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2023 China Automation Congress, CAC 2023

作者： Chen, Ying Zhao, Zi-Yi Sun, Peng Guo, Xiang-Gui Wang, Jian-Liang Coutinho, Daniel School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing Engineering Research Center of Industrial Spectrum Imaging Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Hangzhou Innovation Institute of Beihang University Collaborative Flying Robotics HangZhou China Universidade Federal de Santa Catarina Department of Automation and Systems Florianópolis Brazil

ISBN: (纸本)9798350303759

This paper studies cooperative control problem for multiple Euler-Lagrange systems with unknown harmonic disturbances, where the disturbances are the superposition of sinusoidal components with unknown amplitude, phase, and frequency. A novel adaptive disturbance observer without requiring the disturbance frequency information is designed to estimate the disturbances. Over the directed network topology, a cooperative control scheme is proposed based on backstepping techniques, so that the tracking error is ultimately uniformly bounded. The scheme developed can be used to reject the harmonic disturbances in a uniform way, which means the characteristics of the disturbances can be described as a fixed parameter vector. Finally, an application is presented to demonstrate the effectiveness of the proposed control strategy in the context of a two-link manipulator system. © 2023 IEEE.

关键词： Backstepping

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

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Hyperbolic Delaunay Geometric Alignment

arXiv

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arXiv 2024年

作者： Medbouhi, Aniss Aiman Marchetti, Giovanni Luca Polianskii, Vladislav Kravberg, Alexander Poklukar, Petra Varava, Anastasia Kragic, Danica Division of Robotics Perception and Learning Department of Intelligent Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

Hyperbolic machine learning is an emerging field aimed at representing data with a hierarchical structure. However, there is a lack of tools for evaluation and analysis of the resulting hyperbolic data representations. To this end, we propose Hyperbolic Delaunay Geometric Alignment (HyperDGA) – a similarity score for comparing datasets in a hyperbolic space. The core idea is counting the edges of the hyperbolic Delaunay graph connecting datapoints across the given sets. We provide an empirical investigation on synthetic and real-life biological data and demonstrate that HyperDGA outperforms the hyperbolic version of classical distances between sets. Furthermore, we showcase the potential of HyperDGA for evaluating latent representations inferred by a Hyperbolic Variational Auto-Encoder. © 2024, CC BY.

关键词： Geometry

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Improving Lidar Based Tracking of UAVs Using an Approach Based on Focusing

Improving Lidar Based Tracking of UAVs Using an Approach Bas...

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Iberian robotics Conference (ROBOT)

作者： Alberto Cerutti Sedat Dogru Lino Marques Department of Electronics and Telecommunications (DET) Polytechnic University of Turin Turin Italy Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

This paper presents a novel approach for detecting drones using a lidar sensor integrated on a robotic turret. The turret rotates allowing the sensor to scan the sky, and both hardware components communicate in real-time. When the lidar sensor detects a drone, the detection data is used to dynamically adjust the motion pattern of the turret, allowing the system to capture more data by orienting the lidar sensor towards the drone, eventually improving tracking accuracy thanks to the higher number of detections achieved in this manner. In this paper we also explore different motion patterns for the turret, comparing them on the field to track a drone using the built lidar-turret system.

关键词： Laser radar Accuracy Uncertainty Tracking Focusing Robot sensing systems Real-time systems Hardware Optimization Drones

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GISR: Geometric Initialization and Silhouette-based Refinement for Single-View Robot Pose and Configuration Estimation

arXiv

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arXiv 2024年

作者： Bilić, Ivan Marić, Filip Bonsignorio, Fabio Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia University of Toronto Institute for Aerospace Studies Space and Terrestrial Autonomous Robotic Systems Laboratory Canada

In autonomous robotics, measurement of the robot’s internal state and perception of its environment, including interaction with other agents such as collaborative robots, are essential. Estimating the pose of the robot arm from a single view has the potential to replace classical eye-to-hand calibration approaches and is particularly attractive for online estimation and dynamic environments. In addition to its pose, recovering the robot configuration provides a complete spatial understanding of the observed robot that can be used to anticipate the actions of other agents in advanced robotics use cases. Furthermore, this additional redundancy enables the planning and execution of recovery protocols in case of sensor failures or external disturbances. We introduce GISR - a deep configuration and robot-to-camera pose estimation method that prioritizes execution in real-time. GISR consists of two modules: (i) a geometric initialization module that efficiently computes an approximate robot pose and configuration, and (ii) a deep iterative silhouette-based refinement module that arrives at a final solution in just a few iterations. We evaluate GISR on publicly available data and show that it outperforms existing methods of the same class in terms of both speed and accuracy, and can compete with approaches that rely on ground-truth proprioception and recover only the pose. Our code is available at https://***/iwhitey/GISR-robot. © 2024, CC BY.

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TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place Recognition

arXiv

引用

arXiv 2023年

作者： Barros, Tiago Garrote, Luís Aleksandrov, Martin Premebida, Cristiano Nunes, Urbano J. The University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Portugal Dahlem Center for Machine Learning and Robotics Freie Universität Berlin Berlin Germany

Autonomous driving systems often require reliable loop closure detection to guarantee reduced localization drift. Recently, 3D LiDAR-based localization methods have used retrieval-based place recognition to find revisited places efficiently. However, when deployed in challenging real-world scenarios, the place recognition models become more complex, which comes at the cost of high computational demand. This work tackles this problem from an information-retrieval perspective, adopting a first-retrieve-then-re-ranking paradigm, where an initial loop candidate ranking, generated from a 3D place recognition model, is re-ordered by a proposed lightweight transformer-based re-ranking approach (TReR). The proposed approach relies on global descriptors only, being agnostic to the place recognition model. The experimental evaluation, conducted on the KITTI Odometry dataset, where we compared TReR with s.o.t.a. re-ranking approaches such as αQE and SGV, indicate the robustness and efficiency when compared to αQE while offering a good trade-off between robustness and efficiency when compared to SGV. © 2023, CC BY.

关键词： Efficiency

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Shape Sensing Stretchable Sensor Grid Towards Camera-Less Motion Capture

Shape Sensing Stretchable Sensor Grid Towards Camera-Less Mo...

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IEEE SENSORS

作者： Joseph Karam Aiden Shaevitz Joseph R. Davidson Matthew L. Johnston School of Electrical Engineering and Computer Science Oregon State University Corvallis OR USA Collaborative Robotics and Intelligent Systems Institute Oregon State University Corvallis OR USA

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

Stretchable electronics and conformal sensors are valuable for applications in soft robotics and wearable devices. Here, we demonstrate a stretchable sensor sheet with integrated readout for measuring strain and orientation across the sheet. Conductive liquid metal wires encased in silicone measure multi-axis strain. These strain sensors, paired with precision 9-axis IMUs mounted on PCBs embedded in the silicone, enable a shape-sensing, stretchable sensor grid. A prototype is presented first with a single strain sensor and IMU pair, which is charac-terized and calibrated for higher accuracy. Then, a larger scale stretchable grid with 24 strain sensors and 16 IMUs is presented. A four-wire liquid metal bus delivers power to the system and serves as a data communication bus. Long-term, our approach may enable motion capture and shape reconstruction without the need for external cameras or instrumented environments.

关键词： Liquids Shape Metals Soft robotics Strain measurement Capacitive sensors Motion capture Wearable devices Wearable sensors Strain

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Localization of Dengue Virus Likelihood Using Image Processing Technique 6

Localization of Dengue Virus Likelihood Using Image Processi...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Hosen, Md. Mahbub Islam, Md. Rahatul Tabassum, Faria Tonni, Syeda Thamina Noor, Malik Mohammad Nafiur Rahman, Shahid Northern University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Kyushu Institute of Technology Dept. of Human Intelligence Systems Wakamatsu Kitakyushu Japan Bangladesh University of Engineering and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Bangladesh Advance Robotics Research Center Human Intelligence Lab Dhaka Bangladesh Canadian University of Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350306613

The Aedes mosquito, found in tropical areas, transmits, and causes dengue fever. The spread of the dengue fever virus from an infected Aedes mosquito bite typically takes between three and fifteen days to manifest itself clinically as dengue fever symptoms. Early and automated detection of this illness will reduce the occurrence of outbreaks and facilitate the digitization of the system. Our primary objective is to identify regions at high risk for dengue fever using limited image data through the utilization of sophisticated machine learning and image processing methods. This work showcases the application of transfer learning algorithms for the timely identification of regions at risk for dengue mosquito infestation, particularly in situations where obtaining early data on dengue outbreaks is challenging. The experiments included images of 2150 dengue-positive data sets and 1440 dengue-negative data sets. We used AI and image classifiers Yolo V4 Tin, Tensor Flow, Google Colab, etc. for the training data set, and the system makes predictions with a 99.63% accuracy rate. Our system has some unique attributes;it not only detects the percentage of dengue probability but also the specific dengue-prone location on the submitted image. © 2024 IEEE.

关键词： aedes mosquito dengue-prone image processing yolo v4 tiny

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Camera Lidar Fusion for Unmanned Aerial Vehicle Detection

Camera Lidar Fusion for Unmanned Aerial Vehicle Detection

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Iberian robotics Conference (ROBOT)

作者： Cosimo Leuci Sedat Dogru Lino Marques Department of Electronics and Telecommunications (DET) Polytechnic University of Turin Turin Italy Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

Due to their low-cost and ease of use, cameras are very popular for drone detection. Thanks to the feature reach images obtained from the cameras, it is possible to classify drones with a relatively high success ratio. However, cameras lack the means for accurate localization, allowing only a rough measure of the orientation of the target UAV. In order to better localize the UAV, in this study we fuse camera and lidar measurements, localizing UAVs accurately. This fusion was implemented using a Kalman filter, which enables integration of both sensor data, improving detection and tracking accuracy

关键词： Location awareness YOLO Laser radar Accuracy Robot vision systems Cameras Autonomous aerial vehicles Kalman filters Vehicle dynamics Drones

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Movement-Based Interaction: An Approach to Visual and Auditory Stimulus and Memory*

Movement-Based Interaction: An Approach to Visual and Audito...

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Experiment@ International Conference (exp.at)

作者： Maria Rita Nogueira Paulo Menezes Institute of Systems and Robotics University of Coimbra Coimbra Portugal Dept. of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

ISBN: (数字)9798350314403

ISBN: (纸本)9798350314410

Movement-based interaction design is an innovative field that leverages the body's natural movements to create intuitive and engaging interfaces. This approach takes a sensorial approach to visual and auditory memory, seeking to enhance user engagement and create more meaningful experiences. The use of movement-based interfaces has significant implications for senior citizens, who may have mobility limitations or cognitive impairments. Using movement-based interfaces, seniors can engage in physical activity and maintain cognitive function, essential aspects for healthy aging. Movement-based interfaces can also help children develop physical coordination, cognitive skills, and social interaction. Furthermore, movement-based interaction design can benefit everyone's physical well-being by promoting physical activity and reducing sedentary behavior. Studies have demonstrated that movement-based interfaces can enhance posture, physical activity levels, and general health. By combining movement with sensory experiences, movement-based interaction design can create a more engaging and enjoyable experience for users, which can motivate them to maintain physical and cognitive health. In this article, we present two case studies that research the effectiveness of movement-based interaction design in enhancing visual and auditory memory. Overall, the results of these case studies suggest that movement-based interaction design is an effective approach to enhancing visual and auditory memory. The introduction of movement-based interfaces can give participants a more enjoyable and memorable experience, improving user engagement and information retention.

关键词： Visualization Pediatrics Aging Older adults

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