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arXiv 2023年

作者： Cao, Zhenzhong Liu, Jingtai The Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300353 China The Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin300350 China

Loop closure, as one of the crucial components in SLAM, plays an essential role in correcting the accumulated errors. Traditional appearance-based methods, such as bag-of-words models, are often limited by local 2D features and the volume of training data, making them less versatile and robust in real-world scenarios, leading to missed detections or false positives detections in loop closure. To address these issues, we first propose a semantic loop closure method based on quadric-level object map topology, which represents scenes through the topological graph of quadric-level objects and achieves accurate loop closure at a wide field of view by comparing differences in the topological graphs. Next, in order to solve the data association problem between frame and map in loop closure, we propose a object-level data association method based on multilevel verification, which can associate 2D semantic features of current frame with 3D objects landmarks of map. Finally, we integrate these two methods into a complete object-aware SLAM system. Qualitative experiments and ablation studies demonstrate the effectiveness and robustness of the proposed object-level data association algorithm. Quantitative experiments show that our semantic loop closure method outperforms existing state-of-the-art methods in terms of precision, recall and localization accuracy metrics. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Two-Photon Image-Guided Automated 3D Locating of Micropipette Tip

Two-Photon Image-Guided Automated 3D Locating of Micropipett...

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第43届中国控制会议

作者： Zizhen Li Jie Li Qili Zhao Xin Zhao Mingzhu Sun National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases Engineering Research Center of Trusted Behavior Intelligence Ministry of Education Tianjin Key Laboratory of Intelligent Robotics (tikLIR) Institute of Robotics and Automatic Information System (IRAIS) Nankai University Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The 21st century has been universally recognized by the scientific community as the era of neuroscience, with the patch clamp technique serving as a key to unraveling the intricate mysteries of brain science. Due to its advantages such as reduced sample damage and enhanced penetration capability, the application of two-photon microscope in deep imaging of live animals has become prevalent, making it a preferred choice over confocal microscope. At present, there exist a few near complete automation techniques of image-guided patching in vivo, which typically start from the micropipette tip appearing in the imaging field of view. The operational workflow for the step involving the installation of the micropipette until the micropipette tip becomes visible within the field of view has regrettably not been provided. However, it is crucial information that is of utmost importance to experimental personnel. In this paper, we present a comprehensive workflow for 3D locating of micropipette tip in a self-built two-photon microscope tailored for patch clamp experiments. Moreover, by fully incorporating the distinctive features of two-photon imaging, the study applies statistical methods to achieve automated execution of the corresponding workflow. The experimental results indicate that the process of micropipette tip locating takes an average of 58.4 ***, the method is not sensitive to parameter selection, allowing experimenters to obtain suitable parameters for different systems through simple testing. The automated 3D micropipette tip locating method proposed in this paper, with its versatility, holds great potential for seamless integration into commercial two-photon microscope systems. It significantly contributes to reducing the work burden on experimenters and minimizing the time required for experimental preparation.

关键词： 3D micropipette tip locating two-photon microscope automated method

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SlowFast GCN Network for Quantification of Parkinsonian Gait Using 2D Videos 12

SlowFast GCN Network for Quantification of Parkinsonian Gait...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Zeng, Qingyi Liu, Peipei Bai, Ying Yu, Huan Sun, Xiaoyi Han, Jianda Wu, Jialing Yu, Ningbo College of Artificial Intelligence Nankai University Haihe Education Park Tianjin300350 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Haihe Education Park Tianjin300350 China Department of Neurology Tianjin Huanhu Hospital Tianjin300350 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institute of Intelligence Technology and Robotics Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Parkinson's disease(PD) is a progressive neu-rodegenerative disease defined by clinical syndrome including bradykinesia, tremor and postural instability. The PD-related disability and impairment are usually monitored by clinicals using the MDS-UPDRS scale. However, due to COVID-19, it became much harder for the patients to reach hospitals and obtain necessary assessment and treatment. Nowadays, 2D videos are easily accessible and can be a promising so-lution for on-site and remote diagnosis of movement disorder. Inspired by the frequency-based video processing mechanism of human visual system, we propose a video-based SlowFast GCN network to quantify the gait disorder. The model consists of two parts: the fast pathway and the slow pathway. The former detects characteristics such as tremor and bilateral asymmetry, while the latter extracts characteristics such as bradykinesia and freezing of gait. Furthermore, in order to investigate the influence of age on the model performance, an aged control group and a young control group were set up for verification. The proposed model was evaluated on a video dataset collected from 68 participants. We achieved a balanced accuracy of 87.5% and precision of 87.9%, which outperformed existing competing methods. When replacing the young healthy controls with the same number of older controls, the balanced accuracy and precision were decreased by 10.4% and 9.7%, which indicates that age has a significant effect on the model perfomance. © 2022 IEEE.

关键词： COVID-19

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Dynamic Multi-Robot Path Planning in Narrow-Lane Environments with One-Way Constraint 6

Dynamic Multi-Robot Path Planning in Narrow-Lane Environment...

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6th International Conference on Intelligent Autonomous systems, ICoIAS 2023

作者： Song, Zhenyu Zheng, Ronghao Zhang, Senlin Liu, Meiqin College of Electrical Engineering Zhejiang University Hangzhou310027 China Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xian710049 China

ISBN: (纸本)9798350371253

With the rapid development of the logistics industry, the path planning problem for automated guided vehicles (AGVs) in warehousing systems has become a prominent research topic. Traditional multi-robot path planning algorithms often perform poorly in narrow-lane environments found in warehouses. This paper proposes an improved dynamic multi-robot path planning algorithm specifically tailored for narrow-lane environments. By constraining the traversal directions within narrow lanes and dynamically determining the optimal directions, this algorithm achieves efficient multi-robot path planning and enhances the quality of robot paths. The effectiveness of the algorithm is theoretically proven in this paper. Extensive simulation data demonstrates that our algorithm efficiently generates high-quality paths in high-density narrow-lane warehouse environments. © 2023 IEEE.

关键词： Multipurpose robots

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A Combined Inverse Kinematics Algorithm Using FABRIK with Optimization

arXiv

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arXiv 2022年

作者： Xu, Zichun Li, Yuntao Yang, Xiaohang Zhao, Zhiyuan Zhao, Jingdong Liu, Hong The State Key Laboratory of Robotics and System Harbin Institute of Technology Heilongjiang Province Harbin150001 China

Forward and backward reaching inverse kinematics (FABRIK) is a heuristic inverse kinematics solver that is gradually applied to manipulators with the advantages of fast convergence and generating more realistic configurations. However, under the high error constraint, FABRIK exhibits unstable convergence behavior, which is unsatisfactory for the real-time motion planning of manipulators. In this paper, a novel inverse kinematics algorithm that combines FABRIK and the sequential quadratic programming (SQP) algorithm is presented, in which the joint angles deduced by FABRIK will be taken as the initial seed of the SQP algorithm to avoid getting stuck in local minima. The combined algorithm is evaluated with experiments, in which our algorithm can achieve higher success rates and faster solution times than FABRIK under the high error constraint. Furthermore, the combined algorithm can generate continuous trajectories for the UR5 and KUKA LBR IIWA 14 R820 manipulators in path tracking with no pose error and permitted position error of the end-effector. Copyright © 2022, The Authors. All rights reserved.

关键词： Inverse kinematics

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Microsphere Superlenses-Based Optical Super-Resolution Imaging of Ovarian Tissue Sections 13

Microsphere Superlenses-Based Optical Super-Resolution Imagi...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2023

作者： Yuan, Shuai Jiang, Chaodi Luo, Hao Li, Shendi Wang, Xiaoduo School of Electrical & Control Engineering Shenyang Jianzhu University Liaoning Province Shenyang110168 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China School of Shenyang Ligong University Shenyang110159 China Institutes for Robotics and Intelligent Manufacturing Shenyang110016 China Shenyang Institute of Automation State Key Laboratory of Robotics Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9798350315196

Tissue sections can reveal tumour-specific changes, and a tumour diagnosis can be made by analyzing the arrangement and distribution of cells on the surface. Although histopathological diagnosis based on tissue section observation has been the gold standard for tumour diagnosis, there is still much room for improvement in terms of scale due to the limited spatial resolution of conventional optical microscopy. Microsphere super-resolution microscopy offers a potential solution to these problems. Super-resolution imaging of the surface of tissue sections was achieved with the aid of microspheres fixed to the surface of the tissue section and a removable piezoelectric ceramic displacement stage. A single microsphere of barium titanate, measuring 50µm in diameter, is capable of achieving super-resolution and is particularly suitable for observing the surface nanostructure of tissue sections. In our experiment, we successfully combined the microsphere super-resolution microscope with scanning stitching techniques to obtain tissue section structure images with a resolution lower than 200nm. Our research results will help improve the accuracy of tissue section observations and promote advancements in this field. © 2023 IEEE.

关键词： Microspheres

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Convolutional neural network based on sparse graph attention mechanism for MRI super-resolution

arXiv

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arXiv 2023年

作者： Hua, Xin Du, Zhijiang Yu, Hongjian Ma, Jixin State Key Laboratory of Robotics Technology and Systems Harbin Institute of Technology Heilongjiang Harbin15000 China

Magnetic resonance imaging (MRI) is a valuable clinical tool for displaying anatomical structures and aiding in accurate diagnosis. Medical image super-resolution (SR) reconstruction using deep learning techniques can enhance lesion analysis and assist doctors in improving diagnostic efficiency and accuracy. However, existing deep learning-based SR methods predominantly rely on convolutional neural networks (CNNs), which inherently limit the expressive capabilities of these models and therefore make it challenging to discover potential relationships between different image features. To overcome this limitation, we propose an A-network that utilizes multiple convolution operator feature extraction modules (MCO) for extracting image features using multiple convolution operators. These extracted features are passed through multiple sets of cross-feature extraction modules (MSC) to highlight key features through inter-channel feature interactions, enabling subsequent feature learning. An attention-based sparse graph neural network module is incorporated to establish relationships between pixel features, learning which adjacent pixels have the greatest impact on determining the features to be filled. To evaluate our model's effectiveness, we conducted experiments using different models on data generated from multiple datasets with different degradation multiples, and the experimental results show that our method is a significant improvement over the current state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Robust motion of quadruped robot on slippery surface based on virtual model control

Robust motion of quadruped robot on slippery surface based o...

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Information Science, Electrical and Automation Engineering (ISEAE), International Conference on

作者： Yaowei Wang Zhiyue Fang Yongxu Lu Ran Liu Junlin Li State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331510381

ISBN: (纸本)9798331510398

The motion of a quadruped robot on a slippery surface is very challenging. Most of the slip detection and recovery methods focus on the stability of the robot, and less attention is paid to its overall motion ability. In this paper, a controller based on virtual model control is designed to make the quadruped robot maintain stability on the wet and slip surface while increasing its speed as much as possible. An anti-toppling strategy based on position control is proposed to improve the robustness of robot motion. And the gait is planned based on the touchdown signal, a slip detection method based on foot position is proposed. On this basis, a speed control strategy is designed. Finally, the effectiveness of the proposed controller is verified by simulation.

关键词： Legged locomotion Robot motion Friction Simulation Velocity control Position control Stability analysis Robustness Quadrupedal robots Foot

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Resilient Event-Triggered Model Predictive Control for Adaptive Cruise Control Under Sensor Attacks

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 807-809页

作者： Zhijian Hu Rong Su Kai Zhang Zeyuan Xu Renjie Ma School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technologyand the Center of Ultra-Precision Optoelectronic Instrument EngineeringHarbin Institute of TechnologyHarbin 150001China Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China Department of Chemical and Biomolecular Engineering National University of SingaporeSingapore 117585Singapore State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150001China IEEE

Dear Editor,This letter addresses the resilient model predictive control(MPC)problems for adaptive cruise control(ACC)systems under sensor *** the light of vulnerabilities of ACC systems to sensor attacks,an intrusion detection mechanism is proposed at the controller side to distinguish abnormal ***,the robust control gains are derived to design the terminal region constraint for MPC.

关键词： Trigger Event constraint

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An Efficient Planner for Dual-Arm Massage Robots in Constrained state

An Efficient Planner for Dual-Arm Massage Robots in Constrai...

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IEEE International Conference on robotics and Biomimetics

作者： Yuan Xu Wenxiao Wang Lechuan Zhou Weichao Guo Xinjun Sheng State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Meta Robotics Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Dual-arm massage robots necessitate reaching the massage pose along an optimal collision-free path in constrained state, where traditional path planning methods often exhibit inefficiency. This paper proposes a sampling-based planning algorithm that integrates lazy evaluation, transition testing, and bidirectional Rapidly-exploring Random Tree to reduce computational load, enhance planning efficiency, and improve the planner's ability to effectively explore the state space, avoid local minima, and converge to high-quality solutions. Comparative experiments conducted on a simulation platform across multiple algorithms have demonstrated the efficacy of the proposed planner, achieving a 95 % planning success rate within 1 second. The paths generated by the algorithm exhibit superior quality and enhanced consistency. In summary, the proposed algorithm is capable of producing asymptotically optimal, collision-free paths for a dual-arm massage robot.

关键词： Hands Simulated annealing Path planning Skin Planning Space exploration Computational efficiency Collision avoidance Optimization Testing

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