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arXiv 2021年

作者： Barros, Tiago Garrote, Luís Pereira, Ricardo Premebida, Cristiano Nunes, Urbano J. University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Portugal

LiDAR-based place recognition is one of the key components of SLAM and global localization in autonomous vehicles and robotics applications. With the success of DL approaches in learning useful information from 3D LiDARs, place recognition has also benefited from this modality, which has led to higher re-localization and loop-closure detection performance, particularly, in environments with significant changing conditions. Despite the progress in this field, the extraction of proper and efficient descriptors from 3D LiDAR data that are invariant to changing conditions and orientation is still an unsolved challenge. To address this problem, this work proposes a novel 3D LiDAR-based deep learning network (named AttDLNet) that uses a range-based proxy representation for point clouds and an attention network with stacked attention layers to selectively focus on long-range context and inter-feature relationships. The proposed network is trained and validated on the KITTI dataset and an ablation study is presented to assess the novel attention network. Results show that adding attention to the network improves performance, leading to efficient loop closures, and outperforming an established 3D LiDAR-based place recognition approach. From the ablation study, results indicate that the middle encoder layers have the highest mean performance, while deeper layers are more robust to orientation change. The code is publicly available at:https://***/Cybonic/AttDLNet. © 2021, CC BY.

关键词： Ablation

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QTER: QoS-Aware Three-Dimensional Efficient and Reliable Routing for LEO Satellite Networks

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IEEE Internet of Things Journal 2025年

作者： Zheng, Jinkai Luan, Tom H. Li, Guanjie Zhang, Yanfeng Yuan, Mingfeng Pan, Jianping Dongguan University of Technology School of Electrical Engineering and Intelligentization China Xi’an Jiaotong University China Xidian University School of Cyber Engineering China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore University of Toronto Institute for Aerospace Studies University of Toronto Robotics Institute Canada University of Victoria Department of Computer Science Canada

Low Earth Orbit (LEO) satellite networks offer a promising approach for achieving global coverage and high-speed Internet access. However, the existing communication frameworks within these networks may result in low robustness and reliability. This paper introduces QTER, a novel and lightweight routing framework specifically designed for LEO networks, aimed at addressing these challenges. Our contributions are threefold. First, QTER establishes a cost-effective and reliable routing framework that accommodates a variety of applications with distinct Quality of Service (QoS) requirements. By enabling multi-path routing, the framework minimizes costs while ensuring end-to-end reliability, thus enhancing adaptability to diverse service demands. Second, we leverage the unique structural characteristics of LEO satellite networks by modeling the satellite constellation as a three-dimensional network, wherein higher-shell satellites serve as management satellite nodes (MSNs) to coordinate routing strategies among lower-shell satellites. This architecture significantly improves system efficiency and adaptability. Third, we propose a failure recovery mechanism that allows MSNs to relay packets when lower-orbit satellites are rendered unavailable due to environmental factors, thereby enhancing system robustness. Extensive simulations demonstrate that QTER exhibits resilience to node failures and dynamic network conditions, achieving reductions in average cost and delay by 59.4% and 38.9%, respectively, compared to baselines. © 2014 IEEE.

关键词： Resource allocation

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Towards Connecting Control to Perception: High-Performance Whole-Body Collision Avoidance Using Control-Compatible Obstacles

arXiv

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arXiv 2023年

作者： Eckhoff, Moritz Knobbe, Dennis Zwirnmann, Henning Swikir, Abdalla Haddadin, Sami Technical University of Munich Germany TUM School of Computation Information and Technology Department of Computer Engineering Chair of Robotics and Systems Intelligence Germany Germany Technische Universität Dresden Germany Department of Electrical and Electronic Engineering Omar Al-Mukhtar University Albaida Libya

One of the most important aspects of autonomous systems is safety. This includes ensuring safe human-robot and safe robot-environment interaction when autonomously performing complex tasks or in collaborative scenarios. Although several methods have been introduced to tackle this, most are unsuitable for real-time applications and require carefully hand-crafted obstacle descriptions. In this work, we propose a method combining high-frequency and real-time self and environment collision avoidance of a robotic manipulator with low-frequency, multimodal, and high-resolution environmental perceptions accumulated in a digital twin system. Our method is based on geometric primitives, so-called primitive skeletons. These, in turn, are information-compressed and realtime compatible digital representations of the robot's body and environment, automatically generated from ultra-realistic virtual replicas of the real world provided by the digital twin. Our approach is a key enabler for closing the loop between environment perception and robot control by providing the millisecond real-time control stage with a current and accurate world description, empowering it to react to environmental changes. We evaluate our whole-body collision avoidance on a 9-DOFs robot system through five experiments, demonstrating the functionality and efficiency of our framework. Copyright © 2023, The Authors. All rights reserved.

关键词： Collision avoidance

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Efficient Segmentation of Intraoperative Anatomical Landmarks in Laparoscopic Cholecystectomy Based on Deep Learning

SSRN

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SSRN 2024年

作者： Yang, Zongjin Wen, Jun Huang, Deqing Yang, Aisen Zhang, Rong Ren, Bo Chen, Zhenhao Yin, Yirui Qin, Na Institute of Systems Science and Technology School of Electrical Engineering Southwest Jiaotong University Chengdu611756 China Section for HepatoPancreatoBiliary Surgery Department of General Surgery The Third People’s Hospital of Chengdu Chengdu610031 China

Background Laparoscopic cholecystectomy (LC) is the gold standard procedure for the treatment of benign gallbladder diseases, but there is the risk of intraoperative bile duct injury, which can lead to surgical failure and cause significant social and economic burden. When surgeons rely on visual inspection to identify tissue structures during LC, subjective factors such as experience, psychological factors, and fatigue can compromise the intraoperative recognition of anatomic landmarks. The positioning of anatomical landmarks by the surgeon in the pre-dissection phase of LC is relatively vague and requires step-by-step exploration as the surgery progresses, becoming clearer in the post-dissection *** To alleviate the pressure on surgeons during procedures, this study aimed to achieve real-time intraoperative navigation during LC by dynamically identifying and annotating key anatomical landmarks, including the gallbladder, Calot's triangle, and common bile duct. The study proposed a novel semantic segmentation neural network called the Channel Attention Pyramid Scene Parsing Plus Network (CPPN). The network utilized pooling layers with different scales and assigned non-equal weights to extract feature information. Additionally, a spatial channel attention module was added to accurately capture important features or contextual information, improving the model's performance and effectiveness. Training was conducted using video frames from the pre-dissection phase, while testing used video frames from the post-dissection *** All models were subjected to a 10-fold cross-validation on 1425 selected frames from 132 LC videos, with training and validation conducted in two separate LC stages. The proposed model CPPN achieved a mean intersection over union (mIoU) of 0.855 (±0.03), outperforming other segmentation neural networks. The model demonstrated optimal performance across most metrics, with an intersection over union (IoU) of 0.881 (±0.01) for the g

关键词： Deep learning

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Place recognition survey: An update on deep learning approaches

arXiv

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arXiv 2021年

作者： Barros, Tiago Pereira, Ricardo Garrote, Luís Premebida, Cristiano Nunes, Urbano J. The University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Portugal

Autonomous Vehicles (AV) are becoming more capable of navigating in complex environments with dynamic and changing conditions. A key component that enables these intelligent vehicles to overcome such conditions and become more autonomous is the sophistication of the perception and localization systems. As part of the localization system, place recognition has benefited from recent developments in other perception tasks such as place categorization or object recognition, namely with the emergence of deep learning (DL) frameworks. This paper surveys recent approaches and methods used in place recognition, particularly those based on deep learning. The contributions of this work are twofold: surveying recent sensors such as 3D LiDARs and RADARs, applied in place recognition;and categorizing the various DL-based place recognition works into supervised, unsupervised, semi-supervised, parallel, and hierarchical categories. First, this survey introduces key place recognition concepts to contextualize the reader. Then, sensor characteristics are addressed. This survey proceeds by elaborating on the various DL-based works, presenting summaries for each framework. Some lessons learned from this survey include: the importance of NetVLAD for supervised end-to-end learning;the advantages of unsupervised approaches in place recognition, namely for cross-domain applications;or the increasing tendency of recent works to seek, not only for higher performance but also for higher efficiency. © 2021, CC BY.

关键词： Surveys

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An Augmented Reality Application for Upper-Limb Rehabilitation Supported by a Robotic Arm

An Augmented Reality Application for Upper-Limb Rehabilitati...

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Experiment@ International Conference (exp.at)

作者： José Carlos Rodrigues Maria Teresa Restivo Paulo Menezes LAETA-INEGI Faculty of Engineering University of Porto Porto Portugal LAETA-INEGI University of Porto Porto Portugal A3ES Lisbon Portugal Department of Electrical and Computer Engineering University of Coimbra Institute of Systems and Robotics Coimbra Portugal

ISBN: (数字)9798350314403

ISBN: (纸本)9798350314410

This work looks at the potential use of a robotic arm and augmented reality application, with a head-mounted display, in an upper-limb rehabilitation context. A sample application using a robotic simulator and a cross-sectional study using a real robotic arm are shown. Results from the usability evaluation are positive and encourage further development, but some identified problems must be corrected before further evaluations.

关键词： Head-mounted displays Sociology Medical treatment Collaborative robots Manipulators Real-time systems Usability

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Thermal Properties of Refractory SWCNT-Based Nanocomposites Investigated by Automated Approach 17

Thermal Properties of Refractory SWCNT-Based Nanocomposites ...

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17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer engineering, TCSET 2024

作者： Zhydenko, Illia Klym, Halyna Karbovnyk, Ivan Kostiv, Yuriy Vasylchyshyn, Ivanna Chalyy, Dmytro Lviv State University of Life Safety Department of fire tactics and emergency rescue work Lviv Ukraine Lviv Polytechnic National University Specialized Computer Systems Dpt. Lviv Ukraine Ivan Franko National University of Lviv Radiophysics and Computer Technology Dpt. Lviv Ukraine Lviv Polytechnic National University Information Technology Security Dpt. Lviv Ukraine Lviv Polytechnic National University Department of Theoretical and General Electrical Engineering Lviv Ukraine

ISBN: (纸本)9798331520564

Thermal properties of refractory nanocomposites with embedded single-walled carbon nanotubes were investigated using proposed automated approach. In this approach, differences in nanocomposites with varying parameters, observed under temperature differentials and thermal loads, are highlighted. These findings are crucial for their integration into sensor systems and for applications requiring enhanced fire resistance. The results indicate that nanocomposites with a nanotube dispersion time of 2 hours exhibit a peak temperature difference of 55 'C, while those with a 1-hour dispersion time show a maximum difference of 50 °C. A comparative analysis of heat transfer dynamics suggests that extending the dispersion time by 1 hour increases fire resistance by 15%. © 2024 IEEE.

关键词： Nanoclay

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Experimental Evaluation of Data Fusion Techniques and Adaptive Control for Mobile Robot Localization

Experimental Evaluation of Data Fusion Techniques and Adapti...

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International Carpathian Control Conference (ICCC)

作者： Wolmar Araújo Neto Daniel Khede Dourado Villa Mário Sarcinelli-Filho Murillo Ferreira Dos Santos José Lima Ana Isabel Pereira Maurício Herche Fófano De Morais Paolo Mercorelli Department of Electrical Engineering Federal University of Vitória (UFES) Vitória Brazil Department of Electroelectronics CEFET-MG Leopoldina Brasil IPB Research Centre in Digitalization and Intelligent Robotics (CeDRI) Bragança Portugal Institute for Production Technology and Systems Leuphana University of Lüneburg Lüneburg Germany

ISBN: (数字)9798331501273

ISBN: (纸本)9798331501280

This study presents an approach to improving the localization of mobile robots in a controlled test environment through sensor fusion. Currently, the position of the ground robot (Unmanned Ground Vehicle (UGV) P3DX) can be estimated using an optimization algorithm or another technique based on a previously known map. In contrast, the aerial robot (Unmanned Aerial Vehicle (UAV) Bebop Parrot 2) can determine its relative position by detecting a marker on the ground robot using the Robot Operating System (ROS) WhyCon package. However, ambiguities or disturbances may compromise the accuracy of the robots’ localization. To mitigate these limitations, Ultra-WideBand (UWB) sensors were incorporated into the system, enabling a more robust data fusion by integrating information from multiple sensors. Additionally, a filter was applied to reduce the impact of high-frequency noise on position estimation. Accurate localization test data were used to construct a simulated scenario and analyze the performance of the proposed approach. The simulation results demonstrate that sensor fusion, combined with noise filtering, significantly improves localization accuracy, making the approach promising for applications in mobile robot navigation.

关键词： Location awareness Accuracy Three-dimensional displays Navigation Noise Data integration Sensor fusion Robot sensing systems Autonomous aerial vehicles Mobile robots

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells

Application of Deep Learning Method to Estimate Bottomhole P...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng Valeriy Vyatkin 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 Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry.

关键词：

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Functional Mode Switching for Safe and Efficient Human-Robot Interaction

Functional Mode Switching for Safe and Efficient Human-Robot...

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IEEE-RAS International Conference on Humanoid Robots

作者： Petr Svarny Mazin Hamad Alexander Kurdas Matej Hoffmann Saeed Abdolshah Sami Haddadin Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Department of Computer Engineering Chair of Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence School of Computation Information and Technology Technische Universität München (TUM)

ISBN: (纸本)9798350309805

Various approaches can ascertain the safety and efficiency of physical human-robot collaboration. This paper presents the concept of robot functional mode switching to efficiently ensure human safety during collaborative tasks based on biomechanical pain and injury data and task information. Besides the robot's reflected inertial properties summarizing its impact dynamics, our concept also integrates safe and smooth velocity shaping that respects human partner motion, interaction type, and task knowledge. We further discuss different approaches to safely shape the robot velocity without sacrificing the overall task execution time and motion smoothness. The experimental results showed that our proposed approaches could decrease jerk level during functional mode switching and limit the impact of safety measures on productivity, especially when guided with additional task knowledge.

关键词： Robot motion Service robots Human-robot interaction Humanoid robots Switches Interference Safety

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