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42nd Chinese Control Conference, CCC 2023

作者： Zhang, Shuaikang Yuan, Mingxing Zhang, Xuebo Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing400065 China

ISBN: (纸本)9789887581543

At present, most CT scanning systems adopt the traditional horizontal O-frame, suspension or C-arm structures, which can not balance the flexibility and functionality. With the development of robot technology, CT scanning systems based on manipulators have gradually emerged. However, limited by the motion accuracy of the manipulator, most researchers sacrifice the flexibility or increase the mass to improve the accuracy. To overcome this limitation, a new master-slave teaching-based scanning method is proposed to improve the scanning accuracy of the dual-manipulator Robot-CT system with the auxiliary function of rigid links. First, a rigid link is employed to connect the end effectors of the two manipulators. Next, the sampled trajectory of each joint of the slave-manipulator is recorded while the master-manipulator pulls the slave-manipulator to perform a circular motion. Finally, when carrying out the scanning, the dual-manilupator system reproduces the recorded joint trajectory without the rigid element, thereby improving the accuracy of the dual-manipulator CT scanning system to achieve a high-accuracy spatiotemporal synchronization and alignment of pose. To this end, a fifth-order spline interpolation is used to interpolate the sampled trajectory of each joint of the slave-manipulator, which allows it to perfectly reproduce the joint trajectory recorded during the teaching process and ensures the smoothness of the angular velocity and acceleration curves. Simulations are investigated and the corresponding results confirm the effectiveness of the trajectory planning method of the slave-manipulator. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Manipulators

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A Space-Refine Paradigm for Automatic Carotid Artery Centerline Extraction in Magnetic Resonance Imaging*

A Space-Refine Paradigm for Automatic Carotid Artery Centerl...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Pu Zhang Jingmin Xin Jiayi Wu Nanning Zheng Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

Vessel centerline extraction is essential for carotid stenosis assessment and atherosclerotic plaque identification in clinical diagnosis. Simultaneously, it provides a region of interest identification and boundary initialization for computer-assisted diagnosis tools. In magnetic resonance imaging (MRI) cross-sectional images, the lumen shape and vascular topology result in a challenging task to extract the centerline accurately. To this end, we propose a space-refine framework, which exploits the positional continuity of the carotid artery from frame to frame to extract the carotid artery centerline. The proposed framework consists of a detector and a refinement module. Specifically, the detector roughly extracts the carotid lumen region from the original image. Then, we introduce a refinement module that uses the cascade of regressors from a detector to perform sequence realignment of lumen bounding boxes for each subject. It improves the lumen localization results and further enhances the centerline extraction accuracy. Verified by large carotid artery data, the proposed framework achieves state-of-the-art performance compared to conventional vessel centerline extraction methods or standard convolutional neural network *** relevance— Our proposed framework can be used as an important aid for physicians to quantitatively analyze the carotid artery in clinical practice. It is also used as a new paradigm for extracting the centerline of carotid vessels in computer-assisted tools.

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RGB-Depth Structure Similarity for Self-supervised Monocular Depth Estimation

RGB-Depth Structure Similarity for Self-supervised Monocular...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Lulu Zhang Meng Yang Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

Monocular depth estimation is a fundamental technique for robots to perceive the real (unseen) scene. Supervised methods rely on large-scale datasets with groundtruth (GT) depth labels, which cannot be well generalized to other scenes. A dominant solution is to directly train the model on target scenes in self-supervised way with pseudo depth labels (e.g. generated by stereo matching). However, pseudo depth labels are often unreliable especially near object boundaries. It may disturb the training of the model and consequently decrease the depth quality in the inference. In this paper, we investigate the structure similarity of RGB-Depth based on Gaussian kernels, because the structure of RGB image is always reliable. Such RGB-Depth structure similarity measurement is then used to improve the self-supervised depth estimation in two aspects. It is first utilized to measure the confidence of pseudo depth labels and filter unreliable pixels. It is then utilized to limit the structure of predicted depth maps in the loss. Experiments on the KITTI Eigen Splits datasets verify that the proposed method achieves better or comparable quantitative results and always achieves better visual results with clear depth boundaries compared with five recent baselines.

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Improved Adaptive Controller Design Scheme to Attenuate the Noise Amplification Effect of Differentials∗

Improved Adaptive Controller Design Scheme to Attenuate the ...

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2022 IEEE International Conference on Real-Time Computing and robotics, RCAR 2022

作者： Fa, Zhi Fan, Yongchun Wu, Yinan Liu, Chunhuan Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin China Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Adaptive control is widely used in nonlinear systems, and is under active research. Although the conventional adaptive control guarantees the asymptotic stability, it may yield limited performance in real experiments due to many factors, such as noise and hardware limitations. In this paper, a proportional-integral adaptive controller with a proportional-integral-derivative update law is proposed to attenuate the effect of measurement noise and improve the performance of the closed-loop system. Two more improvements, dividing differential signals into two smoother signals and facilitating additional estimations to weight different components of the same signal, are used in addition to the proportional-integral-derivative-Type update law. The stability is proved theoretically and the performance is verified by simulation tests. © 2022 IEEE.

关键词： Asymptotic stability

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Dynamic Modeling and Analysis for Parallel Robots Actuated by Pneumatic artificial Muscles 1

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3rd International Conference on Control, Instrumentation and Mechatronics Engineering, CIM 2022

作者： Wang, Qihang Liu, Gendi Yang, Tong Chen, He Qin, Yanding Sun, Ning Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Nankai University Tianjin300350 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China

ISBN: (数字)9789811939235

ISBN: (纸本)9789811939228

As a kind of pneumatic actuators, pneumatic artificial muscles (PAMs) possess remarkable merits, such as lightweight, compliance, higher security. However, with significant characteristics, e.g., strong bearing capacity, satisfactory dynamic performance, parallel robots used in industry are mostly actuated by traditional actuators. In this case, the compliance of PAMs is missing from parallel robots actuated by traditional actuators;thus, combining parallel robots with PAMs can make full use of the advantages of flexible structures and operation reliability. However, due to the complicated characteristics of PAMs and complex structures of parallel robots, the modeling of PAM-actuated parallel robots is an intractable problem. Therefore, in this paper, we use Lagrange’s method and the three-element model to obtain the dynamic model of PAM-actuated parallel robots. Then, by utilizing a proportional derivative (PD) control method with gravity compensation, simulation results validate the feasibility of the proposed model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Pneumatic actuators

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A Novel Map Representation for Automatic Construction of Topological Map and Lane-Level Route Planning

A Novel Map Representation for Automatic Construction of Top...

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Chinese Automation Congress (CAC)

作者： Jian Sun Jianru Xue Gengxin Li Jingcheng Liu Institute of Artificial Intelligence and Robotics Xi’ an Jiaotong University Xi'an China

Traditional road-level topological maps cannot meet the demand for high precision navigation services required by human drivers and autonomous vehicles. Meanwhile, current enhanced lane-level topological maps with redundant details pose practical challenges for automatic map construction and efficient lane-level route planning. To address these challenges, this paper proposes a novel topological map representation. Based on this representation, a hierarchical mapping framework is established, which includes the extraction of road primitives, generation of directed graph, and automatic construction of lane-level topological map. The constructed topological map enables efficient lane-level route planning by the shortest path search algorithm with having the minimal nodes of topological graph. The proposed mapping framework and lane-level route planning have been extensively tested and evaluated on two real urban traffic scenarios, demonstrating the feasibility and effectiveness of this novel topological map representation.

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Learning Object Relation Graph and Goal-Aware Policy For Visual Room Rearrangement

Learning Object Relation Graph and Goal-Aware Policy For Vis...

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Chinese Automation Congress (CAC)

作者： Beibei Wang Xiaohan Wang Yuehu Liu College of Artificial Intelligence Xi'an Jiaotong University Xi'an China Institute of Artificial Intelligence and Robotics College of Artificial Intelligence Xi'an Jiaotong University Xi'an China

Embodied AI, where agents accomplish specific tasks through interaction with their surrounding environment, is attracting attention in the community. As a more comprehensive and practical embodied task, visual room rearrangement aims to rearrange the initially misplaced objects in a room to the target configuration. In this task, the misplaced targets are mostly small objects that are difficult to locate, and there is no indication of which object needs to be rearranged in sensory input. Direct search for small and unknown targets poses a challenge. Previous works tend to directly incorporate sensory input into a reward-based framework. These methods do not learn the relationships between objects and infer which objects need to be rearranged, resulting in low performance. Understanding the scene structure and reasoning task goals can enable agents to rearrange targets more efficiently. In this paper, we propose a receptacle-target relation graph, which considers the spatial relationship between target objects and the receptacle objects appearing around them, enabling agents to understand the environment. Furthermore, we also propose a target reasoning network to explicitly predict the target object. Finally, our goal-aware policy can plan a series of actions based on their understanding of the task goal. Extensive compmethodarative and ablation experiments were conducted on the AI2Thor platform, demonstrating the effectiveness of our method.

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A deep convolutional neural network for diabetic retinopathy detection via mining local and long-range dependence

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CAAI Transactions on intelligence Technology 2024年第1期9卷 153-166页

作者： Xiaoling Luo Wei Wang Yong Xu Zhihui Lai Xiaopeng Jin Bob Zhang David Zhang Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of TechnologyShenzhenChina Peng Cheng Laboratory ShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina College of Big Data and Internet Shenzhen Technology UniversityShenzhenChina The Department of Computer and Information Science University of MacaoMacaoMacaoChina The Chinese University of Hong Kong(Shenzhen) ShenzhenChina

Diabetic retinopathy(DR),the main cause of irreversible blindness,is one of the most common complications of *** present,deep convolutional neural networks have achieved promising performance in automatic DR detection *** convolution operation of methods is a local cross-correlation operation,whose receptive field de-termines the size of the local neighbourhood for ***,for retinal fundus photographs,there is not only the local information but also long-distance dependence between the lesion features(*** and exudates)scattered throughout the whole *** proposed method incorporates correlations between long-range patches into the deep learning framework to improve DR ***-wise re-lationships are used to enhance the local patch features since lesions of DR usually appear as *** Long-Range unit in the proposed network with a residual structure can be flexibly embedded into other trained *** experimental results demon-strate that the proposed approach can achieve higher accuracy than existing state-of-the-art models on Messidor and EyePACS datasets.

关键词： image classification medical image processing pattern recognition

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Development of a Demolition System and Collision Detection Algorithm

Development of a Demolition System and Collision Detection A...

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2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Wang, Chengjiang Mu, Zonggao Ding, Ning Qian, Huihuan Xu, Yangsheng The Chinese University of Hong Kong School of Science and Engineering Shenzhen China The Chinese University of Hong Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Shandong University of Technology School of Mechanical Engineering China

ISBN: (纸本)9798350372601

This paper presents a demolition system, which is composed of a spherical inspection robot and a demolition robot. For the former, it is designed to get information about the demolition scene. The demolition robot with three-fold arm was developed to perform demolition tasks. The arm can be folded if necessary to pass through narrow space, which improves its flexibility. Besides that, to protect the arm while working, a modified oriented bounding box method was proposed to detect the potential collision between the robot and the obstacles. This method not only offers the advantages of simplicity and feasibility, but also takes into consideration the computational requirements. Besides that, an integrated simulation system is established based on Simulink, which demonstrates the relative motion intuitively. The simulation and experimental results provide evidence of the effectiveness of collision detection. © 2024 IEEE.

关键词： Demolition

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CuES: Conditional Uncorrelation-based Characteristic Enhancement and Fusion of Electrical Signals

CuES: Conditional Uncorrelation-based Characteristic Enhance...

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International Conference on Information Fusion

作者： Zhaohui Wang Yue Zhang Jin Zhou Haohao Cai Xichun Liu Jianji Wang Nanning Zheng Institute of Artificial Intelligence and Robotics College of Artificial Intelligence Xi’an Jiaotong University Xi’an China DongFang Electric machinery Co. Ltd Deyang China

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

The lifespan of a generator greatly depends on the quality and aging of its stator bar insulation material. Aging of insulation materials can lead to premature equipment failure and significant material loss, resulting in substantial economic losses. However, existing methods for predicting the lifespan of electronic wire bars have several drawbacks, such as slow training speed, the need for a large amount of training data, and a tendency to overfit. To address this issue, we propose a characteristic enhancement algorithm based on conditional uncorrelation. This algorithm leverages characteristic enhancement to generate an extensive dataset and utilizes subset selection to identify relevant electrical parameters for predicting the remaining life span of the stator bar’s main insulation configurations. Experimental results demonstrate the advantages of our research compared to deep learning models. Our approach offers a promising solution for accurately predicting the remaining life of stator bar insulation, thereby facilitating effective maintenance planning and minimizing economic losses.

关键词： Economics Deep learning Insulation Accuracy Biological system modeling Aging Stator bars Prediction algorithms Generators Maintenance

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