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WRC Symposium on advanced robotics and automation (WRC SARA)

作者： Yinjia Zhang Qian Yang Dengbo Zhang Xudong Ma Jie Tang Yuanzheng Hou Yida David Hu Junchen Wang Changsheng Li Shaolong Kuang Wenyong Liu Bai-Quan Su Medical Robotics Laboratory School of Intellgent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China Department of Neurosurgery Xuanwu Hospital Capital Medical University Beijing China Brigham and Women’s Hospital Harvard Medical School Boston MA USA School of Mechanical Engineering and Automation Beihang University Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China School of Health Science and Environmental Engineering Shenzhen University of Technology Shenzhen China Key Laboratory for Biomechaniscs and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

The replacement of surgical tools is one of the basic operations in performing surgery. However, this step is time-consuming in operational processes, and elevates the associated risks. Reason: The revision enhances clarity and employs more formal language suitable for an academic context. At present, medical robots still need manual operation to replace surgical tools. In order to enable the robot to change surgical tools, this study designed a clamping tool holder for changing surgical tools. The clamping tool holder is driven by a 6-degree of freedom robotic arm. In this paper, CAD model of the clamping tool holder is presented, the motion of the clamping tool holder analysis and pick up the tools of simulation operation. Finally, the prototype experiment verified that the clamping tool holder can hold and release surgical tools without manual assistance. The structure can realize the effect of saving manpower and improving operation efficiency.

关键词： Solid modeling Analytical models Medical robotics Prototypes Surgery Formal languages Manuals

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Single Segment Controllable Continuum Robot: A Pilot Study*

Single Segment Controllable Continuum Robot: A Pilot Study*

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WRC Symposium on advanced robotics and automation (WRC SARA)

作者： Xu-Dong Ma Qian Yang Fengtong Wu Yinjia Zhang Yida David Hu Wenyong Liu Junchen Wang Changsheng Li Jie Tang Shaolong Kuang Bai-Quan Su Medical Robotics Laboratory School of Intellgent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China Brigham and Women’s Hospital Harvard Medical School Boston MA USA Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China School of Mechanical Engineering and Automation Beihang University Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China Department of Neurosurgery Xuanwu Hospital Capital Medical University Beijing China School of Health Science and Environmental Engineering Shenzhen University of Technology Shenzhen China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

In existing continuum robots, not every segment within a section is controlled, limiting the full utilization of their flexibility and making navigation through narrow and continuously curved spaces challenging. This paper introduces a single-section controllable continuum robot where each segment can be independently controlled via driving wires. Firstly, we designe the structure and connection method of the continuum robot segments. Secondly, we establish the kinematic model of the single-section controllable continuum robot, enabling independent control of each segment through the driving wires. Finally, we compares and tests the fitting accuracy of different configurations of continuum robots with controllable parts containing 1, 3, 5, and 7 segments in one section, regarding their ability to fit the same curve. The single-section controllable continuum robot can independently control the position of each joint within the continuum, demonstrating superior curve fitting capability and enabling navigation through narrow, continuously curved spaces.

关键词： Target tracking Limiting Navigation Wires Fitting Kinematics Aerospace electronics

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Hair Direction Detection*

Hair Direction Detection*

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WRC Symposium on advanced robotics and automation (WRC SARA)

作者： Peng Ba Pengyi Wang Hongde Wu Qian Yang Yongqiang Feng Junchen Wang Yida David Hu Changsheng Li Wenyong Liu Shaolong Kuang Bai-Quan Su Medical Robotics Laboratory School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China Plastic Surgery Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China School of Mechanical Engineering and Automation Beihang University Beijing China Brigham and Women’s Hospital Harvard Medical School Boston MA USA School of Mechatronical Engineering Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China School of Health Science and Environmental Engineering Shenzhen University of Technology Shenzhen China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

Hair direction is an important external feature of hair, and recognising hair direction is a prerequisite for processing hair. In this paper, a new algorithm is proposed and systematically verified experimentally for the problem of recognising hair direction. The main goal of this paper is to develop an algorithm that can identify hair direction in a complex image environment. A curve segment analysis method based on image skeletonisation is adopted, which is based on skeleton extraction, intersection identification, curve segmentation and direction prediction. In addition, this paper combines the technique of non-maximal value suppression and PCA analysis to improve the accuracy and stability of the estimation. In the experimental design, this paper chooses a representative image dataset to verify the effectiveness of this paper's algorithm. The experimental process includes the steps of image preprocessing, skeletonisation processing, intersection detection and merging, and direction prediction. The experimental results show that the method in this paper can accurately and effectively identify the hair direction. The main contribution of this paper is to propose a new hair direction recognition method and experimentally verify its effectiveness and accuracy in complex backgrounds.

关键词： Hair Image segmentation Accuracy Three-dimensional displays Merging Prediction algorithms Stability analysis

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SC-NeRF: Self-Correcting Neural Radiance Field with Sparse Views

arXiv

引用

arXiv 2023年

作者： Song, Liang Wang, Guangming Liu, Jiuming Fu, Zhenyang Miao, Yanzi Wang, Hesheng Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

In recent studies, the generalization of neural radiance fields for novel view synthesis task has been widely explored. However, existing methods are limited to objects and indoor scenes. In this work, we extend the generalization task to outdoor scenes, trained only on object-level datasets. This approach presents two challenges. Firstly, the significant distributional shift between training and testing scenes leads to black artifacts in rendering results. Secondly, viewpoint changes in outdoor scenes cause ghosting or missing regions in rendered images. To address these challenges, we propose a geometric correction module and an appearance correction module based on multi-head attention mechanisms. We normalize rendered depth and combine it with light direction as query in the attention mechanism. Our network effectively corrects varying scene structures and geometric features in outdoor scenes, generalizing well from object-level to unseen outdoor scenes. Additionally, we use appearance correction module to correct appearance features, preventing rendering artifacts like blank borders and ghosting due to viewpoint changes. By combining these modules, our approach successfully tackles the challenges of outdoor scene generalization, producing high-quality rendering results. When evaluated on four datasets (Blender, DTU, LLFF, Spaces), our network outperforms previous methods. Notably, compared to MVSNeRF, our network improves average PSNR from 19.369 to 25.989, SSIM from 0.838 to 0.889, and reduces LPIPS from 0.265 to 0.224 on Spaces outdoor scenes. © 2023, CC BY.

关键词： Rendering (computer graphics)

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MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentation

arXiv

引用

arXiv 2024年

作者： Tang, Fenghe Nian, Bingkun Li, Yingtai Jiang, Zihang Yang, Jie Liu, Wei Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China Suzhou Institute for Advanced Research USTC Suzhou China Department of Automation Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Institute of Medical Robotics Shanghai Jiao Tong University China State Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Anhui Hefei230026 China

Recently, the state space model Mamba has demonstrated efficient long-sequence modeling capabilities, particularly for addressing long-sequence visual tasks in 3D medical imaging. However, existing generative self-supervised learning methods have not yet fully unleashed Mamba’s potential for handling long-range dependencies because they overlook the inherent causal properties of state space sequences in masked modeling. To address this challenge, we propose a general-purpose pre-training framework called MambaMIM, a masked image modeling method based on a novel TOKen-Interpolation strategy (TOKI) for the selective structure state space sequence, which learns causal relationships of state space within the masked sequence. Further, MambaMIM introduces a bottom-up 3D hybrid masking strategy to maintain a masking consistency across different architectures and can be used on any single or hybrid Mamba architecture to enhance its multi-scale and long-range representation capability. We pre-train MambaMIM on a large-scale dataset of 6.8K CT scans and evaluate its performance across eight public medical segmentation benchmarks. Extensive downstream experiments reveal the feasibility and advancement of using Mamba for medical image pre-training. In particular, when we apply the MambaMIM to a customized architecture that hybridizes MedNeXt and Vision Mamba, we consistently obtain the state-of-the-art segmentation performance. The code is available at: https://***/FengheTan9/MambaMIM. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Pseudo-LiDAR for Visual Odometry

arXiv

引用

arXiv 2022年

作者： Deng, Huiying Wang, Guangming Feng, Zhiheng Jiang, Chaokang Wu, Xinrui Miao, Yanzi Wang, Hesheng Advanced Robotics Research Center AI Research Institute School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

As one of the important tasks in the field of robotics and machine vision, LiDAR/visual odometry provides tremendous help for various applications such as navigation, location, etc. In the existing methods, LiDAR odometry shows superior performance, but visual odometry is still widely used for its price advantage. Conventionally, the task of visual odometry mainly rely on the input of continuous images. However, it is very complicated for the odometry network to learn the epipolar geometry information provided by the images. In this paper, the concept of pseudo-LiDAR is introduced into the odometry to solve this problem. The pseudo-LiDAR point cloud back-projects the depth map generated by the image into the 3D point cloud, which changes the way of image representation. Compared with the stereo images, the pseudo-LiDAR point cloud generated by the stereo matching network can get the explicit 3D coordinates. Since the 6 Degrees of Freedom (DoF) pose transformation occurs in 3D space, the 3D structure information provided by the pseudo-LiDAR point cloud is more direct than the image. Compared with sparse LiDAR, the pseudo-LiDAR has a denser point cloud. In order to make full use of the rich point cloud information provided by the pseudo-LiDAR, a projection-aware dense odometry pipeline is adopted. Most previous LiDAR-based algorithms sampled 8192 points from the point cloud as input to the odometry network. The projection-aware dense odometry pipeline takes all the pseudo-LiDAR point clouds generated from the images except for the error points as the input to the network. While making full use of the 3D geometric information in the images, the semantic information in the images is also used in the odometry task. The fusion of 2D-3D is achieved in an image-only based odometry. Experiments on the KITTI dataset prove the effectiveness of our method. To the best of our knowledge, this is the first visual odometry method using pseudo-LiDAR. Copyright © 2022, The Authors. A

关键词： Pipelines

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Optimization Design and Simulation of Light Broadband Multilayer Composite Absorber

Research Square

引用

Research Square 2021年

作者： Yang, Ping-An Huang, Yuxuan Li, Rui Sun, Yang Huang, Xin Shou, Mengjie Wang, Xiaojie School of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China Bio-inspired Robotics and Intelligent Material Laboratory Institute of Advanced Manufacturing Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Changzhou213164 China

Multilayer plate structure is an effective way to design the absorbing coating with excellent performance. Based on the analysis of the electromagnetic (EM) absorbing principle of multilayer structure, this paper selects materials with magnetic loss type and resistance loss type to establish the database of EM parameters. Genetic algorithm is used to optimize the design of the multilayer structure. Finally, COMSOL is used for simulation verification. The results show that the multilayer materials prepared by different types of EM loss materials have the characteristics of large absorption bandwidth and strong absorption capacity. When optimizing for different frequency bands and the thickness is limited to 5 mm, the absorption band can be adjusted freely between 5 GHz and 15 GHz by selecting the materials and thickness of each layer, which solves the limitation that a single material only meet the requirements in a specific frequency band. When optimizing for absorption intensity, the minimum reflection loss (RLmin) can reach -30.7 dB and the effective absorption bandwidth (EAB) is 3.3 GHz (13.1 GHz -16.4 GHz) achieved with only 2.44 mm. This design method provides the possibility to design multilayer composite materials with high absorption and low reflection in a wider frequency band range. © 2021, CC BY.

关键词： Genetic algorithms

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Online Change-Point Detection of Force Signal at the Tip of Surgical Instrument with Morphological Wavelet

Online Change-Point Detection of Force Signal at the Tip of ...

引用

International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Yinan Deng Lijun Hao Tangwen Yang Shaotao Dai Guoli Song Institute of Information Science and the Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing Jiaotong University Beijing China School of Electrical Engineering Beijing Jiaotong University Beijing China State Key Laboratory of Robotics Shenyang Institute of Automation CAS Shenyang China

ISBN: (纸本)9781665431545

Change-point detection of force signal at the tip of surgical tool can provide an early warning signal for the adjustment of robotic controller, potentially avoiding unwanted collisions between end-effector and tissue of human body. In this paper, an online model-free change-point detection method with a small delay and low computational complexity is presented. An averaged-difference morphological operator is first constructed as the detail analysis operator of morphological wavelet (MW) to map time series to MW domain. Then, a significance difference test based on Chebyshev’s principle is presented to compare the means of MW detail coefficients in the past and current windows, to determine whether a change-point has occurred. Experiment results on simulated force signals demonstrate the reliability and timeliness of the proposed method.

关键词： Wavelet domain Mechatronics Machine vision Instruments Force Time series analysis Chebyshev approximation

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ARST: Auto-Regressive Surgical Transformer for Phase Recognition from Laparoscopic Videos

arXiv

引用

arXiv 2022年

作者： Zou, Xiaoyang Liu, Wenyong Wang, Junchen Tao, Rong Zheng, Guoyan Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Biomechanics and Mechanobiology of Ministry of Eduction Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing100083 China School of Mechanical Engineering and Automation Beihang University Beijing100091 China

Phase recognition plays an essential role for surgical workflow analysis in computer assisted intervention. Transformer, originally proposed for sequential data modeling in natural language processing, has been successfully applied to surgical phase recognition. Existing works based on transformer mainly focus on modeling attention dependency, without introducing auto-regression. In this work, an Auto-Regressive Surgical Transformer, referred as ARST, is first proposed for on-line surgical phase recognition from laparoscopic videos, modeling the inter-phase correlation implicitly by conditional probability distribution. To reduce inference bias and to enhance phase consistency, we further develop a consistency constraint inference strategy based on auto-regression. We conduct comprehensive validations on a well-known public dataset Cholec80. Experimental results show that our method outperforms the state-of-the-art methods both quantitatively and qualitatively, and achieves an inference rate of 66 frames per second (fps). Copyright © 2022, The Authors. All rights reserved.

关键词： Probability distributions

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A Deep Learning-Based Autonomous Robot Manipulator for Sorting Application

A Deep Learning-Based Autonomous Robot Manipulator for Sorti...

引用

IEEE International Conference on Robotic Computing (IRC)

作者： Hoang-Dung Bui Hai Nguyen Hung Manh La Shuai Li Advanced Robotics and Automation (ARA) Laboratory University of Nevada Reno NV USA College of Engineering Swansea University Wales UK

ISBN: (数字)9781728152370

ISBN: (纸本)9781728152387

Robot manipulation and grasping mechanisms have received considerable attention in the recent past, leading to development of wide-range of industrial applications. This paper proposes the development of an autonomous robotic grasping system for object sorting application. RGB-D data is used by the robot for performing object detection, pose estimation, trajectory generation and object sorting tasks. The proposed approach can also handle grasping on certain objects chosen by users. Trained convolutional neural networks are used to perform object detection and determine the corresponding point cloud cluster of the object to be grasped. From the selected point cloud data, a grasp generator algorithm outputs potential grasps. A grasp filter then scores these potential grasps, and the highest-scored grasp will be chosen to execute on a real robot. A motion planner will generate collision-free trajectories to execute the chosen grasp. The experiments on AUBO robotic manipulator show the potentials of the proposed approach in the context of autonomous object sorting with robust and fast sorting performance.

关键词： Three-dimensional displays Service robots Grasping Trajectory Collision avoidance Robots Sorting

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