自主智能系统(英文)最新文献_第2页

Competing with autonomous model vehicles: a software stack for driving in smart city environments 与自动驾驶模型车竞争：智能城市环境中的驾驶软件堆栈

自主智能系统(英文) Pub Date : 2024-08-14 DOI: 10.1007/s43684-024-00074-w

Julius Bächle, Jakob Häringer, Noah Köhler, Kadir-Kaan Özer, Markus Enzweiler, Reiner Marchthaler

{"title":"Competing with autonomous model vehicles: a software stack for driving in smart city environments","authors":"Julius Bächle, Jakob Häringer, Noah Köhler, Kadir-Kaan Özer, Markus Enzweiler, Reiner Marchthaler","doi":"10.1007/s43684-024-00074-w","DOIUrl":"10.1007/s43684-024-00074-w","url":null,"abstract":"<div><p>This article introduces an open-source software stack designed for autonomous 1:10 scale model vehicles. Initially developed for the Bosch Future Mobility Challenge (BFMC) student competition, this versatile software stack is applicable to a variety of autonomous driving competitions. The stack comprises perception, planning, and control modules, each essential for precise and reliable scene understanding in complex environments such as a miniature smart city in the context of BFMC. Given the limited computing power of model vehicles and the necessity for low-latency real-time applications, the stack is implemented in C++, employs YOLO Version 5 s for environmental perception, and leverages the state-of-the-art Robot Operating System (ROS) for inter-process communication. We believe that this article and the accompanying open-source software will be a valuable resource for future teams participating in autonomous driving student competitions. Our work can serve as a foundational tool for novice teams and a reference for more experienced participants. The code and data are publicly available on GitHub.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00074-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142411651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel method for measuring center-axis velocity of unmanned aerial vehicles through synthetic motion blur images 通过合成运动模糊图像测量无人驾驶飞行器中心轴速度的新方法

自主智能系统(英文) Pub Date : 2024-07-09 DOI: 10.1007/s43684-024-00073-x

Quanxi Zhan, Yanmin Zhou, Junrui Zhang, Chenyang Sun, Runjie Shen, Bin He

{"title":"A novel method for measuring center-axis velocity of unmanned aerial vehicles through synthetic motion blur images","authors":"Quanxi Zhan, Yanmin Zhou, Junrui Zhang, Chenyang Sun, Runjie Shen, Bin He","doi":"10.1007/s43684-024-00073-x","DOIUrl":"10.1007/s43684-024-00073-x","url":null,"abstract":"<div><p>Accurate velocity measurement of unmanned aerial vehicles (UAVs) is essential in various applications. Traditional vision-based methods rely heavily on visual features, which are often inadequate in low-light or feature-sparse environments. This study presents a novel approach to measure the axial velocity of UAVs using motion blur images captured by a UAV-mounted monocular camera. We introduce a motion blur model that synthesizes imaging from neighboring frames to enhance motion blur visibility. The synthesized blur frames are transformed into spectrograms using the Fast Fourier Transform (FFT) technique. We then apply a binarization process and the Radon transform to extract light-dark stripe spacing, which represents the motion blur length. This length is used to establish a model correlating motion blur with axial velocity, allowing precise velocity calculation. Field tests in a hydropower station penstock demonstrated an average velocity error of 0.048 m/s compared to ultra-wideband (UWB) measurements. The root-mean-square error was 0.025, with an average computational time of 42.3 ms and CPU load of 17%. These results confirm the stability and accuracy of our velocity estimation algorithm in challenging environments.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00073-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141666199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An uncertainty-aware domain adaptive semantic segmentation framework 不确定性感知领域自适应语义分割框架

自主智能系统(英文) Pub Date : 2024-07-08 DOI: 10.1007/s43684-024-00070-0

Huilin Yin, Pengyu Wang, Boyu Liu, Jun Yan

{"title":"An uncertainty-aware domain adaptive semantic segmentation framework","authors":"Huilin Yin, Pengyu Wang, Boyu Liu, Jun Yan","doi":"10.1007/s43684-024-00070-0","DOIUrl":"10.1007/s43684-024-00070-0","url":null,"abstract":"<div><p>Semantic segmentation is significant to realize the scene understanding of autonomous driving. Due to the lack of annotated real-world data, the technology of domain adaptation is applied so that the model is trained on the synthetic data and inferred on the real data. However, this domain gap leads to aleatoric and epistemic uncertainty. These uncertainties link to the potential safety issue of autonomous driving in normal weather and adverse weather. In this study, we explore the scientific problem that has received sparse attention previously. We postulate that the Dual Attention module can mitigate the uncertainty in the task of semantic segmentation and provide some empirical study to validate it. Furthermore, the utilization of Kullback-Leibler divergence (KL divergence) helps the estimation of aleatoric uncertainty and boosts the robustness of the segmentation model. Our empirical study on the diverse datasets of semantic segmentation demonstrates the effectiveness of our method in normal and adverse weather. Our code is available at: https://github.com/liubo629/Seg-Uncertainty-dual-attention.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00070-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141667973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiple unmanned ship coverage and exploration in complex sea areas 复杂海域的多无人船覆盖和勘探

自主智能系统(英文) Pub Date : 2024-07-05 DOI: 10.1007/s43684-024-00069-7

Feifei Chen, Qingyun Yu

{"title":"Multiple unmanned ship coverage and exploration in complex sea areas","authors":"Feifei Chen, Qingyun Yu","doi":"10.1007/s43684-024-00069-7","DOIUrl":"10.1007/s43684-024-00069-7","url":null,"abstract":"<div><p>This study addresses the complexities of maritime area information collection, particularly in challenging sea environments, by introducing a multi-agent control model for regional information gathering. Focusing on three key areas—regional coverage, collaborative exploration, and agent obstacle avoidance—we aim to establish a multi-unmanned ship coverage detection system. For regional coverage, a multi-objective optimization model considering effective area coverage and time efficiency is proposed, utilizing a heuristic simulated annealing algorithm for optimal allocation and path planning, achieving a 99.67% effective coverage rate in simulations. Collaborative exploration is tackled through a comprehensive optimization model, solved using an improved greedy strategy, resulting in a 100% static target detection and correct detection index. Agent obstacle avoidance is enhanced by a collision avoidance model and a distributed underlying collision avoidance algorithm, ensuring autonomous obstacle avoidance without communication or scheduling. Simulations confirm zero collaborative failures. This research offers practical solutions for multi-agent exploration and coverage in unknown sea areas, balancing workload and time efficiency while considering ship dynamics constraints.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00069-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141673492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Water-saving control system based on multiple intelligent algorithms 基于多种智能算法的节水控制系统

自主智能系统(英文) Pub Date : 2024-07-04 DOI: 10.1007/s43684-024-00068-8

Fengnian Liu, Xiang Yu, Junya Tang

引用次数: 0

A nonlinear optimal control approach for 3-DOF four-cable driven parallel robots 3-DOF 四缆驱动并联机器人的非线性优化控制方法

自主智能系统(英文) Pub Date : 2024-07-01 DOI: 10.1007/s43684-024-00066-w

G. Rigatos, M. Abbaszadeh, J. Pomares

引用次数: 0

A binary-domain recurrent-like architecture-based dynamic graph neural network 基于二元域循环结构的动态图神经网络

自主智能系统(英文) Pub Date : 2024-06-25 DOI: 10.1007/s43684-024-00067-9

Zi-chao Chen, Sui Lin

{"title":"A binary-domain recurrent-like architecture-based dynamic graph neural network","authors":"Zi-chao Chen, Sui Lin","doi":"10.1007/s43684-024-00067-9","DOIUrl":"10.1007/s43684-024-00067-9","url":null,"abstract":"<div><p>The integration of Dynamic Graph Neural Networks (DGNNs) with Smart Manufacturing is crucial as it enables real-time, adaptive analysis of complex data, leading to enhanced predictive accuracy and operational efficiency in industrial environments. To address the problem of poor combination effect and low prediction accuracy of current dynamic graph neural networks in spatial and temporal domains, and over-smoothing caused by traditional graph neural networks, a dynamic graph prediction method based on spatiotemporal binary-domain recurrent-like architecture is proposed: Binary Domain Graph Neural Network (BDGNN). The proposed model begins by utilizing a modified Graph Convolutional Network (GCN) without an activation function to extract meaningful graph topology information, ensuring non-redundant embeddings. In the temporal domain, Recurrent Neural Network (RNN) and residual systems are employed to facilitate the transfer of dynamic graph node information between learner weights, aiming to mitigate the impact of noise within the graph sequence. In the spatial domain, the AdaBoost (Adaptive Boosting) algorithm is applied to replace the traditional approach of stacking layers in a graph neural network. This allows for the utilization of multiple independent graph learners, enabling the extraction of higher-order neighborhood information and alleviating the issue of over-smoothing. The efficacy of BDGNN is evaluated through a series of experiments, with performance metrics including Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) for link prediction tasks, as well as metrics for traffic speed regression tasks across diverse test sets. Compared with other models, the better experiments results demonstrate that BDGNN model can not only better integrate the connection between time and space information, but also extract higher-order neighbor information to alleviate the over-smoothing phenomenon of the original GCN.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00067-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142413589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction 多域融合货运无人机故障诊断知识图谱构建

自主智能系统(英文) Pub Date : 2024-06-21 DOI: 10.1007/s43684-024-00072-y

Ao Xiao, Wei Yan, Xumei Zhang, Ying Liu, Hua Zhang, Qi Liu

{"title":"Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction","authors":"Ao Xiao, Wei Yan, Xumei Zhang, Ying Liu, Hua Zhang, Qi Liu","doi":"10.1007/s43684-024-00072-y","DOIUrl":"10.1007/s43684-024-00072-y","url":null,"abstract":"<div><p>The fault diagnosis of cargo UAVs (Unmanned Aerial Vehicles) is crucial to ensure the safety of logistics distribution. In the context of smart logistics, the new trend of utilizing knowledge graph (KG) for fault diagnosis is gradually emerging, bringing new opportunities to improve the efficiency and accuracy of fault diagnosis in the era of Industry 4.0. The operating environment of cargo UAVs is complex, and their faults are typically closely related to it. However, the available data only considers faults and maintenance data, making it difficult to diagnose faults accurately. Moreover, the existing KG suffers from the problem of confusing entity boundaries during the extraction process, which leads to lower extraction efficiency. Therefore, a fault diagnosis knowledge graph (FDKG) for cargo UAVs constructed based on multi-domain fusion and incorporating an attention mechanism is proposed. Firstly, the multi-domain ontology modeling is realized based on the multi-domain fault diagnosis concept analysis expression model and multi-dimensional similarity calculation method for cargo UAVs. Secondly, a multi-head attention mechanism is added to the BERT-BILSTM-CRF network model for entity extraction, relationship extraction is performed through ERNIE, and the extracted triples are stored in the Neo4j graph database. Finally, the DJI cargo UAV failure is taken as an example for validation, and the results show that the new model based on multi-domain fusion data is better than the traditional model, and the precision rate, recall rate, and F1 value can reach 87.52%, 90.47%, and 88.97%, respectively.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00072-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142412924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human feedback enhanced autonomous intelligent systems: a perspective from intelligent driving 人的反馈增强型自主智能系统：智能驾驶的视角

自主智能系统(英文) Pub Date : 2024-06-13 DOI: 10.1007/s43684-024-00071-z

Kang Yuan, Yanjun Huang, Lulu Guo, Hong Chen, Jie Chen

引用次数: 0

Variational autoencoder-based techniques for a streamlined cross-topology modeling and optimization workflow in electrical drives 基于变分自动编码器的技术，用于简化电气传动中的跨拓扑建模和优化工作流程

自主智能系统(英文) Pub Date : 2024-05-24 DOI: 10.1007/s43684-024-00065-x

Marius Benkert, Michael Heroth, Rainer Herrler, Magda Gregorová, Helmut C. Schmid

{"title":"Variational autoencoder-based techniques for a streamlined cross-topology modeling and optimization workflow in electrical drives","authors":"Marius Benkert, Michael Heroth, Rainer Herrler, Magda Gregorová, Helmut C. Schmid","doi":"10.1007/s43684-024-00065-x","DOIUrl":"10.1007/s43684-024-00065-x","url":null,"abstract":"<div><p>The generation and optimization of simulation data for electrical machines remain challenging, largely due to the complexities of magneto-static finite element analysis. Traditional methodologies are not only resource-intensive, but also time-consuming. Deep learning models can be used to shortcut these calculations. However, challenges arise when considering the unique parameter sets specific to each machine topology. Building on two recent studies (Parekh et al. in IEEE Trans. Magn. 58(9):1–4, 2022; Parekh et al., Deep learning based meta-modeling for multi-objective technology optimization of electrical machines, 2023, arXiv:2306.09087), that utilized a variational autoencoder to cohesively map diverse topologies into a singular latent space for subsequent optimization, this paper proposes a refined architecture and optimization workflow. Our modifications aim to streamline and enhance the robustness of both the training and optimization processes, and compare the results with the variational autoencoder architecture proposed recently.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-024-00065-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142413467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0