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arXiv 2022年

作者： Wei, Yi Zhao, Linqing Zheng, Wenzhao Zhu, Zheng Rao, Yongming Huang, Guan Lu, Jiwen Zhou, Jie Beijing National Research Center for Information Science and Technology China Department of Automation Tsinghua University China School of Electrical and Information Engineering Tianjin University China PhiGent Robotics

Depth estimation from images serves as the fundamental step of 3D perception for autonomous driving and is an economical alternative to expensive depth sensors like LiDAR. The temporal photometric constraints enables self-supervised depth estimation without labels, further facilitating its application. However, most existing methods predict the depth solely based on each monocular image and ignore the correlations among multiple surrounding cameras, which are typically available for modern self-driving vehicles. In this paper, we propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras. Specifically, we employ a joint network to process all the surrounding views and propose a cross-view transformer to effectively fuse the information from multiple views. We apply cross-view self-attention to efficiently enable the global interactions between multi-camera feature maps. Different from self-supervised monocular depth estimation, we are able to predict real-world scales given multi-camera extrinsic matrices. To achieve this goal, we adopt the two-frame structure-from-motion to extract scale-aware pseudo depths to pretrain the models. Further, instead of predicting the ego-motion of each individual camera, we estimate a universal ego-motion of the vehicle and transfer it to each view to achieve multi-view ego-motion consistency. In experiments, our method achieves the state-of-the-art performance on the challenging multi-camera depth estimation datasets DDAD and nuScenes. Code is available at https://***/weiyithu/SurroundDepth. Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

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FabricFolding: Learning Efficient Fabric Folding without Expert Demonstrations

arXiv

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arXiv 2023年

作者： He, Can Meng, Lingxiao Sun, Zhirui Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China

Autonomous fabric manipulation is a challenging task due to complex dynamics and potential self-occlusion during fabric handling. An intuitive method of fabric folding manipulation first involves obtaining a smooth and unfolded fabric configuration before the folding process begins. However, the combination of quasi-static actions such as pick & place and dynamic action like fling proves inadequate in effectively unfolding long-sleeved T-shirts with sleeves mostly tucked inside the garment. To address this limitation, this paper introduces an improved quasi-static action called pick & drag, specifically designed to handle this type of fabric configuration. Additionally, an efficient dual-arm manipulation system is designed in this paper, which combines quasi-static (including pick & place and pick & drag) and dynamic fling actions to flexibly manipulate fabrics into unfolded and smooth configurations. Subsequently, keypoints of the fabric are detected, enabling autonomous folding. To address the scarcity of publicly available keypoint detection datasets for real fabric, we gathered images of various fabric configurations and types in real scenes to create a comprehensive keypoint dataset for fabric folding. This dataset aims to enhance the success rate of keypoint detection. Moreover, we evaluate the effectiveness of our proposed system in real-world settings, where it consistently and reliably unfolds and folds various types of fabrics, including challenging situations such as long-sleeved T-shirts with most parts of sleeves tucked inside the garment. Specifically, our method achieves a coverage rate of 0.822 and a success rate of 0.88 for long-sleeved T-shirts folding. Copyright © 2023, The Authors. All rights reserved.

关键词： Drag

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case

Optimal Linear Deception Attacks on Remote State Estimation ...

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IEEE Conference on Decision and Control

作者： Jun Shang Hanwen Zhang Jing Zhou Tongwen Chen Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems and Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack’s stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback–Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples.

关键词： Wireless communication Measurement Technological innovation System performance Kalman filters State estimation Intelligent sensors

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Modular Adaptive Aerial Manipulation under Unknown Dynamic Coupling Forces

arXiv

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arXiv 2024年

作者： Yadav, Rishabh Dev Dantu, Swati Pan, Wei Sun, Sihao Roy, Spandan Baldi, Simone Department of Computer Science The Univerity of Manchester United Kingdom Multi-robot systems group Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Department of Cognitive Robotics Delft Univesrity of Technology Netherlands Robotics Research Center International Institute of Information Technology Hyderabad India School of Mathematics Southeast University Nanjing China

Successful aerial manipulation largely depends on how effectively a controller can tackle the coupling dynamic forces between the aerial vehicle and the manipulator. However, this control problem has remained largely unsolved as the existing control approaches either require precise knowledge of the aerial vehicle/manipulator inertial couplings, or neglect the state-dependent uncertainties especially arising during the interaction phase. This work proposes an adaptive control solution to overcome this long standing control challenge without any a priori knowledge of the coupling dynamic terms. Additionally, in contrast to the existing adaptive control solutions, the proposed control framework is modular, that is, it allows independent tuning of the adaptive gains for the vehicle position sub-dynamics, the vehicle attitude sub-dynamics, and the manipulator sub-dynamics. Stability of the closed loop under the proposed scheme is derived analytically, and real-time experiments validate the effectiveness of the proposed scheme over the state-of-the-art approaches. © 2024, CC BY.

关键词： Adaptive control systems

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MMA-Net: Multiple Morphology-Aware Network for Automated Cobb Angle Measurement

arXiv

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arXiv 2023年

作者： Qiu, Zhengxuan Yang, Jie Wang, Jiankun Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Jiaxing Research Institute Southern University of Science and Technology Jiaxing China

Scoliosis diagnosis and assessment depend largely on the measurement of the Cobb angle in spine X-ray images. With the emergence of deep learning techniques that employ landmark detection, tilt prediction, and spine segmentation, automated Cobb angle measurement has become increasingly popular. However, these methods encounter difficulties such as high noise sensitivity, intricate computational procedures, and exclusive reliance on a single type of morphological information. In this paper, we introduce the Multiple Morphology-Aware Network (MMA-Net), a novel framework that improves Cobb angle measurement accuracy by integrating multiple spine morphology as attention information. In the MMA-Net, we first feed spine X-ray images into the segmentation network to produce multiple morphological information (spine region, centerline, and boundary) and then concatenate the original X-ray image with the resulting segmentation maps as input for the regression module to perform precise Cobb angle measurement. Furthermore, we devise joint loss functions for our segmentation and regression network training, respectively. We evaluate our method on the AASCE challenge dataset and achieve superior performance with the SMAPE of 7.28% and the MAE of 3.18°, indicating a strong competitiveness compared to other outstanding methods. Consequently, we can offer clinicians automated, efficient, and reliable Cobb angle measurement. Copyright © 2023, The Authors. All rights reserved.

关键词： Angle measurement

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Air-Ground Collaborative Robots for Fire and Rescue Missions: Towards Mapping and Navigation Perspective

arXiv

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arXiv 2024年

作者： Zhang, Ying Yan, Haibao Zhu, Danni Wang, Jiankun Zhang, Cui-Hua Ding, Weili Luo, Xi Hua, Changchun Meng, Max Q.-H. School of Electrical Engineering The Hebei Key Laboratory of Intelligent Rehabilitation and Neuromodulation Yanshan University Qinhuangdao066004 China Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China Jiaxing Research Institute Southern University of Science and Technology Jiaxing314031 China

Air-ground collaborative robots have shown great potential in the field of fire and rescue. Mapping and navigation, as the key foundation for air-ground collaborative robots to achieve efficient task execution, have attracted a great deal of attention. This growing interest in collaborative robot mapping and navigation is conducive to improving the intelligence of fire and rescue task execution, but there has been no comprehensive investigation of this field to highlight their strengths. In this paper, we present a systematic review of the ground-to-ground cooperative robots for fire and rescue from a new perspective of mapping and navigation. First, an air-ground collaborative robots framework for fire and rescue missions based on unmanned aerial vehicle (UAV) mapping and unmanned ground vehicle (UGV) navigation is introduced. Then, the research progress of mapping and navigation under this framework is systematically summarized, including UAV mapping, UAV/UGV co-localization, and UGV navigation, with their main achievements and limitations. Based on the needs of fire and rescue missions, the collaborative robots with different numbers of UAVs and UGVs are classified, and their practicality in fire and rescue tasks is elaborated, with a focus on the discussion of their merits and demerits. In addition, the application examples of air-ground collaborative robots in various firefighting and rescue scenarios are given. Finally, this paper emphasizes the current challenges and potential research opportunities, rounding up references for practitioners and researchers willing to engage in this vibrant area of air-ground collaborative robots. Copyright © 2024, The Authors. All rights reserved.

关键词： Collaborative robots

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Edge AI Algorithm for FBG-Based E-Skin Touch Localization on Embedded Electronics

Edge AI Algorithm for FBG-Based E-Skin Touch Localization on...

引用

IEEE SENSORS

作者： Elisabetta Leogrande Francesco Dell'Olio Stefano Mazzoleni Calogero Maria Oddo Mariangela Filosa Department of Electrical and Information Engineering Polytechnic University of Bari Italy Department of Excellence in Robotics & AI The BioRobotics Institute Sant' Anna School of Advanced Studies Pisa Italy Interdisciplinary Research Center Health Science Sant’ Anna School of Advanced Studies Pisa Italy

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

The field of collaborative robotics is constantly growing given the central role that safe human-robot interactions are gaining in the current era of Industry 4.0. To act and interact, robotic systems are increasingly being either natively equipped with or adapted to house sensing devices. In this scenario, tactile sensors are fundamental to detect impacts, accurately manipulate objects and enable learning from demonstration strategies. The development of such systems requires technological efforts in terms of both design, for their seamless integration in robots, and software programming, for achieving reliable and prompt reactions to relevant events. In this study, an edge AI algorithm deployed on an embedded device for the recognition of contacts on an FBG (Fiber Bragg Grating)-based e-skin is presented. A Convolutional Neural Network has been trained offline to localize touch events occurred on the e-skin. The performance of the algorithm assessed on the test set reached a mean absolute error on the three cartesian coordinates of less than 4 mm. The algorithm has been then ported to a microcontroller board for real-time testing of both simulated and concurrent contacts. The achievements of this work pave the way to new opportunities for collaborative robotics applications through intelligent FBG-based artificial tactile skins.

关键词： Service robots Robot kinematics Software algorithms Tactile sensors Collaboration Edge AI Fiber gratings Software Software reliability Testing

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Indoor Exploration and Simultaneous Trolley Collection Through Task-Oriented Environment Partitioning

arXiv

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arXiv 2023年

作者： Gao, Junjie Xie, Peijia Gao, Xuheng Sun, Zhirui Wang, Jiankun Meng, Max Q.-H. The Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

In this paper, we present a simultaneous exploration and object search framework for the application of autonomous trolley collection. For environment representation, a task-oriented environment partitioning algorithm is presented to extract diverse information for each sub-task. First, LiDAR data is classified as potential objects, walls, and obstacles after outlier removal. Segmented point clouds are then transformed into a hybrid map with the following functional components: object proposals to avoid missing trolleys during exploration;room layouts for semantic space segmentation;and polygonal obstacles containing geometry information for efficient motion planning. For exploration and simultaneous trolley collection, we propose an efficient exploration-based object search method. First, a traveling salesman problem with precedence constraints (TSP-PC) is formulated by grouping frontiers and object proposals. The next target is selected by prioritizing object search while avoiding excessive robot backtracking. Then, feasible trajectories with adequate obstacle clearance are generated by topological graph search. We validate the proposed framework through simulations and demonstrate the system with real-world autonomous trolley collection tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Traveling salesman problem

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Neural Augmentation Based Panoramic High Dynamic Range Stitching

arXiv

引用

arXiv 2024年

作者： Zheng, Chaobing Xu, Yilun Chen, Weihai Wu, Shiqian Zhang, Sen Li, Zhengguo Institute of Robotics and Intelligent Systems School of Electronic Information Wuhan University of Science and Technology 947 Heping Avenue Wuhan430081 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China School of Automation and Electrical Engineering University of Science and Technology Beiing Beijing100083 China Key Laboratory of Knowledge Auomalion for indusrial Proceses of Mimistry ofEducation University of Science and Techmology Beijing Beijing1083 China Shunde lnnovation School University of Science and Technology Beijing Foshan 528300 China VI Department Institute for Infocomm Research A*STAR 138632 Singapore

Due to saturated regions of inputting low dynamic range (LDR) images and large intensity changes among the LDR images caused by different exposures, it is challenging to produce an information enriched panoramic LDR image without visual artifacts for a high dynamic range (HDR) scene through stitching multiple geometrically synchronized LDR images with different exposures and pairwise overlapping fields of views (OFOVs). Fortunately, the stitching of such images is innately a perfect scenario for the fusion of a physics-driven approach and a data-driven approach due to their OFOVs. Based on this new insight, a novel neural augmentation based panoramic HDR stitching algorithm is proposed in this paper. The physics-driven approach is built up using the OFOVs. Different exposed images of each view are initially generated by using the physics-driven approach, are then refined by a data-driven approach, and are finally used to produce panoramic LDR images with different exposures. All the panoramic LDR images with different exposures are combined together via a multi-scale exposure fusion algorithm to produce the final panoramic LDR image. Experimental results demonstrate the proposed algorithm outperforms existing panoramic stitching algorithms. © 2024, CC0.

关键词： Image fusion

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AWGUNET: ATTENTION-AIDED WAVELET GUIDED U-NET FOR NUCLEI SEGMENTATION IN HISTOPATHOLOGY IMAGES

arXiv

引用

arXiv 2024年

作者： Roy, Ayush Pramanik, Payel Kaplun, Dmitrii Antonov, Sergei Sarkar, Ram Department of Electrical Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University"LETI" Saint Petersburg Russia

Accurate nuclei segmentation in histopathological images is crucial for cancer diagnosis. Automating this process offers valuable support to clinical experts, as manual annotation is time-consuming and prone to human errors. However, automating nuclei segmentation presents challenges due to uncertain cell boundaries, intricate staining, and diverse structures. In this paper, we present a segmentation approach that combines the U-Net architecture with a DenseNet-121 backbone, harnessing the strengths of both to capture comprehensive contextual and spatial information. Our model introduces the Wavelet-guided channel attention module to enhance cell boundary delineation, along with a learnable weighted global attention module for channel-specific attention. The decoder module, composed of an upsample block and convolution block, further refines segmentation in handling staining patterns. The experimental results conducted on two publicly accessible histopathology datasets, namely Monuseg and TNBC, underscore the superiority of our proposed model, demonstrating its potential to advance histopathological image analysis and cancer diagnosis. The code is made available at: https://***/AyushRoy2001/AWGUNET © 2024, CC BY.

关键词： Image segmentation

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