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

作者： Maximilian Ulmer Maximilian Durner Martin Sundermeyer Manuel Stoiber Rudolph Triebel Institute of Robotics and Mechatronics German Aerospace Center (DLR) Wessling Germany Department of Computer Science Technical University of Munich (TUM) Garching Germany

We present a novel technique to estimate the 6D pose of objects from single images where the 3D geometry of the object is only given approximately and not as a precise 3D model. To achieve this, we employ a dense 2D-to-3D correspondence predictor that regresses 3D model coordinates for every pixel. In addition to the 3D coordinates, our model also estimates the pixel-wise coordinate error to discard correspondences that are likely wrong. This allows us to generate multiple 6D pose hypotheses of the object, which we then refine iteratively using a highly efficient region-based approach. We also introduce a novel pixel-wise posterior formulation by which we can estimate the probability for each hypothesis and select the most likely one. As we show in experiments, our approach is capable of dealing with extreme visual conditions including overexposure, high contrast, or low signal-to-noise ratio. This makes it a powerful technique for the particularly challenging task of estimating the pose of tumbling satellites for in-orbit robotic applications. Our method achieves state-of-the-art performance on the SPEED+ dataset and has won the SPEC2021 post-mortem competition. Code, trained models, and the used satellite model will be made publicly available.

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DistB-VNET: Distributed Cluster-based Blockchain Vehicular Ad-Hoc Networks through SDN-NFV for Smart City

DistB-VNET: Distributed Cluster-based Blockchain Vehicular A...

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International Conference on computer and Information Technology (ICCIT)

作者： Anichur Rahman MD. Zunead Abedin Eidmum Dipanjali Kundu Mahir Hossain MD Tanjum An Tashrif Md Ahsan Karim Md. Jahidul Islam Dept. of Computer Science and Engineering National Institute of Textile Engineering and Research (NITER) Constituent Institute of Dhaka University Savar Dhaka Dept. of Internet of Things and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Dept. of Computer Science and Engineering Green University of Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

In the developing topic of smart cities, Vehicular Ad-Hoc Networks (VANETs) are crucial for providing successful interaction between vehicles and infrastructure. This research proposes a distributed Blockchain-based Vehicular Ad-hoc Network ("DistB-VNET") architecture that includes binary malicious traffic classification, Software Defined Networking (SDN), and Network Function Virtualization (NFV) to ensure safe, scalable, and reliable vehicular networks in smart cities. The suggested framework is the decentralized blockchain for safe data management, SDN-NFV for dynamic network management and resource efficiency, and a noble isolation forest algorithm that works as an IDS (Intrusion Detection System). Further, "DistB-VNET" offers a dual-layer blockchain system, where a distributed blockchain provides safe communication between vehicles, while a centralized blockchain in the cloud is in charge of data verification and storage. This improves security, scalability, and adaptability, ensuring better traffic management, data security, and privacy in VANETs. Furthermore, the unsupervised isolation forest model achieves a high accuracy of 99.23% for detecting malicious traffic. Additionally, reveals that our method greatly improves network performance, offering decreased latency, increased security, and reduced congestion, an effective alternative for existing smart city infrastructures.

关键词： Smart cities Scalability Heuristic algorithms Forestry Dynamic scheduling Blockchains Network function virtualization Resource management Vehicle dynamics Software defined networking

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Suturing Tasks Automation Based on Skills Learned From Demonstrations: A Simulation Study

arXiv

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arXiv 2024年

作者： Zhou, Haoying Jiang, Yiwei Gao, Shang Wang, Shiyue Kazanzides, Peter Fischer, Gregory S. Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA United States Department of Computer Science Johns Hopkins University BaltimoreMD United States Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD United States

In this work, we develop an open-source surgical simulation environment that includes a realistic model obtained by MRI-scanning a physical phantom, for the purpose of training and evaluating a Learning from Demonstration (LfD) algorithm for autonomous suturing. The LfD algorithm utilizes Dynamic Movement Primitives (DMP) and Locally Weighted Regression (LWR), but focuses on the needle trajectory, rather than the instruments, to obtain better generality with respect to needle grasps. We conduct a user study to collect multiple suturing demonstrations and perform a comprehensive analysis of the ability of the LfD algorithm to generalize from a demonstration at one location in one phantom to different locations in the same phantom and to a different phantom. Our results indicate good generalization, on the order of 91.5%, when learning from more experienced subjects, indicating the need to integrate skill assessment in the future. © 2024, CC BY.

关键词： Demonstrations

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Text-Guided Image Generation for Railway Intrusion Anomaly Detection

Text-Guided Image Generation for Railway Intrusion Anomaly D...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Jinghang Chen Chi Zhang Xiaodan Feng Yuehu Liu Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Computer Science and Technology Xi'an Jiaotong University Xi'an China

Railway intrusion are characterized by strong suddenness, high unpredictability and many disturbing factors, which are key factors affecting railway safety. Currently, the deep neural network-based intrusion object detection algorithm relies on the diversity of training data. However, the data of railway intrusion image is scarce and difficult to obtain. Existing image generation methods face the problem of insufficient realism in the generated images and difficulty in generating large amounts of data. We aim to explore a text-guided image data generation framework for specific visual tasks by using an existing large language model combined with a text-to-image diffusion model. In this paper, we construct a framework for railway intrusion image generation guided by textual prompts and build a dataset of railway intrusion images based on this framework. The dataset generated in this paper contains a wide range of intrusion images and includes a variety of environmental factors such as weather and time, which provides better diversity than existing datasets. The evaluation results show that the intrusion images generated in this paper have high quality and realism.

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RESIDUAL LIFE PREDICTION OF LITHIUM BATTERY BASED ON IMPROVED MULTI-OPTIMIZATION ALGORITHM AND SUPPORT VECTOR REGRESSION 14

RESIDUAL LIFE PREDICTION OF LITHIUM BATTERY BASED ON IMPROVE...

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14th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2024

作者： Tao, Yulong Wang, Jun Wang, Kai School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China Key Laboratory of Intelligent Technology for Chemical Process Industry Liaoning Province Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Robotics and Artificial Intelligence Manufacturing Innovation Institute Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781837242672

The lithium-ion battery is increasingly critical in the fields of electric vehicles and sustainable energy. Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential to mitigate risks and minimize potential losses. This paper introduces a novel fusion algorithm, FPSOGWO, which combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) with an enhanced Circle chaos strategy and a random adaptive weighting strategy. The FPSOGWO algorithm, integrated with Support Vector Regression (FPSOGWO-SVR), was utilized to predict the RUL of health factors in lithium batteries. Compared to other optimization algorithms, FPSOGWO exhibits superior optimization speed and value, effectively overcoming issues of local optimality. Specifically, when benchmarked against the Whale Optimization Algorithm-SVR (WOA-SVR), the R-Square values of FPSOGWO-SVR showed improvements of 15.62%, 1.28%, 5.67%, and 10.65% across four different types of batteries. Experimental results demonstrate that FPSOGWO-SVR provides more accurate RUL predictions for lithium batteries, highlighting its potential for broader application in battery health management systems. © The Institution of Engineering & Technology 2024.

关键词： Support vector regression

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HyenaPixel: Global Image Context with Convolutions

arXiv

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arXiv 2024年

作者： Spravil, Julian Houben, Sebastian Behnke, Sven Fraunhofer IAIS Germany University of Applied Sciences Bonn-Rhein-Sieg Germany University of Bonn Computer Science Institute VI Center for Robotics Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany

In computer vision, a larger effective receptive field (ERF) is associated with better performance. While attention natively supports global context, its quadratic complexity limits its applicability to tasks that benefit from high-resolution input. In this work, we extend Hyena, a convolution-based attention replacement, from causal sequences to bidirectional data and two-dimensional image space. We scale Hyena’s convolution kernels beyond the feature map size, up to 191×191, to maximize ERF while maintaining sub-quadratic complexity in the number of pixels. We integrate our two-dimensional Hyena, HyenaPixel, and bidirectional Hyena into the MetaFormer framework. For image categorization, HyenaPixel and bidirectional Hyena achieve a competitive ImageNet-1k top-1 accuracy of 84.9% and 85.2%, respectively, with no additional training data, while outperforming other convolutional and large-kernel networks. Combining HyenaPixel with attention further improves accuracy. We attribute the success of bidirectional Hyena to learning the data-dependent geometric arrangement of pixels without a fixed neighborhood definition. Experimental results on downstream tasks suggest that HyenaPixel with large filters and a fixed neighborhood leads to better localization performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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Probabilistic Health Monitoring and Maintenance Scheduling for Nuclear Power Systems

Probabilistic Health Monitoring and Maintenance Scheduling f...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Amer, Ahmad Tsilifis, Panagiotis Saxena, Abhinav Baglietto, Emilio Khan, Genghis Probabilistic Design & Material Informatics GE Aerospace Research Niskayuna NY12309 United States Robotics AI & Computer Vision GE Vernova Advanced Research Niskayuna NY12309 United States AI & Computer Vision GE Aerospace Research San RamonCA94583 United States Department of Nuclear Science & Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Sustainable Systems GE Aerospace Research Niskayuna NY12309 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Operations and Maintenance (O&M) cost optimization in the nuclear energy industry is an imperative task for developing sustainable systems and efficient renewable technologies. We present a modular probabilistic framework for addressing maintenance scheduling challenges for small modular nuclear reactors using a Dynamic Bayesian Network (DBN) approach that monitors fatigue and performance in multiple subsystems of the reactors and provides a probabilistic assessment for potential failure events. The DBN framework fuses information from potential digital twins that assimilate real time data from several sensors with different time granularities in order to monitor and update thecurrentstate of knowledge aboutthestatus of several fatigue or performance criteria. Comprised of multi-modal digital twins (empirical, data- and physics-based twins), this DBN serves as a predictive framework forecasting the future state of specific reactor subsystems and enhancing decision-making capabilities associated with preventing system failure and minimizing the time and cost of maintenance events. The work presented in this paper is a holistic approach for monitoring and safeguarding complex, multi-component engineering systems where maintenance scheduling is critical and does not restrict itself to nuclear environments. However, our goal is to illustrate how such a framework can be adjusted to small modular water reactors. © 2025, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Nuclear energy

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The Kernel-SME Filter with Adaptive Kernel Widths for Association-free Multi-target Tracking

The Kernel-SME Filter with Adaptive Kernel Widths for Associ...

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American Control Conference (ACC)

作者： Eugen Ernst Florian Pfaff Uwe D. Hanebeck Marcus Baum Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany Institute of Computer Science Georg- August-University Göttingen Germany

Different objectives and paradigms exist for tracking multiple targets when measurements do not contain information about the target identities (IDs). The Symmetric Measurement Equation (SME) filter can be used when one is agnostic to the labels and does not attempt to assign different IDs to the different targets. We present an extension of the Kernel-SME filter that, unlike the original variant, uses adaptive kernel widths that depend on the respective uncertainty. In our evaluation, it outperformed existing SMEbased approaches, while it is only second to a more complex global nearest neighbor tracker.

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CPL-CLIP: Compound Prompt Learning for Flexible-Modal Face Anti-Spoofing

CPL-CLIP: Compound Prompt Learning for Flexible-Modal Face A...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Ying Zhang Xiangyu Zhu Ajian Liu Xun Lin Jun Wan Jing Zhang Zhen Lei State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences School of Computer Science and Engineering Beihang University School of Computer Science and Engineering Macau University of Science and Technology Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

Face anti-spoofing (FAS) is pivotal in safeguarding the integrity of face recognition systems. Flexible-modal FAS utilizes multi-modal data and trains a unified model adaptable to any single-modal testing scenario. This innovation addresses the shortcomings of conventional multi-modal FAS approaches, which typically demand separate model training and deployment for each modality. However, existing flexible-modal FAS approaches activate specific network branches based on the modality of the tested sample. This not only increases the model’s parameters but also necessitates the provision of the image’s modality for testing, thereby constraining deployment flexibility. To address the issue, we present Compound Prompt Learning CLIP (CPL-CLIP), a novel method for flexible-modal FAS. This approach capitalizes on a learned textual prompt that is nearly independent of modality, thus bolstering class-based classification across arbitrary modalities. Specifically, our CPL-CLIP introduces a Dual-Branch Prompt (DBP), consisting of class and modal prompts that describe and guide classification, where each prompt is composed of learnable vectors and fixed templates. To further render the class prompt as modality-agnostic as possible, a Cosine Similarity Loss (CSL) is proposed to facilitate the maximal separation of the class prompt from the modality prompt. With only the class prompt utilized during testing, CPL-CLIP enables deployment in diverse modal testing scenarios without the necessity of the test image’s modality to be known. Extensive experiments demonstrate CPL-CLIP’s superiority over existing methods on several flexible-modal FAS benchmarks.

关键词： Training Adaptation models Technological innovation Accuracy Face recognition Benchmark testing Vectors Robustness Data models Compounds

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Learning from SAM: Harnessing a Foundation Model for Sim2Real Adaptation by Regularization

arXiv

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arXiv 2023年

作者： Bonani, Mayara E. Schwarz, Max Behnke, Sven Computer Science VI University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany Center for Robotics University of Bonn Germany

Domain adaptation is especially important for robotics applications, where target domain training data is usually scarce and annotations are costly to obtain. We present a method for self-supervised domain adaptation for the scenario where annotated source domain data (e.g. from synthetic generation) is available, but the target domain data is completely unannotated. Our method targets the semantic segmentation task and leverages a segmentation foundation model (Segment Anything Model) to obtain segment information on unannotated data. We take inspiration from recent advances in unsupervised local feature learning and propose an invariance-variance loss over the detected segments for regularizing feature representations in the target domain. Crucially, this loss structure and network architecture can handle overlapping segments and oversegmentation as produced by Segment Anything. We demonstrate the advantage of our method on the challenging YCB-Video and HomebrewedDB datasets and show that it outperforms prior work and, on YCB-Video, even a network trained with real annotations. Additionally, we provide insight through model ablations and show applicability to a custom robotic application. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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