorithm for Hybrid Manufacturing and ...

引用

international conference on robotics and Artificial Intelligence (ICRAI)

作者： Owen Cooke Hamdan Al-Musaibeli Rafiq Ahmad Department of Mechanical Engineering Smart & Sustainable Manufacturing Systems Laboratory (SMART LAB) University of Alberta Edmonton Canada

Establishing efficient, non-traditional manufacturing methods is critical for achieving a circular economy. Remanufacturing, the process in which a part is modified to restore original or like-new functionality, can improve manufacturing sustainability by closing the supply chain loop. A critical remanufacturing technique is hybrid manufacturing: combined additive and subtractive manufacturing processes that enable features to be added or removed from an existing part. This paper proposes a method for automating the feature extraction process for hybrid manufacturing, which is otherwise labor-intensive. The feature extraction is performed between a CAD model of the desired part and a 3D scan of the actual part to be remanufactured. The extracted features are then classified for the optimal manufacturing process and exported as STL files, which can be utilized as inputs for various tool path planning algorithms. An experiment is performed on an end-of-life case study to validate the proposed methodology. Additionally, simulations of the additive and subtractive remanufacturing processes are included to demonstrate one potential application of the proposed feature extraction process.

关键词： Solid modeling Visualization Manufacturing processes Additives Three-dimensional displays automation Feature extraction

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Complementing Onboard Sensors with Satellite Maps: A New Perspective for HD Map Construction

Complementing Onboard Sensors with Satellite Maps: A New Per...

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IEEE international conference on robotics and automation (ICRA)

作者： Wenjie Gao Jiawei Fu Yanqing Shen Haodong Jing Shitao Chen Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi P.R. China The Chinese University of Hong Kong Shatin Hong Kong

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

High-definition (HD) maps play a crucial role in autonomous driving systems. Recent methods have attempted to construct HD maps in real-time using vehicle onboard sensors. Due to the inherent limitations of onboard sensors, which include sensitivity to detection range and susceptibility to occlusion by nearby vehicles, the performance of these methods significantly declines in complex scenarios and long-range detection tasks. In this paper, we explore a new perspective that boosts HD map construction through the use of satellite maps to complement onboard sensors. We initially generate the satellite map tiles for each sample in nuScenes and release a complementary dataset for further research. To enable better integration of satellite maps with existing methods, we propose a hierarchical fusion module, which includes feature-level fusion and BEV-level fusion. The feature-level fusion, composed of a mask generator and a masked cross-attention mechanism, is used to refine the features from onboard sensors. The BEV-level fusion mitigates the coordinate differences between features obtained from onboard sensors and satellite maps through an alignment module. The experimental results on the augmented nuScenes showcase the seamless integration of our module into three existing HD map construction methods. The satellite maps and our proposed module notably enhance their performance in both HD map semantic segmentation and instance detection tasks. Our code will be available at https://***/xjtu-csgao/SatforHDMap.

关键词： Cloud computing Satellites Sensitivity Tiles Semantic segmentation Sensor phenomena and characterization Sensor fusion

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Trust Mechanisms in Decentralized Federated Learning: Enhancing Model Parameter Aggregation

Trust Mechanisms in Decentralized Federated Learning: Enhanc...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Xinyi Fan Jielei Chu Zhaoyu Li Jiangtao Hu Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China CREC R&D Center for Carbon Peaking and Carbon Neutrality China Railway Eryuan Engineering Group Co. Ltd. Chengdu China Sichuan International Travel Healthcare Center Chengdu Customs Chengdu China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

With the increasing demand for data privacy protection and the enhanced computational capabilities of edge devices, decentralized federated learning (DFL) has emerged as a key paradigm for distributed training. Unlike traditional centralized federated learning, DFL enables peer-to-peer communication for exchanging model parameters without relying on a central server. However, the presence of heterogeneous (non-IID) data often leads to performance degradation due to discrepancies in data distribution across different clients. Moreover, effectively controlling the progress and quality of model aggregation across nodes poses a significant challenge in DFL. To address these issues, we propose a novel framework, called Decentralized Federated Learning with Adaptive Trust and Consensus (DFL-ATC). This framework leverages a trust mechanism that assigns dynamic weights to the contributions of neighboring models by evaluating the feature outputs between clients. Additionally, we design an exponential scheduler to adjust the consensus step size, achieving a balance between model aggregation and handling data heterogeneity. Extensive simulations demonstrate that DFL-ATC achieves significant accuracy improvements compared to commonly used federated learning algorithms such as FedAvg and decentralized algorithms like D-PSGD.

关键词： Training Performance evaluation Degradation Adaptation models Data privacy Federated learning Service robots Peer-to-peer computing Servers Protection

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Computational methods to Support Prototyping of an Adaptive Robot Joystick Controller for Children with Upper Limb Impairments

Computational Methods to Support Prototyping of an Adaptive ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Melanie Jouaiti Negin Azizi Kerstin Dautenhahn Department of Electrical and Computer Engineering University of Waterloo Waterloo Ontario Canada

Between 2% to 5% of children are affected by Developmental Coordination Disorders in Canada and have been diagnosed with upper limb impairments, which affect their daily lives and reduces their autonomy. Motor impairments can be part of progressive disorders, so despite regular therapy, progress remains fleeting. Affected individuals therefore consistently face many barriers, including entertainment opportunities, as availability of off-the-shelf inclusive technology is very limited. Our long-term goal is to develop a play-mediator robot, which would facilitate play between children with motor impairments and their peers or family members. Here, games that the robot can play are remotely controlled by the participants, using appropriate interfaces (e.g. joysticks). In this paper, we take the first step towards that goal and develop an adaptive joystick controller that can compensate for individual deficits. We monitor movement statistics to determine if re-calibration of the controller is necessary. Moreover, we propose a computational model of data ‘distortion’, as a tool for developers to test their technology in the very early stages of prototype development, without requiring access to participants. This work is validated with data from healthy adults and children with upper limb impairments.

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Fault Diagnosis of Quadruped Robot Joint Modules Based on TDIC and Random Forest

Fault Diagnosis of Quadruped Robot Joint Modules Based on TD...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), international conference on

作者： Yufan Chen Beibei Fan Xin Xiong Mapping Shi School of Mechatronic Engineering and Automation Shanghai University Shanghai China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

As robotics technology advances, quadruped robots are increasingly instrumental in diverse applications, including rescue operations and healthcare. However, the intricate operating conditions expose the components within the joint modules of these robots to alternating and impact loads. Consequently, research into signal processing and fault diagnosis techniques for planetary gearboxes in joint modules holds significant engineering importance. Given the challenges associated with fault signal extraction from planetary gearboxes, this study proposes a novel fault feature extraction method based on Time-Dependent Intrinsic Correlation (TDIC), integrated with a random forest classifier for fault classification. This approach leverages signal frequency analysis to adaptively extract segments characterized by high correlation among different physical field signals as fault features. The random forest model is utilized for fault classification and its performance is compared against CNN, KNN, and XGBoost models. Experimental results validate the superior fault classification efficacy of the proposed method.

关键词： Fault diagnosis Adaptation models Correlation Accuracy Computational modeling Feature extraction Real-time systems Quadrupedal robots Random forests Load modeling

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FogROS: An Adaptive Framework for Automating Fog robotics Deployment 17

FogROS: An Adaptive Framework for Automating Fog Robotics De...

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17th IEEE international conference on automation Science and Engineering (CASE)

作者： Chen, Kaiyuan (Eric) Liang, Yafei Jha, Nikhil Ichnowski, Jeffrey Danielczuk, Michael Gonzalez, Joseph Kubiatowicz, John Goldberg, Ken Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Univ Calif Berkeley Dept Ind Engn & Operat Res Berkeley CA 94720 USA

ISBN: (纸本)9781665418737

As many robot automation applications increasingly rely on multi-core processing or deep-learning models, cloud computing is becoming an attractive and economically viable resource for systems that do not contain high computing power onboard. Despite its immense computing capacity, it is often underused by the robotics and automation community due to lack of expertise in cloud computing and cloud-based infrastructure. Fog robotics balances computing and data between cloud edge devices. We propose a software framework, FogROS, as an extension of the Robot Operating System (ROS), the defacto standard for creating robot automation applications and components. It allows researchers to deploy components of their software to the cloud with minimal effort, and correspondingly gain access to additional computing cores, GPUs, FPGAs, and TPUs, as well as predeployed software made available by other researchers. FogROS allows a researcher to specify which components of their software will be deployed to the cloud and to what type of computing hardware. We evaluate FogROS on 3 examples: (1) simultaneous localization and mapping (ORB-SLAM2), (2) Dexterity Network (Dex-Net) GPU-based grasp planning, and (3) multi-core motion planning using a 96-core cloud-based server. In all three examples, a component is deployed to the cloud and accelerated with a small change in system launch configuration, while incurring additional latency of 1.2 s, 0.6 s, and 0.5 s due to network communication, the computation speed is improved by 2:6x, 6:0x and 34:2x, respectively. Code, videos, and supplementary material can be found at https://***/Berkeleyautomation/FogROS.

关键词： Computers Cloud computing automation Codes Adaptive systems Software Hardware

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AI-Enhanced Predictive Analytics for Fraud Detection in Smart Grid Systems

AI-Enhanced Predictive Analytics for Fraud Detection in Smar...

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Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), international conference on

作者： A Akashpalaniappa S Nithya Roopa Department of Information Technology Kumaraguru College of Technology Coimbatore TamilNadu India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Electricity theft is a serious threat to the reliability and effectiveness of smart grids. As smart meters become increasingly common, the traditional methods of detection are no longer sufficient to counteract ever more sophisticated fraud schemes. The goal of this research is to examine the use of prospective data analytics for the detection of electricity fraud through machine learning models optimized for maximum accuracy. The smart meter real-time data is proposed to enhance algorithms for techniques what can be undertaken in a model using in the use of hyperparameter optimization. The proposed method aims to detect unusual patterns of consumption, minimize false positive generation, and enhance the quality of fraud detection; thereby improving the security and efficiency of smart grids.

关键词： Analytical models Accuracy Computational modeling Electricity Predictive models Hyperparameter optimization Data models Smart meters Fraud Smart grids

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