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

共 24667 条记录，以下是231-240 订阅

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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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Optimization Design of the Toroidal Worm Wheel Hob for Robotic Joint with Worm Gear Pair Transmission

Optimization Design of the Toroidal Worm Wheel Hob for Robot...

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

作者： Zhijiang Chen Chunming Xia Yingfan Ren Chao Zhang Jie Yang Department of Mechanical and Electronic Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

To improve the transmission efficiency of the toroidal worm gear pairs in robotic joints and to enhance the accuracy of the toroidal worm wheel hob in cutting worm wheels, a method is proposed that involves using cylindrical forming surface milling to create helical grooves on the front face of the toroidal worm wheel hob. Based on the principle of gear meshing, a mathematical model is established for the development of the helical grooves on the front face of the toroidal worm wheel hob using a cylindrical generating surface. The model uses planar second envelopment for the toroidal worm wheel. Additionally, formulas are provided for calculating the rake angles on both sides of the cutting edges in the indexing ring plane. The analysis of the obtained rake angle values reveals that when machining helical chip clearance grooves for variable transmission ratio toroidal worm wheel hobs, the rake angles on both sides of the hob are within ±0.25°. Compared to machining helical chip clearance grooves for fixed transmission ratio toroidal worm wheel hobs, there is a noticeable decrease in the absolute value of the rake angles on both sides of the cutting edges, leading to a significant improvement in cutting conditions. Simulation machining of the helical grooves for the toroidal worm wheel hob was carried out, and the rake angle was measured within the simulation software. The measurement results align with the calculated results, validating the correctness of the method presented in this paper.

关键词： Accuracy Gears Robot kinematics Wheels Machining Mathematical models Mobile robots Software measurement Grippers Faces

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Spatially Nonuniform Filtering and Encoding of Visual Information in a Retinomorphic Hardware System

Spatially Nonuniform Filtering and Encoding of Visual Inform...

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international conference on Autonomous Robots and Agents, ICARA

作者： Tatsuya Yamada Hiroyuki Yokota Yuki Hayashida Shinsuke Yasuakwa Dept. Human Intelligence Syst. Kyushu Institute of Technology Kitakyushu Fukuoka JAPAN Dept. Inform. Eng. Grad. Eng. Mie University Tsu Mie JAPAN

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Efficient information processing in biological early-vision systems is thought to rely on spatially nonuniform computations in the central and peripheral visual fields. Inspired by this functional segregation, we developed a digital hardware vision system that integrates two distinct spatiotemporal filters and spike encoders for the central and peripheral regions on a single image sensor plane. The system employs digital processing techniques to emulate graded potential signals and point-process spike outputs, mimicking the functionality of vertebrate retinas. System-level testing on actual hardware equipment verified region-specific response characteristics for the central and peripheral regions. This proposed approach advances the realization of retinomorphic efficient computations in future robotics and embedded vision systems.

关键词： Visualization Image coding Robot vision systems Optimization methods Retina Hardware Spatiotemporal phenomena Computational efficiency Robots Testing

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HILO: A Large-Scale Heterogeneous Object Dataset for Benchmarking Robotic Grasping Approaches

HILO: A Large-Scale Heterogeneous Object Dataset for Benchma...

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international conference on Autonomous Robots and Agents, ICARA

作者： Xinchao Song Sean Banerjee Natasha Kholgade Banerjee Dept. of Computer Science Clarkson University Potsdam NY USA Dept. of Computer Science & Engineering Wright State University Dayton OH USA

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Robust object manipulation is essential for robotics applications in real-world environments, especially when handling diverse and complex everyday objects. To facilitate this research, we present HILO, a large-scale dataset of 253 real-world everyday objects and 288 diverse scenes. HILO bridges a crucial gap in existing manipulation datasets through its heterogeneity and dual-resolution approach, combining high-resolution individual object scans with low-resolution scans of cluttered scenes. This provides both the precise geometric data needed for grasp planning and realistic environmental context. The dataset's comprehensive representations enable rigorous benchmarking of robotic grasping algorithms. Our evaluation of three leading grasping algorithms-Contact-GraspNet, GraspNet Baseline, and DexNet 4.0-reveals critical trade-offs between grasp quantity and quality, demonstrating the dataset's value in advancing robotic grasping research. HILO's rich object diversity and dual-resolution methodology provide a foundation for developing more versatile robotic systems capable of reliable real-world robotic manipulation. Our dataset is available at https://***/hilo_dataset/.

关键词： Bridges automation Diversity reception Collaboration Grasping Benchmark testing Diversity methods Planning Reliability Robots

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Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework

Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinfo...

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international conference on Autonomous Robots and Agents, ICARA

作者： Xibo Li Shruti Patel Christof Büskens Optimization and Optimal Control Center for Industrial Mathematics University of Bremen Bremen Germany Department for Autonomous Systems TOPAS Industriemathematik Innovation gGmbH Bremen Germany

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Deep reinforcement learning (DRL) allows a system to interact with its environment and take actions by training an efficient policy that maximizes self-defined rewards. In autonomous driving, it can be used as a strategy for high-level decision making, whereas low-level algorithms such as the hybrid A* path planning have proven their ability to solve the local trajectory planning problem. In this work, we combine these two methods where the DRL makes high-level decisions such as lane change commands. After obtaining the lane change command, the hybrid A* planner is able to generate a collision-free trajectory to be executed by a model predictive controller (MPC). In addition, the DRL algorithm is able to keep the lane change command consistent within a chosen time-period. Traffic rules are implemented using linear temporal logic (LTL), which is then utilized as a reward function in DRL. Furthermore, we validate the proposed method on a real system to demonstrate its feasibility from simulation to implementation on real hardware.

关键词： Training Trajectory planning Predictive models Deep reinforcement learning Prediction algorithms Planning Trajectory Logic Autonomous vehicles Robots

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Machine Learning-Driven Container Scheduling for Edge-Empowered Microservices

Machine Learning-Driven Container Scheduling for Edge-Empowe...

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robotics and Technologies for Industrial automation (ROBOTHIA), IEEE international conference on

作者： Oras Baker Wei Li Haoyu Ma Kasthuri Subaramaniam Dobrila Lopez Sellappan Palaniappan Computer Science Ravensbourne University London London United Kingdom New Media Arts and Business Southern Institute of Technology Invercargill New Zealand Business and Economics Universiti Malaya Kuala Lumpur Malaysia School of Computing Eastern Institute of Technology Auckland New Zealand Computing and Digital Technology Help University Kuala Lumpur Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

This research investigates the integration of machine learning technology into microservices architectures for edge-enabled container scheduling strategies. Deploying microservices applications can be intricate, while container virtualisation technology solves environmental complexities. However, container scheduling strategies remain challenging, and machine learning techniques can be leveraged to rapidly and rationally adjust strategies for different services. A comparative analysis of container scheduling strategies with and without machine learning demonstrates a 60.6% improvement in scheduling speed. The evaluation includes two algorithms: K-Nearest Neighbours (KNN) and Bayesian Personalised Ranking (BPR). Results indicate that BPR outperforms KNN, achieving an 11.1% increase in speed, particularly for large-scale datasets. These improvements highlight BPR's effectiveness in edge computing environments, where containerised microservices operate closer to data sources and end-users. Additionally, the proposed control strategy minimises resource allocation errors and enhances system scalability. The findings provide a framework for integrating machine learning into edge-native microservices architectures, optimising container scheduling for edge computing scenarios.

关键词： Job shop scheduling Processor scheduling Service robots Microservice architectures Machine learning Computer architecture Containers Nearest neighbor methods Bayes methods Edge computing

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Enhancing Bone Fracture Detection: A Feature-Based Transfer Learning Approach Using DenseNet with SVM 2nd

Enhancing Bone Fracture Detection: A Feature-Based Transfer ...

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2nd international conference on Intelligent Manufacturing and robotics, ICIMR 2024

作者： Luo, Yang Liu, Xiaoyan Song, Rui P.P. Abdul Majeed, Anwar Zhang, Fan Xu, Yifeng Tan, Andrew Chen, Wei School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China School of Engineering and Technology Sunway University Selangor Darul Ehsan Bandar Sunway47500 Malaysia China Electric Power Research Institute Company Ltd. Beijing100192 China School of Robotics Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Accurate classification of bone fractures is essential for effective treatment, yet traditional methods can be error-prone and time-consuming. Transfer learning, particularly feature-based transfer learning, has shown promise in improving medical image analysis. This study investigates the effectiveness of DenseNet architectures combined with SVM classifiers with default hyperparameter for classifying hairline and pathological fractures. A dataset of 500 bone fracture x-ray images was utilized from open source for this study. Features were extracted using DenseNet121, DenseNet169, and DenseNet201 models. The findings indicated that the combination of DenseNet169 and SVM yielded the highest overall accuracy and generalization capacity, with a notable proficiency in differentiating hairline fractures. The classification performance of DenseNet121-SVM and DenseNet201-SVM was robust, although significantly less successful in identifying hairline fractures. The exceptional performance of the DenseNet169-SVM model highlights its promise as a dependable tool for automated bone fracture classification in medical imaging. This work emphasizes the efficacy of integrating advanced transfer learning techniques with conventional classifiers to enhance diagnostic precision, hence facilitating improvements in medical procedures. Additional investigation is advised to authenticate these discoveries using more extensive, practical clinical datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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State-space modelling approach for control and observer design in property-controlled reverse flow forming 11

State-space modelling approach for control and observer desi...

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11th Vienna international conference on Mathematical Modelling, MATHMOD 2025

作者： Kersting, Lukas Arian, Bahman Vasquez, Julian Rozo Trächtler, Ansgar Homberg, Werner Walther, Frank Fraunhofer IEM Paderborn33102 Germany Paderborn33098 Germany Dortmund44227 Germany Heinz Nixdorf Institute Paderborn University Paderborn33102 Germany

Flexible, metal forming processes such as flow forming are exposed to many disturbances that affect geometry and material properties during forming and reduce the reproducibility of those processes. In this context, closed-loop property control is a feasible solution for process improvement. To include advanced controllers and observers in closed-loop property control concepts, mathematical models in terms of state-space models are advantageous. However, especially in case of flow forming these models are not spread due to the complex process kinematics, nonlinearity of the process and spatial dimensions of the workpiece. This makes mathematic state-space modelling challenging. Thus, a model has to be newly developed. This paper therefore presents a novel discrete-Time state-space model for flow forming. The model treats the workpiece geometry as discrete and represents the complex process kinematics in the form of a local sampling of the roller trajectory. It can be shown that the model is valid and that the resulting system dynamics especially results the from the displacement between roller and sensor which is considered in the model. Thus, the result of this paper is a valid state-space model that can be prospectively used in a model-based state observer for flow forming. This state observer could later extent a closed-loop property control to enable more complex property structures than before. Copyright © 2025 The Authors.

关键词： State space methods

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Vision-Based Robotic Grasping: Integrating YOLOv8 and GRCNN for Dynamic Object Handling 2nd

Vision-Based Robotic Grasping: Integrating YOLOv8 and GRCNN ...

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2nd international conference on Intelligent Manufacturing and robotics, ICIMR 2024

作者： Lyu, Xingze Hajjaj, Sami Salama Hussen School of Robotics Xi’an Jiaotong-Liverpool University 111 Taicang Avenue Jiangsu Province Suzhou Taicang215400 China School of Engineering and Technology Sunway University No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia

ISBN: (纸本)9789819639489

This work presents an approach to vision-based robotic grasping, combining scene understanding with advanced neural network architectures. The primary goal is to develop a system that enables robots to grasp a wide variety of objects in unstructured environments using RGB-D data. The methodology integrates two key components: the YOLOv8 model for real-time object detection and the Generative Residual Convolutional Neural Network (GRCNN) for precise grasp pose inference. The experimental setup employs the PyBullet simulation environment, utilizing a Universal Robots UR5 model equipped with a Robotiq 2F-85 parallel-jaw gripper. The YOLOv8 model is trained using a custom dataset created in PyBullet and annotated via the Roboflow platform, ensuring accurate object detection and localization. The GRCNN model generates grasp quality maps, including quality, angle, and width maps, which are crucial for determining the optimal grasp points and orientations. The system is thoroughly tested in the PyBullet environment, where it demonstrates robust performance across various scenarios, accurately detecting objects and planning grasps in real-time. The simulation results are validated through real-world experiments using the UR5 robot, confirming the system’s effectiveness in practical applications. This work also addresses the limitations of traditional grasping methods, which often rely on pre-programmed instructions and detailed 3D models, making them unsuitable for dynamic environments. By leveraging deep learning and real-time processing, the proposed system overcomes these challenges, providing a scalable and efficient solution for robotic grasping in unstructured settings. Future work will involve optimizing neural networks, exploring adaptive grasping strategies, and enhancing language understanding capabilities to improve system responsiveness and versatility in complex tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Human robot interaction

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OpenLiDARMap: Zero-Drift Point Cloud Mapping Using Map Priors 11

OpenLiDARMap: Zero-Drift Point Cloud Mapping Using Map Prior...

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11th international conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2025

作者： Kulmer, Dominik Leitenstern, Maximilian Weinmann, Marcel Lienkamp, Markus Institute of Automotive Technology Munich Institute of Robotics and Machine Intelligence Technical University of Munich Garching Germany

ISBN: (纸本)9789897587450

Accurate localization is a critical component of mobile autonomous systems, especially in Global Navigation Satellite Systems (GNSS)-denied environments where traditional methods fail. In such scenarios, environmental sensing is essential for reliable operation. However, approaches such as LiDAR odometry and Simultaneous Localization and Mapping (SLAM) suffer from drift over long distances, especially in the absence of loop closures. Map-based localization offers a robust alternative, but the challenge lies in creating and georeferencing maps without GNSS support. To address this issue, we propose a method for creating georeferenced maps without GNSS by using publicly available data, such as building footprints and surface models derived from sparse aerial scans. Our approach integrates these data with onboard LiDAR scans to produce dense, accurate, georeferenced 3D point cloud maps. By combining an Iterative Closest Point (ICP) scan-to-scan and scan-to-map matching strategy, we achieve high local consistency without suffering from long-term drift. Thus, we eliminate the reliance on GNSS for the creation of georeferenced maps. The results demonstrate that LiDAR-only mapping can produce accurate georeferenced point cloud maps when augmented with existing map priors. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Global positioning system

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