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第33届中国控制与决策会议

作者： Yuwen Li Chaobing Zheng Shiqian Wu Wangming Xu Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology The Institute of Robotics and Intelligent Systems School of Information Science and EngineeringWuhan University of Science and Technology

Aiming at the existing single image haze removal algorithms,which are based on prior knowledge and assumptions,subject to many limitations in practical applications,and could suffer from noise and halo *** end-to-end system is proposed in this paper to reduce defects by combining the prior knowledge and deep learning *** haze image is decomposed into the base layer and detail layers through a weighted guided image filter(WGIF) firstly,and the airlight is estimated from the base ***,the base layer image is passed to the efficient deep convolutional network for estimating the transmission *** restore object close to the camera completely without amplifying noise in sky or heavily hazy scene,an adaptive strategy is proposed based on the value of the transmission *** the transmission map of a pixel is small,the base layer of the haze image is used to recover a haze-free image via atmospheric scattering model,***,the haze image is *** show that the proposed method achieves superior performance over existing methods.

关键词： single image haze removal Weighted guided image filtering prior knowledge Convolutional Neural Networks

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Lunar Asset Messaging and on Orbit Navigation (LA MOON)

Lunar Asset Messaging and on Orbit Navigation (LA MOON)

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IAF Space Exploration Symposium 2021 at the 72nd International Astronautical Congress, IAC 2021

作者： Derewa, Chrishma Holt, Samuel Lally, Michael Trybula, Robert Yuan, Christine Coble, Kyle Huang, Calvin Jet Propulsion Laboratory California Institute of Technology United States University of Colorado Boulder Aerospace Engineering United States New York University Computer Science United States University of Southern California Computer Science United States Cornell University Mechanical Engineering United States Kth Royal Institute of Technology Systems Control and Robotics Pasadena City College Computer Science United States

ISBN: (纸本)9781713842965

NASA has titled its 2020 thrust for the Moon, Artemis. The increased focus on the Moon as a destination for future human and robotic expeditions necessitates general purpose navigational and communications infrastructure reducing their complexity to help establish a sustained presence. A framework through which Lunar missions can relay communications and localize their positions shifts the burden from the individual mission and enables resource allocation tailored to mission-specific goals. During the summer of 2020, student interns under the Innovation to Flight (i2F) program at the National Aeronautics and Space Administration s (NASA) Jet Propulsion laboratory (JPL) in collaboration with the University of Colorado Boulder designed, built, and tested a prototype framework capable of providing surface assets with communication and positioning services. The team utilized the existing i2F CubeSat bus in addition to developing several CubeSat engineering development units (EDUs), a ground vehicle, and a ground station to simulate a scenario in which a lunar surface mission is supported by these services. A primary goal of the summer was to develop a method for localizing the ground vehicle through trilateration. Distances are inferred from the round-Trip time of flight (ToF) of radio signals between an asset and several elements. Signals were sent and received using LimeSDR software defined radios on-board both the ground vehicle and the EDUs;ToF and trilateration were calculated on a Qualcomm Snapdragon development board located within the LA MOON payload data system. The ModalAI chipset on the Qualcomm was instrumental in executing visual based position estimation. Communications was facilitated through a bent-pipe approach addressing the NASA requirement to provide solutions for in communication denied locations. The ground vehicle relayed information to other surface assets in addition to its ground station through the supporting constellation. This project demons

关键词： Orbits

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Coherence as a criterion for multichannel combining low SNR communications in an upwelling environment

Coherence as a criterion for multichannel combining low SNR ...

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OCEANS

作者： Fabio B. Louza Sergio M. Jesus Laboratory of Robotic Systems in Engineering and Science (LARSyS) University of Algarve Faro Portugal

ISBN: (数字)9781728154466

ISBN: (纸本)9781728184098

This paper presents a study on the impact of temporal (TCOH) and spatial (SCOH) coherence over low SNR communications in an environment with intense upwelling and demonstrates how to use coherence as a criterion to perform multichannel combining. Using low power signals collected in a shallow water experiment, from two independent and different geometry arrays with four hydrophones each, temporal coherence was estimated. Signals whose TCOH was above a threshold were averaged and SCOH between channels were estimated. Bit error rate results showed that SCOH can be used as a criterion to discard noisy and high multipath channels. Multichannel combination based on SCOH indicated that the use of the arrays can increase the processing gain, even if the sensors are just a few wavelengths from each other. Moreover, longer averaging times provided lower BER but reduced the effective bit rate.

关键词： Bit error rate Bit rate Spatial coherence Sonar equipment Coherence Signal to noise ratio Sensor arrays

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Tension Analysis of Small Motor Stator Winding Tensioning Process

Tension Analysis of Small Motor Stator Winding Tensioning Pr...

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作者： Zhao, Yanling Zhang, Zhao Xiang, Jingzhong Bao, Yudong State Key Laboratory of Robotics and Systems Harbin150080 China School of Mechanical and Power Engineering Harbin University of Science and Technology Harbin150080 China

In view of the small motor stator winding due to the size of the tension on the enameled wire is different, so that the winding tightness is different and lead to enameled wire damage or motor can not be used normally. Through the establishment of the tension model of the tensioning process, the stress of each layer of enameled wire during tensioning is analyzed, which involves the mechanical theory of tensioning process and the theory of thick-walled thin cylinder. According to this theory, the mechanical behavior of the enameled wire during the tensioning process is studied, the influence of tension on the radial stress and hoop stress of the special point and the variation law are obtained. After simulation and experiment, the results show that the tension and the number of winding layers have different effects on the radial and hoop stress of the special points. Applying reasonable tension can effectively ensure the normal operation of the motor and greatly increase its reliability. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Winding

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Research on Intrusion Detection of Industrial Control System Based on FastICA-SVM Method 7th

Research on Intrusion Detection of Industrial Control System...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Chen, Haonan Liu, Xianda Wang, Tianyu Zhang, Xuejing College of Information Science and Engineering Northeastern University Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Beijing China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Beijing China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030786083

As an important part of guard system, intrusion detection systems have great significance to the security of industrial control systems. Cause the industrial control systems of the on-site environment is often very complicated, the process data extracted on-site have a wide variety of features, and they always have complex associations and are not independent. The intrusion detection algorithms for traditional industrial control systems often do not solve this problem, which results in a decrease in the accuracy, precision and high false negative rate (FNR), false positives rate (FPR). Aiming at solving the current problems, this paper proposes an algorithm combining the Fast Independent Principal Component Analysis (FastICA) and Support Vector Machine (SVM). Finally compared with the SVM and PCA-SVM algorithms, the experimental results show that the accuracy rate has been significantly improved and slight FPR. © 2021, Springer Nature Switzerland AG.

关键词： Intrusion detection

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A Haptic Exploration and Surface Classification of Objects with Four Typical Surface Properties

A Haptic Exploration and Surface Classification of Objects w...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Peng Qi Yunfeng Wu Tianliang Yao Bo Lu Yi Sun Jian S. Dai Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai P. R. China Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Suzhou Jiangsu China Laboratory of Intelligent Systems Ecole Polytechnique Federale de Lausanne Lausanne Switzerland Department of Mechanical and Energy Engineering College of Engineering Southern University of Science and Technology Shenzhen P. R. China School of Natural Mathematical and Engineering Sciences King's College London

To effectively interact with the physical world, an intelligent robot is required to have the ability to obtain the detailed features of an unknown object. Visual devices are commonly used to detect the global geometry of an object; however, detailed information such as surface properties cannot be identified using these devices. The current study proposes an efficient haptic exploration method to recognize the physical properties of surfaces using an intelligent fingertip. Our surface-following algorithm utilizes the normal force vector and the tangential force vector at the contact point between the fingertip and the target object to predict the moving direction and implement the surface exploration. And a correction index $K$ is introduced to adjust the sliding velocity. The algorithm is proved to be robust and high performance both in simulation and lab experiments. Finally, we propose and explore a haptic prediction neural network, which enables our robot to have an accurate feel through physical interaction. In the classification experiment of four types of objects with different surface properties, the average accuracy of the proposed model is 90.2%.

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Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection

arXiv

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arXiv 2023年

作者： Merlo, Elena Lagomarsino, Marta Lamon, Edoardo Ajoudani, Arash Human-Robot Interfaces and Interaction Laboratory Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Dept. of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Dept. of Information Engineering and Computer Science University of Trento Trento Italy

This paper presents a new method to describe spatio-temporal relations between objects and hands, to recognize both interactions and activities within video demonstrations of manual tasks. The approach exploits Scene Graphs to extract key interaction features from image sequences while simultaneously encoding motion patterns and context. Additionally, the method introduces event-based automatic video segmentation and clustering, which allow for the grouping of similar events and detect if a monitored activity is executed correctly. The effectiveness of the approach was demonstrated in two multi-subject experiments, showing the ability to recognize and cluster hand-object and object-object interactions without prior knowledge of the activity, as well as matching the same activity performed by different subjects. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Lifelong-MonoDepth: Lifelong Learning for Multi-Domain Monocular Metric Depth Estimation

arXiv

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arXiv 2023年

作者： Hu, Junjie Fan, Chenyou Zhou, Liguang Gao, Qing Liu, Honghai Lam, Tin Lun The Chinese University of Hong Kong Shenzhen China The School of Artificial Intelligence South China Normal University China The School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen China State Key Laboratory of Robotics and systems Harbin Institute of Technology Shenzhen China The School of Science and Engineering The Chinese University of Hong Kong Shenzhen China

With the rapid advancements in autonomous driving and robot navigation, there is a growing demand for lifelong learning models capable of estimating metric (absolute) depth. Lifelong learning approaches potentially offer significant cost savings in terms of model training, data storage, and collection. However, the quality of RGB images and depth maps is sensor-dependent, and depth maps in the real world exhibit domain-specific characteristics, leading to variations in depth ranges. These challenges limit existing methods to lifelong learning scenarios with small domain gaps and relative depth map estimation. To facilitate lifelong metric depth learning, we identify three crucial technical challenges that require attention: i) developing a model capable of addressing the depth scale variation through scale-aware depth learning, ii) devising an effective learning strategy to handle significant domain gaps, and iii) creating an automated solution for domain-aware depth inference in practical applications. Based on the aforementioned considerations, in this paper, we present i) a lightweight multi-head framework that effectively tackles the depth scale imbalance, ii) an uncertainty-aware lifelong learning solution that adeptly handles significant domain gaps, and iii) an online domain-specific predictor selection method for real-time inference. Through extensive numerical studies, we show that the proposed method can achieve good efficiency, stability, and plasticity, leading the benchmarks by 8% ∼ 15%. The code is available at https://***/Freeformrobotics/Lifelong-MonoDepth. © 2023, CC BY.

关键词： Numerical methods

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Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection

Automatic Interaction and Activity Recognition from Videos o...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Elena Merlo Marta Lagomarsino Edoardo Lamon Arash Ajoudani Human-Robot Interfaces and Interaction Laboratory Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Dept. of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Dept. of Information Engineering and Computer Science University of Trento Trento Italy

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Hardware-in-the-loop and Digital Twin Enabled Autonomous robotics-assisted Environment Inspection*

Hardware-in-the-loop and Digital Twin Enabled Autonomous Rob...

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International Symposium on Autonomous systems (ISAS)

作者： Johnny Li Bo Shang Iresh Jayawardana Genda Chen Department of Soil and Water Systems Precision Agriculture and Intelligent Robotics Laboratory University of Idaho Moscow USA Department of Electrical Engineering The City College of New York (CCNY /CUNY City College) New York NY USA Center for Intelligent Infrastructure Missouri University of Science and Technology Rolla USA

Empowered by the advanced 3D sensing, computer vision and AI algorithm, autonomous robotics provide an unprecedented possibility for close-up infrastructure environment inspection in an efficient and reliable fashion. Deep neural network (DNN) learning algorithms, pretrained on the large database can empower real-time object detection as well as fully autonomous, safe robotic navigation in unstructured environments while avoiding the potential obstacle. However, the development and deployment of the robots, inspection planning and operation procedures are still tedious and segmented with tremendous manual intervention during environmental inspection and anomaly monitoring. The proposed digital twin approach is able to provide a virtual representation model of the target environment either from a build-design or from 3D scanning of the real-world physical assets at high resolution in the Unity simulation environment, a transverse drone robot model and test its robotics Operating System(ROS) autonomous navigation and obstacle avoidance software stack, and the hardware-in-the-loop test can thus be conducted for the flight control algorithm effectiveness and real-time object detection performance evaluation. The preliminary result shows that VGG16-UNet deep learning algorithm was able to use only a small amount of guidance and time from experienced inspection pilots to successfully identify the critical elements and defects and real-time navigate around the unstructured environment. The proposed digital twin framework and methodology is promising to be utilized for developing and testing fully autonomous inspection robots and its path planning and navigation and detection operation with greater cost- and time-efficiency.

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