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arXiv 2024年

作者： Yin, Xiaoting Shi, Hao Bao, Yuhan Bing, Zhenshan Liao, Yiyi Yang, Kailun Wang, Kaiwei State Key Laboratory of Modern Optical Instrumentation Zhejiang University China School of Robotics National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China College of Information Science and Electronic Engineering Zhejiang University China The Chair of Robotics AI and Real-Time Systems Technical University of Munich Germany

Estimating Neural Radiance Fields (NeRFs) from images captured under optimal conditions has been extensively explored in the vision community. However, robotic applications often face challenges such as motion blur, insufficient illumination, and high computational overhead, which adversely affect downstream tasks like navigation, inspection, and scene visualization. To address these challenges, we propose E-3DGS, a novel event-based approach that partitions events into motion (from camera or object movement) and exposure (from camera exposure), using the former to handle fast-motion scenes and using the latter to reconstruct grayscale images for high-quality training and optimization of event-based 3D Gaussian Splatting (3DGS). We introduce a novel integration of 3DGS with exposure events for high-quality reconstruction of explicit scene representations. Our versatile framework can operate on motion events alone for 3D reconstruction, enhance quality using exposure events, or adopt a hybrid mode that balances quality and effectiveness by optimizing with initial exposure events followed by high-speed motion events. We also introduce EME-3D, a real-world 3D dataset with exposure events, motion events, camera calibration parameters, and sparse point clouds. Our method is faster and delivers better reconstruction quality than event-based NeRF while being more cost-effective than NeRF methods that combine event and RGB data by using a single event sensor. By combining motion and exposure events, E-3DGS sets a new benchmark for event-based 3D reconstruction with robust performance in challenging conditions and lower hardware demands. The source code and dataset will be available at https://***/MasterHow/E-3DGS. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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Lidar-Inertial and Downward Vision Based Mobile Robot SLAM in Digital Workshops

Lidar-Inertial and Downward Vision Based Mobile Robot SLAM i...

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Data Driven control and Learning systems (DDCLS)

作者： Yanchao Zhang Hao Geng Bin Liu Yinlong Zhang Xudong Yuan Sichao Zhang School of Information Science and Engineering Shenyang University of Technology Shenyang China Guangzhou Institute of Industrial Intelligence Guangzhou China 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 Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In order to solve the problems of long-term cumulative error and the absence of absolute pose in the existing Lidar-Inertial Measurement Unit (IMU) fusion methods for Automated Guided Vehicle (AGV) Simultaneous Localization and Mapping (SLAM), we propose a new SLAM method for mobile robot, integrating Lidar, IMU, and a downward vision camera. Firstly, we tightly couple Lidar-IMU and downward vision camera using a multimodal fusion framework. Downward visual measurements are integrated as QR factors into the factor graph, providing absolute pose information to correct cumulative error in the Lidar-IMU system and enable the joint pose estimation of the AGV. Secondly, an enhanced Iterative Closest Point (ICP) point cloud matching algorithm has been developed to correct the incorrect pose. This approach achieves Scan-to-Map alignment by employing a coarse-to-fine matching strategy, thereby enhancing the accuracy of point cloud alignment. Finally, the extended Kalman filter is used to optimize the Lidar pose drift, as well as the Lidar mapping effectively by using the Lidar-IMU as the measurements and the downward visual measurements as the updates. The algorithm has been extensively evaluated on the developed AGV experimental platform. The experimental results show that the position error is less than 0.02 meters and the orientation error is less than 1°, which solves the problems of long-term error accumulation and the lack of absolute pose information for AGV navigation.

关键词： Point cloud compression Visualization Laser radar Simultaneous localization and mapping Conferences Robot vision systems Measurement uncertainty

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A Robust Surveillance System for AGV Using Forward-Downward Visual Measurements

A Robust Surveillance System for AGV Using Forward-Downward ...

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Chinese control and Decision Conference, CCDC

作者： Sichao Zhang Wei Liang Xudong Yuan Haibo An Meng Zheng Yinlong Zhang 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 Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Science and Technology on Information Systems Engineering Laboratory the 28th Research Institute of CETC Nanjing China

Automatic Guided Vehicle (AGV) has been widely used in the warehouse for transporting the bulky and heavy cargos. However, the AGV may deviate the regular trajectories in presence of incorrect or untimely commands sent form the server due to, e.g., cyber attacks, unexpected blocks of the wireless signals. In order to ensure AGV running safely, this paper presents a visual surveillance system by making full use of the measurements from the forward and downward cameras. Specifically, the forward camera estimates the AGV positions and attitudes by tracking the surrounding landmarks detected from the forward image sequences. Besides, the downward camera is used to detect the QR codes fixed on the floor and estimate the AGV poses in the absolute reference frame. Apart from that, the AGV poses from the downward camera could correct scale and poses estimated the forward view. The proposed method has been extensively performed on the developed platform. The results proves the effectiveness in using the complementary forward-downward visual measurements for AGV security surveillance.

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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel State Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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An Container Elastic Autoscaling Strategy Based Adaptive Integrated Resource Forecast

An Container Elastic Autoscaling Strategy Based Adaptive Int...

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International Conference on Communications, Information System and Computer Engineering (CISCE)

作者： Weiwei Miao Yong Sun Zeng Zeng Tao Hong Maoran Xiao Information & Telecommunication Branch State Grid Jiangsu Electric Power Co. Ltd. Nanjing China 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)9798350353174

ISBN: (纸本)9798350353181

The built-in elastic autoscaling strategy of Kubernetes, a container cloud orchestration and management system, balances the relationship between application service quality and cluster resource usage by regularly monitoring a single resource and comparing it with a set threshold to determine whether to scale up or down. However, the built-in strategy has the problems of incomplete evaluation of a single resource index, response delay and scaling jitter. To address the above problems, this paper uses an adaptive weighting algorithm to obtain a comprehensive prediction index based on long-range time multi-resource prediction sequences. This index is not only more comprehensive than a single index, but also measures the resource usage in the future time period, so that the adaptive comprehensive prediction index has the ability to more comprehensively measure the changes in application workload in the future time period. The experimental results show that the autoscaling strategy based on adaptive integrated resource forecast can effectively solve the problems of regulation lag and scaling jitter.

关键词： Clustering algorithms Jitter Containers Elasticity Prediction algorithms Time measurement Regulation

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Improved YOLOv5-based Method for Recognition of Insulator String Pins 12

Improved YOLOv5-based Method for Recognition of Insulator St...

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12th International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2022

作者： Du, Jingbo Jiang, Yong Shenyang Ligong University Shenyang110158 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Aiming at the problem that the substation insulator replacement robot cannot accurately identify insulator pins in the complex outdoor light environment, an improved YOLOv5-based insulator pin identification method YOLOv5-CBP is proposed. For the substation insulator pin recognition task, the backbone of the network is replaced by the lightweight EfficientNet-lite0;the Neck of the network is optimized and a more efficient feature fusion module BiFPN combined with an attention mechanism is introduced. The bounding box regression loss function was changed to improve the accuracy of inspection frame detection. Finally, the improved algorithm is compared with the original YOLOv5 algorithm on a self-built dataset for experiments. The results show that the insulator pin recognition accuracy is improved by 8.3%, the size of the algorithm model is reduced to 54% of the original, and the number of parameters is reduced by 49.4%. The improved algorithm model more lightweight. © 2022 IEEE.

关键词： Degradation Substations Inspection Insulators control systems Pins Neck

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Research on Industrial control Network Security Measurement Method Based on GRU-SVM

Research on Industrial Control Network Security Measurement ...

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2022 Chinese Automation Congress, CAC 2022

作者： Chen, Hengfei Liu, Xianda Sheng, Chuan Pan, Hao College of Information Engineering Shenyang University of Chemical Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665465335

Since the industrial Internet entered people's field of vision, industrial control network security has gradually received attention. Traditional network security measurement methods rely on the accuracy of situation sequences and historical situation values, and various factors in the network have a great influence on the results. However, compared with industrial control networks, the number and types of affected factors are less than those of the modern Internet. Therefore, based on the research on network security situational awareness, this paper proposes an industrial control network security measurement method model based on GRU-SVM. First, the data sets with threat type labels are classified, and then the similarity scoring mechanism and softmax function are introduced, to process the classification results. For the GRU-SVM classification module, the analysis of the simulation results shows that the convergence is better and the robustness is stronger. For the safety prediction measurement part, by comparing with different models, the mean absolute error and root mean square error are smaller and the reliability is higher. © 2022 IEEE.

关键词： Network security

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E-Nose-Based Cross-Domain Recognition of Combustion-Supporting Agents via M-BFFNet

E-Nose-Based Cross-Domain Recognition of Combustion-Supporti...

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Chinese control Conference (CCC)

作者： De-Xiu Wang Qing-Hao Meng Rui-Xue Han Hong-Yue Li Hui-Rang Hou Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin China Tianjin Navigation Instruments Research Institute Tianjin China

Identification of the type of combustion-supporting agents (CSAs) by an electronic nose (e-nose) is severely limited due to the absence of untested gas concentration in the e-nose training set. In order to solve this problem, this study designs an e-nose for classifying four CSAs (Diesel, Gasoline, Ethanol and Thinner), and builds cross-concentrations data set. This study uses Gramian Angular Field method that converts one-dimensional time series data to two-dimensional data, and proposes an algorithm named the Multi-Branch Feature Fusion Network (M-BFFNet). This algorithm passes the one-dimensional and two-dimensional data through two different networks and obtains two types of features. First, the algorithm can strength the one-dimensional raw data, integrate the advantages of different networks, and fuse the characteristics of different networks. Second, it can learn the characteristics of volatile gas with specific concentrations to identify the unknown concentrations. Last, a feature fusion module is designed to improve the algorithm ability of learning odor fingerprint. In order to verify the superiority of the algorithm, this study tests the recognition performance of the e-nose based systems and compares the proposed M-BFFNet with traditional algorithms. The results show that the proposed M-BFFNet improves the identification accuracy compared to other algorithms. According to the analysis of this work, the M-BFFNet can be used to identify CSAs with unknown concentrations.

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The Development of a Marine Internet of Things System Architecture for the control of Heterogeneous Robotic Agents

The Development of a Marine Internet of Things System Archit...

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Ocean Studies (ICOS), International Conference on

作者： Aleksey A. Kabanov Vadim A. Kramar Denis A. Tokarev Kirill V. Dementiev Department of Informatics and Control in Technical Systems Sevastopol State University Sevastopol Russian Federation Robotics and Intelligent Control System Laboratory Sevastopol State University Sevastopol Russian Federation

ISBN: (纸本)9781665473736

To create a platform for the Marine IoT for ensuring the interaction and digital navigation of heterogeneous marine robotic agents, it is necessary to combine into one network heterogeneous robotic agents, active and passive means of communication and illumination of the underwater and surface environment (marine communication buoys, underwater sonar stations, coastal connection posts). For their joint work, it is necessary to create a communication system unifying all elements of the platform, an integrated navigation and information management system for solving complex problems of navigation and motion control of robotic agents included in the platform to implement a joint mission. To do this, it is necessary to define the architecture of the platform to use a unified approach to data exchange.

关键词： Underwater communication systems architecture Sonar Sea measurements Switches Network architecture User interfaces

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I2EKF-LO: A Dual-Iteration Extended Kalman Filter Based LiDAR Odometry

I2EKF-LO: A Dual-Iteration Extended Kalman Filter Based LiDA...

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

作者： Wenlu Yu Jie Xu Chengwei Zhao Lijun Zhao Thien-Minh Nguyen Shenghai Yuan Mingming Bai Lihua Xie State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Hangzhou Guochen Robot Technology Company Limited Hangzhou China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

LiDAR odometry is a pivotal technology in the fields of autonomous driving and autonomous mobile robotics. However, most of the current works focus on nonlinear optimization methods, and still existing many challenges in using the traditional Iterative Extended Kalman Filter (IEKF) framework to tackle the problem: IEKF only iterates over the observation equation, relying on a rough estimate of the initial state, which is insufficient to fully eliminate motion distortion in the input point cloud; the system process noise is difficult to be determined during state estimation of the complex motions; and the varying motion models across different sensor carriers. To address these issues, we propose the Dual-Iteration Extended Kalman Filter (I 2 EKF) and the LiDAR odometry based on I 2 EKF (I 2 EKF-LO). This approach not only iterates over the observation equation but also leverages state updates to iteratively mitigate motion distortion in LiDAR point clouds. Moreover, it dynamically adjusts process noise based on the confidence level of prior predictions during state estimation and establishes motion models for different sensor carriers to achieve accurate and efficient state estimation. Comprehensive experiments demonstrate that I 2 EKF-LO achieves outstanding levels of accuracy and computational efficiency in the realm of LiDAR odometry. Additionally, to foster community development, our code is open-sourced. 1

关键词： Point cloud compression Laser radar Accuracy Nonlinear distortion Noise Robot sensing systems Mathematical models Odometry Kalman filters State estimation

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