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Parallel Intelligence in Metaverses: Welcome to Hanoi!

IEEE INTELLIGENT systems 2022年第1期37卷 16-20页

作者： Wang, Fei-Yue Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macao Inst Syst Engn Macau 999078 Peoples R China

This article outlines a journey toward the TRUE DAO System of Intelligent systems based parallel intelligence with the help of digital twins, metaverses, web 3.0, and blockchain technology. It argues for a Hanoi approach, i.e., integrated Human, Artificial, Natural, and Organizational Intelligence for achieving knowledge automation for sustainable and smart societies.

关键词： Automation Digital Twin Blockchains Intelligent systems Artificial Intelligence Expert systems

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A Bioinspired Coiled Cable-Driven Manipulator: Mechatronic Design and Kinematics Planning With Multiconstraints

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2023年第6期28卷 3155-3166页

作者： Luo, Mingrui Li, En Zhang, Aoshun Tan, Min Liang, Zize Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Engn Lab Ind Vis & Intelligent Equipment Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

The cable-driven hyper-redundant manipulators (CDHRMs) are especially suitable for operations in the long narrow confined space due to their light-slender and superior dexterous bodies. However, the high redundancy also brings challenges to mechanical structure and kinematic planning. In this article, a highly compact coiled CDHRM (C-CDHRM) that mimics the coiling behavior of snakes is designed, including 12 fully-constrained rigid bodies, 36 double-screw retractors, and a coiled feeding mechanism that can coil or uncoil according to task needs. Corresponding to the characteristics of the C-CDHRM, a variable redundancy kinematic model (VRKM) is established, including the equivalent mapping from the planning point to the cable length and the analytical approximate workspace that can determine the minimum number of active segments according to the target. Based on the VRKM, multiconstraint kinematic planning (MCKP) is presented, which integrates the smoothness of cable-driven joints, the mobility of the feeding base, and the collision-free performance of complex environments. In simulation tests, the MCKP has a higher success rate, smoother angular distribution, and less motion than typical methods. Furthermore, the MCKP can continuously get joint configurations of reaching the target in dense obstacles. Finally, a prototype is developed and typical experiments are performed. The results show that the VRKM and MCKP enable the bioinspired coiled manipulator to exert excellent dexterity and maneuverability.

关键词： Kinematics Planning Power cables Manipulators Mechatronics Gears Force Cable-driven manipulators hyperredundant manipulators kinematics planning workspace analysis

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PLJ-SLAM: Monocular Visual SLAM With Points, Lines, and Junctions of Coplanar Lines

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IEEE SENSORS JOURNAL 2022年第15期22卷 15465-15476页

作者： Ren, Guangli Cao, Zhiqiang Liu, Xilong Tan, Min Yu, Junzhi Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peking Univ Coll Engn Dept Mech & Engn Sci BIC ESATState Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China

Existing monocular visual simultaneous localization and mapping (SLAM) mainly focuses on point and line features, and the constraints among line features are not fully explored. In this paper, a multi-feature monocular SLAM with ORB points, lines, and junctions of coplanar lines is proposed for indoor environments. To create 3D junctions of coplanar lines, an adaptive coordinate confidence is designed to describe the coordinate stability of 2D junctions on the image plane. Based on the matched 2D junctions between the current keyframe and other two associated keyframes, a multi-view coplanarity verification is performed for 3D junction creation. Moreover, reprojection verification is conducted to update the observation of 3D junction, which is used to dynamically update the confidence of coplanarity for confidence-based local bundle adjustment. The camera pose is optimized through multiple constraints of ORB points, lines, and junctions of the coplanar lines. As a result, the performance of monocular SLAM is improved. The experiment verification shows the effectiveness of the proposed method.

关键词： Simultaneous localization and mapping Junctions Visualization Cameras Sensors Optimization Image segmentation Junction of coplanar lines confidence of coplanarity multi-view multi-feature monocular SLAM

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Design and Optimization of an Untethered High-Performance Robotic Tuna

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第5期27卷 4132-4142页

作者： Tong, Ru Wu, Zhengxing Chen, Di Wang, Jian Du, Sheng Tan, Min Yu, Junzhi Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peking Univ Coll Engn Dept Adv Mfg & Robot BIC ESATState Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China

For bioinspired underwater robots, it is a great challenge to achieve both high swimming speed and steering maneuverability. To this end, this article presents an untethered high-performance robotic tuna through mechanism optimization and steering strategies design. First, combining the advantages of single-joint and multijoint robotic fish, a novel mechanism of redundant joints is designed to enrich the swimming patterns of the robotic tuna. Next, a three-dimensional dynamic model is established, and the model parameters are accurately identified by experimental data. With the aid of the model and experiments, the speed performance is optimized under the key parameters of tail fin, such as size and flexibility. Further, from the perspective of imitating tuna and the demand of actual operations, two steering strategies are proposed to improve the steering performance. Finally, extensive simulation and experiments verify the effectiveness of the proposed methods. The obtained results reveal that the untethered robotic tuna can achieve both high swimming speed with 2.26 m/s (equivalent to 3.13 body lengths per second) and outstanding steering maneuverability with 0.48 body lengths in turning radius, providing valuable insight into performing special missions in cluttered ocean environments.

关键词： High maneuverability high swimming motion control motion optimization robotic tuna

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Single Camera Based Gait Analysis Method with Scaled and Image Coordinate key-points

Single Camera Based Gait Analysis Method with Scaled and Ima...

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

作者： Xiang, Tian-Yu Zhou, Xiao-Hu Xie, Xiao-Liang Liu, Shi-Qi Wang, Chen Gui, Mei-Jiang Li, Hao Hou, Zeng-Guang University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation School of Artificial Intelligence Beijing100049 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Clinical experiments and scientific evidence show the relations between gait and cognition. Neurological diseases (like stroke and cerebral palsy) distract patients' attention and restrict their executive functions. Furthermore, these diseases impair patients' movement and limit their social participation. Quantitative motion assessment is significant to medical decision-making for neurological disorders but is possible only with expensive motion capture systems and professional medical workers. Here, an end-To-end video-based quantitative gait analysis algorithm is proposed. Unlike previous works, which only focus on image coordinate key-points, our algorithm also utilizes scaled key-points that can depict the size of patients. These two groups of features are sent to other neural networks to extract temporal and spatial characteristics, and then the deep features are fused by the attention mechanism. Finally, a statistic model is used to process the proposed outputs to get a final result. Experiments on the public patients' dataset show the effectiveness and accuracy of our model. Furthermore, the ablation study proves that every step of the proposed method contributes to the whole system's performance. © 2022 IEEE.

关键词： Gait analysis

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Vision-Based Power Line Segmentation With an Attention Fusion Network

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IEEE SENSORS JOURNAL 2022年第8期22卷 8196-8205页

作者： Yang, Lei Fan, Junfeng Xu, Shuai Li, En Liu, Yanhong Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Henan Peoples R China Robot Percept & Control Engn Lab Zhengzhou 450001 Henan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Automatic power transmission line detection plays the key role in smart grid which has been widely applied into the path planning and navigation of intelligent inspection platforms, such as Unmanned Aerial Vehicles (UAVs), climbing robots, hybrid inspection robots, etc. Nevertheless, the power lines are always against complex background environment and different illumination conditions. And the power lines occupy a minimal portion image pixels in the aerial images compared with backgrounds which causes the foreground-background class imbalance issue. Therefore, robust and accurate vision-based power line detection still faces a certain challenge. Recently, deep learning has got fast development on pixel-level object segmentation due to strong contextual feature expression ability, especially U-shape network (U-Net) and its variants. However, it still exists a certain shortcomings owing to insufficient process of local contextual features to affect the segmentation precision. Meanwhile, multiple pooling operations in deep convolutional neural networks (DCNNs) also will cause the information loss. To address these issues, with the encoder-decoder architecture, a novel vision-based power line detection network is proposed in this paper to construct an end-to-end detection scheme of power lines from aerial images. To make the segmentation network capture the global contexts and emphasize target regions of power transmission lines, an attention block is proposed to be embedded into the proposed power line detection network to address the class imbalance issue. Meanwhile, faced with the insufficient process of local contextual feature maps of DCNNs, an attention fusion block is proposed for multi-scale feature fusion to acquire more rich information and improve the segmentation precision. Experiments on power lines show that the proposed power line detection network shows a good segmentation performance on real power line environment compared with other advanced detection

关键词： Power transmission lines Inspection Feature extraction Sensors Image segmentation Navigation Vision sensors Power line extraction deep network architecture image segmentation multi-scale feature fusion attention module

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A Precise Calibration Method for the Underwater Binocular Camera With Unknown Refractive Index

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IEEE SENSORS JOURNAL 2023年第2期23卷 1565-1574页

作者： Yang, Huizhen Tian, Shifei Fan, Junfeng Han, Yanqing Zhou, Chao Ou, Yaming Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Army Acad Armored Force Dept Basic Course Beijing 100072 Peoples R China

Underwater camera calibration usually needs to solve an accurate refraction model to improve the calibration accuracy. In the refraction model, there are some key parameters: 1) the distance from the optical center to the refraction plane;2) the thickness of the housing;3) the normal vector of the refraction plane;and 4) the refractive index of the medium. In many cases, the refractive index is often assumed to be a known parameter. However, the refractive index of water will change due to environmental changes such as temperature, salinity, or depth. Inaccurate refractive index will seriously affect the measurement accuracy. Therefore, a precise calibration method for the underwater binocular camera with unknown refractive index is proposed. All the unknown parameters of the refraction model, including the refractive index of the medium, are solved by the nondominated sorting genetic algorithm (NSGA)-III. The parameter solving process is regarded as a multiobjective optimization problem, and the optimization objectives are determined by using the relative position and the actual position relationship of the checkerboard corners. Experiments show that the proposed method can greatly improve the 3-D measurement accuracy of binocular camera in unknown waters.

关键词： Refractive camera model underwater calibration underwater measurement underwater stereo vision

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Image Denoising of Seam Images With Deep Learning for Laser Vision Seam Tracking

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IEEE SENSORS JOURNAL 2022年第6期22卷 6098-6107页

作者： Yang, Lei Fan, Junfeng Huo, Benyan Li, En Liu, Yanhong Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Henan Peoples R China Robot Percept & Control Engn Lab Zhengzhou 450001 Henan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Seam tracking with structured light vision has been widely applied into the robot welding. And the precise laser stripe extraction is the premise of automatic laser vision seam tracking. However, conventional laser stripe extraction methods based on image processing have the shortcomings of poor flexibility and robustness, which are easily affected by considerable image noises in the welding processing, such as arc light, smoke, and splash. To address this issue, inspired by image segmentation, with the strong contextual feature expression ability of deep convolutional neural network (DCNN), a novel image denoising method of seam images is proposed in this paper for automatic laser stripe extraction to serve intelligent robot welding applications, such as seam tracking, seam type detection, weld bead detection, etc. With the deep encoder-decoder network framework, aimed at the information loss issue by multiple convolutional and pooling operations in DCNNs, an attention dense convolutional block is proposed to extract and accumulate multi-scale feature maps. Meanwhile, a residual bi-directional ConvLSTM block (BiConvLSTM) is proposed to effectively learn multi-scale and long-range spatial contexts from local feature maps. Finally, a weighted loss function is proposed for model training to address the class unbalanced issue. Combined with the seam image set, the experimental results show that the proposed image denoising network could correctly extract the laser stripes from seam images which could demonstrate that the proposed method shows a high detection precision and good robustness against the strong image noise interference from welding process.

关键词： Welding Laser noise Sensors Robots Laser modes Feature extraction Lasers Deep network architecture image denoising seam tracking robot welding structured light vision

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A Novel Ultrasound Robot With Force/Torque Measurement and control for Safe and Efficient Scanning

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Bao, Xianqiang Wang, Shuangyi Zheng, Lingling Housden, Richard James Hajnal, Joseph V. Rhode, Kawal Kings Coll London Sch Biomed Engn & Imaging Sci London SE1 7EH England Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Kagawa Univ Fac Engn & Design Takamatsu 7610396 Japan

Medical ultrasound (US) is of increasing importance in medical diagnosis and intraoperative assistance and possesses great potential advantages when integrated with robotics. However, some concerns, including the operation efficiency, operation safety, image quality, and comfort of patients, remain after introducing robotics into medical ultrasound. In this article, an ultrasound robot integrating a force control mechanism, force/torque measurement mechanism, and online adjustment method is proposed to overcome the current limitations. The ultrasound robot can measure operating forces and torques, provide adjustable constant operating forces, eliminate great operating forces introduced by accidental operations, and achieve various scanning depths based on clinical requirements. The proposed ultrasound robot would potentially facilitate sonographers to find the targets quickly, improve operation safety and efficiency, and decrease patients' discomfort. Simulations and experiments were carried out to evaluate the performance of the ultrasound robot. Experimental results show that the proposed ultrasound robot is able to detect operating force in the z-direction and torques around the x- and y-directions with errors of 3.53%, 6.68%, and 6.11% F.S., maintain the constant operating force with errors of less than 0.57 N, and achieve various scanning depths for target searching and imaging. This proposed ultrasound robot has good performance and would potentially be used in medical ultrasound.

关键词： Ultrasonic imaging Robots Force Force measurement Robot sensing systems Ultrasonic variables measurement Probes operation safety robotic ultrasonography ultrasound image ultrasound robot

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Decoupled Metric Network for Single-Stage Few-Shot Object Detection

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第1期53卷 514-525页

作者： Lu, Yue Chen, Xingyu Wu, Zhengxing Yu, Junzhi Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Kuaishou Technol Ytech Beijing 100085 Peoples R China Peking Univ Coll Engn BIC ESAT Dept Adv Mfg & RobotState Key Lab Turbulence & C Beijing 100871 Peoples R China

Within the last few years, great efforts have been made to study few-shot learning. Although general object detection is advancing at a rapid pace, few-shot detection remains a very challenging problem. In this work, we propose a novel decoupled metric network (DMNet) for single-stage few-shot object detection. We design a decoupled representation transformation (DRT) and an image-level distance metric learning (IDML) to solve the few-shot detection problem. The DRT can eliminate the adverse effect of handcrafted prior knowledge by predicting objectness and anchor shape. Meanwhile, to alleviate the problem of representation disagreement between classification and location (i.e., translational invariance versus translational variance), the DRT adopts a decoupled manner to generate adaptive representations so that the model is easier to learn from only a few training data. As for a few-shot classification in the detection task, we design an IDML tailored to enhance the generalization ability. This module can perform metric learning for the whole visual feature, so it can be more efficient than traditional DML due to the merit of parallel inference for multiobjects. Based on the DRT and IDML, our DMNet efficiently realizes a novel paradigm for few-shot detection, called single-stage metric detection. Experiments are conducted on the PASCAL VOC dataset and the MS COCO dataset. As a result, our method achieves state-of-the-art performance in few-shot object detection. The codes are available at https://***/yrqs/DMNet.

关键词： Object detection Feature extraction Training Head Task analysis Shape Measurement Computer vision deep learning few-shot learning object detection

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