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Chinese Automation Congress (CAC)

作者： Tao Zhang Yingjie Xiong Bolun Wang Hongyu Yan Xiyou Chen School of Electrical Engineering Dalian University of Technology Dalian China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China College Of Information Science and Engineering Northeastern University Shenyang China School of Artificial Intelligence Shenyang University of Technology Shenyang China

ISBN: (纸本)9781665426480

Permanent magnet synchronous motors(PMSM) are popular in AC motor applications due to their small size, high efficiency. They have appeared widely in various areas of life. Due to the high-frequency chattering problem caused by sliding mode observer sensorless control of PMSM, an SMO with piecewise control function is designed in this paper. At the same time, based on the back EMF output of the SMO, the gain of the SMO is adapted to reduce the system bucket vibration problem caused by the conventional SMO. Finally, a simulation experiment was carried out in MATLAB/SIMULINK and compared with the conventional SMO. The results show that the improved SMO has achieved a better control effect.

关键词： Vibrations Adaptation models AC motors Force Sensorless control Observers Permanent magnet motors

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VFH∗ Based Local Path Planning for Mobile Robot 2

VFH∗ Based Local Path Planning for Mobile Robot

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2nd China Symposium on Cognitive Computing and Hybrid Intelligence, CCHI 2019

作者： Chen, Wenqiang Wang, Ning Liu, Xiaofeng Yang, Chenguang Key Laboratory of Autonomous System and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou510640 China Bristol Robotics Laboratory University of the West of England BristolBS16 1QY United Kingdom Changzhou Key Laboratory of Robotics and Intelligent Technology College of Internet of Things Engineering Hohai University Changzhou213022 China

ISBN: (纸本)9781728140919

Vector Field Histogram(VFH) and its variants are effective obstacles avoidance method for mobile robots. Under the more consideration of realistic, we proposed a new local path planning method for mobile robot in this paper. The proposed method takes global path point generated by the A∗ algorithm as temporary target point and adopts the basic concepts of the VFH∗ algorithm, the difference is mainly lies in the representation of vector magnitude of the obstacles, the determination of the threshold, the selection of the candidate direction, and the definition of the node cost function in search tree. Besides, according to the optimal obstacle avoidance direction obtained in the above steps, we also give a speed control method to control the movement of the omnidirectional mobile robot to the target point. Simulation and experiment results shows that the proposed method is suited for mobile robot moving in realistic situation. © 2019 IEEE.

关键词： Mobile robots

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Automatic AFM Images Distortion Correction Based on Adaptive Feature Recognition Algorithm

Automatic AFM Images Distortion Correction Based on Adaptive...

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Chinese Automation Congress (CAC)

作者： Chengpeng Yang Shoujin Wang Chunxue Hao Yang Yang Jialin Shi Peng Yu School of Information and Control Engineering Shenyang Jianzhu University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China School of Information and Control Engineering Shenyang Jianzhu University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Atomic Force Microscope (AFM) images will appear tilt and bending of the image background due to the tilt angle between the sample surface and the probe, thermal noise of system, or external vibration of environment. Feature recognition is very crucial for removing unstable imaging background when using the least square fitting method to horizontally correct the AFM full image, which the topography structures higher or lower than the sample substrate will affect the fitting correction results. To realize universal automatic correction of AFM images, this paper proposes an adaptive feature recognition algorithm based on improved image edge detection method to automatically identify, frame and mark the features and then remove the detorsion background using Least Squares Fitting Correction. The experiment results show that this method can realize adaptive feature recognition and automatic fitting correction of the whole image, and improve the correction accuracy.

关键词： Graphics Image edge detection Feature extraction Fitting Distortion Gray-scale Robots

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Real-Time Marker Localization Learning for GelStereo Tactile Sensing

Real-Time Marker Localization Learning for GelStereo Tactile...

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IEEE International Conference on robotics and Biomimetics

作者： Shujuan Liu Shaowei Cui Chaofan Zhang Yinghao Cai Shuo Wang University of Science and Technology Beijing State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science Intelligence Technology Chinese Academy of Sciences Shanghai China

ISBN: (纸本)9781665481106

Visuotactile sensing technology is becoming more popular in tactile sensing, but the effectiveness of the existing marker detection/localization methods remains to be further explored. Instead of contour-based blob detection, this paper presents a learning-based marker localization network for GelStereo visuotactile sensing called Marknet. Specifically, the Marknet presents a grid regression architecture to incorporate the distribution of the GelStereo markers. Furthermore, a marker rationality evaluator (MRE) is modelled to screen suitable prediction results. The experimental results show that the Marknet combined with MRE achieves 93.90% precision for irregular markers in contact areas, which outperforms the traditional contour-based blob detection method by a large margin of 42.32%. Meanwhile, the proposed learning-based marker localization method can achieve better realtime performance beyond the blob detection interface provided by the OpenCV library through GPU acceleration, which we believe will lead to considerable perceptual sensitivity gains in various robotic manipulation tasks.

关键词： Location awareness Sensitivity Object detection Network architecture Predictive models Robot sensing systems Real-time systems

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Weakly Aligned Feature Fusion for Multi-modal Object Detection

arXiv

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arXiv 2022年

作者： Zhang, Lu Liu, Zhiyong Zhu, Xiangyu Song, Zhan Yang, Xu Lei, Zhen Qiao, Hong The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100086 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China The Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China The Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong The Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China

To achieve accurate and robust object detection in the real-world scenario, various forms of images are incorporated, such as color, thermal, depth, etc. However, multi-modal data often suffer from the position shift problem, i.e. the image pair is not strictly aligned, making one object has different positions in different modalities. For the deep learning method, this problem makes it difficult to fuse multi-modal features and puzzles the CNN training. In this paper, we propose a general multi-modal detector named Aligned Region CNN (AR-CNN) to tackle the position shift problem. Firstly, a region feature alignment module with adjacent similarity constraint is designed to consistently predict the position shift between two modalities and adaptively align the cross-modal region features. Secondly, we propose a novel RoI jitter strategy to improve the robustness to unexpected shift patterns. Thirdly, we present a new multi-modal feature fusion method, which selects the more reliable feature and suppresses the less useful one via feature re-weighting. Additionally, by locating bounding boxes in both modalities and building their relationships, we provide novel multi-modal labelling named KAIST-Paired. Extensive experiments on 2D and 3D object detection, RGB-T and RGB-D datasets demonstrate the effectiveness and robustness of our method. Copyright © 2022, The Authors. All rights reserved.

关键词： Object recognition

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A Novel Two-Stage Framework for 2D/3D Registration in Neurological Interventions

A Novel Two-Stage Framework for 2D/3D Registration in Neurol...

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IEEE International Conference on robotics and Biomimetics

作者： De-Xing Huang Xiao-Hu Zhou Xiao-Liang Xie Shi-Qi Liu Zhen-Qiu Feng Jian-Long Hao Zeng-Guang Hou Ning Ma Long Yan The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The School of Information Shanxi University of Finance and Economics Taiyuan China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China The Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa Macau The Department of Interventional Neuroradiology Beijing Tiantan Hospital Capital Medical University Beijing China

ISBN: (纸本)9781665481106

2D/3D medical image registration of pre-operative volumes and intra-operative images plays an important role in neurological interventions. However, vast space of transformation parameters makes this task incredibly challenging. In this paper, a novel two-stage framework for 2D/3D registration is proposed. It consists of two basic modules: CNN regression for preliminarily estimating transformation parameters, and centroid alignment for further reducing estimation errors to get refined images. Experimental results on two patients show the proposed framework can achieve superior performances to baseline methods with an inference rate of 22 FPS, satisfying real-time requirements (6~12 FPS) in clinical practice. Extensive ablation studies prove the effectiveness of centroid alignment in significantly improving registration accuracies. These promising results indicate the framework has the potential to be integrated into navigation frameworks for neurological interventions, facilitating treatments of physicians.

关键词： Image registration Estimation error Three-dimensional displays Navigation Biomimetics Medical services Real-time systems

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Design of Overall Framework of Self-Service Big Data Governance for Power Grid 4th

Design of Overall Framework of Self-Service Big Data Governa...

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4th International Conference on Machine Learning and Intelligent Communications, MLICOM 2019

作者： Qiao, Lin Zhou, Qiaoni Song, Chunhe Wu, Hao Liu, Biqi Yu, Shimao State Grid Liaoning Electric Power Co. Ltd. Shenyang110000 China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9783030323875

At present, power grid companies have not formed a complete data quality control system and a comprehensive and effective data quality assurance mechanism, which restricts the deep mining of data value. In this paper, based on the full-service unified data center of power Grid Company, a general framework of self-service power grid big data governance is presented. Firstly, the related work of big data governance is reviewed;secondly, the architecture and characteristics of the grid company’s full-service unified data center are analyzed;thirdly, the related requirements of self-service grid big data governance are analyzed;fourthly, the overall framework of self-service grid big data governance is proposed. Compared with other general big data governance framework, the proposed framework considers the specification of power grid as well as the features of self-service, making the framework more feasible. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Big data

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A Data Quality Improvement Method Based on the Greedy Algorithm 4th

A Data Quality Improvement Method Based on the Greedy Algori...

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4th International Conference on Machine Learning and Intelligent Communications, MLICOM 2019

作者： Wang, Zhongfeng Fu, Yatong Song, Chunhe Ge, Weichun Qiao, Lin Zhang, Hongyu Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China State Grid Liaoning Electric Power Co. Ltd. 110000 Shenyang China

ISBN: (纸本)9783030323875

High-quality data is very important for data analysis and mining. Data quality can be indicated by many indicators, and some methods have been proposed for data quality improvement by improving one or more data quality indicators. However, there is few work to discuss the impact of the processing order of data quality indicators on the overall data quality. In this paper, first, some data quality indicators and their improvement methods are given;second, the impact of the processing order of data quality indicators on the overall data quality is discussed, and then a novel data quality improvement method based on the greedy algorithm is proposed. Experiments have been shown that the proposed method can improves the data quality while reducing the time and computational costs. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Data reduction

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Efficient two step optimization for large embedded deformation graph based SLAM

Efficient two step optimization for large embedded deformati...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jingwei Song Fang Bai Liang Zhao Shoudong Huang Rong Xiong Centre for Autonomous Systems University of Technology Sydney Broadway Australia State Key Laboratory of Industrial Control Technology and Institute of Cyber-Systems and Control Zhejiang University Zhejiang China

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Embedded deformation graph is a widely used technique in deformable geometry and graphical problems. Although the technique has been transmitted to stereo (or RGB-D) camera based SLAM applications, it remains challenging to compromise the computational cost as the model grows. In practice, the processing time grows rapidly in accordance with the expansion of maps. In this paper, we propose an approach to decouple the nodes of deformation graph in large scale dense deformable SLAM and keep the estimation time to be constant. We observe that only partial deformable nodes in the graph are connected to visible points. Based on this fact, the sparsity of the original Hessian matrix is utilized to split the parameter estimation into two independent steps. With this new technique, we achieve faster parameter estimation with amortized computation complexity reduced from O(n 2 ) to almost O(1). As a result, the computational cost barely increases as the map keeps growing. Based on our strategy, the computational bottleneck in large scale embedded deformation graph based applications will be greatly mitigated. The effectiveness is validated by experiments, featuring large scale deformation scenarios.

关键词： Simultaneous localization and mapping Strain Jacobian matrices Optimization Cameras Deformable models Geometry

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Development and Evaluation of a Robotic System for Safe Cardiac Sheath Delivery

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IEEE Transactions on Biomedical Engineering 2025年 PP卷 PP页

作者： Wang, Weizhao Saija, Carlo Xu, Zhouyang Zeidan, Aya Wilcox, Joshua Patterson, Tiffany Redwood, Simon Wang, Shuangyi Rhode, Kawal Housden, Richard King's College London School of Biomedical Engineering and Imaging Sciences LondonWC2R 2LS United Kingdom Guy's and St Thomas' NHS Foundation Trust Cardiovascular Department LondonSE1 7EH United Kingdom King's College London School of Cardiovascular Medicine and Sciences LondonSE1 7EH United Kingdom Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing101408 China Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Hong Kong

Objective: This study aimed to develop and evaluate a 3-degree-of-freedom (DoF) robotic system for the safe delivery of cardiac sheaths through challenging anatomical structures, including the fossa ovalis and pathways with tight curves. Methods: The robot and its kinematic model were built on a previously proposed single-DoF actuation module and bending model. A sheath delivery strategy (SDS) was developed, combining two control methods: tip position control to approach an optimal entry point and point-constrained control to maintain consistent navigation through this point, minimizing tissue contact. Technical performance was evaluated through trajectory-following and point-crossing tests, followed by feasibility experiments in a simulated scenario. Trials were conducted by three cardiologists using a validated phantom model under fluoroscopic guidance, comparing SDS with joint control (JC) and manual control (MC). Results: Average root mean square errors were 2.10 mm for tip position control and 1.86 mm for point-constrained control. SDS outperformed MC with significantly shorter trajectory lengths and lower root mean square jerk. Compared to JC, SDS reduced sheath-induced movements (an indirect measure of force) and increased retraction success rates at the fossa ovalis. Conclusion: The proposed robotic system reduced tissue wall contact compared to JC and provided smoother, more controlled operations than MC, ensuring safer and more effective delivery through confined pathways. Significance: This work contributes to advancing robotic-assisted cardiac sheath delivery, providing a reliable and safer method for navigating challenging anatomical structures. © 2025 IEEE Computer Society. All rights reserved.

关键词： Medical robotics

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