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16th International Conference on Social robotics, ICSR + InnoBiz 2024

作者： Xu, Yue Zhang, Yinlong Liu, Shuai Liu, Yuanhao Liang, Wei He, Hongsheng Shenyang University of Technology Shenyang110020 China Guangzhou Institute of Industrial Intelligence Guangzhou511458 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Computer Science The University of Alabama TuscaloosaAL35487 United States

ISBN: (纸本)9789819611508

Accurately tracking the robotic arm and human joints is crucial to ensure safety during human-robot interaction. However, traditional pose tracking methods often exhibit insufficient performance and robustness in complex environments. The variations in the robotic arm’s environment and posture make it challenging for traditional methods to accurately capture the positions and posture of its joints. Specifically, when addressing challenges such as high similarity, occlusion, background complexity, and joint recognition failures, they often struggle to provide reliable and accurate results. To address these challenges, this paper proposes a human-robot pose tracking algorithm based on a new convolutional neural network model. To enhance detection accuracy, an improved color detection module is introduced to resolve joint misclassification. A geometric perception module is designed to accurately locate joints even under occlusion. Additionally, innovative iEMA and DBB modules are incorporated. The iEMA module employs edge detection technology to dynamically adjust thresholds for correct matching by improving the edge matching process. The DBB module refines the boundary box parameters for precise localization by introducing adaptive bounding box updates in real-time. This algorithm also integrates human pose recognition, enabling real-time pose recognition, thereby facilitating more intelligent and natural human-robot interaction. The algorithm has been rigorously evaluated on a custom-designed robotic arm platform. Experimental results validate the algorithm’s effectiveness and feasibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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Trajectory Control of Multi-Axis Robotic Arms Based on Physics-Informed Neural Networks and Nonlinear Model Predictive Control

Trajectory Control of Multi-Axis Robotic Arms Based on Physi...

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Chinese automation Congress (CAC)

作者： Yiqing Wang Changqing Xia Xi Jin Chi Xu Yanzhu Zhang School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Multi-axis robotic arms are extensively utilized in intelligent manufacturing scenarios, with trajectory control in flexible scenarios constituting a primary challenge. Physics-Informed Neural Networks (PINNs) represent advanced methods that integrate physical laws with data-driven approaches. Despite their outstanding performance in integrating theory and data, research on their application for controlling robotic arms in complex scenarios remains limited. This paper establishes a nonlinear dynamic model based on the physical relationships between the axes of robotic arms. In the absence of prior data, Sobol sequence random sampling is employed to generate data, which are subsequently trained using PINNs. Considering system noise, the Extended Kalman Filter (EKF) is utilized to predict the next state in noisy environments, and Nonlinear Model Predictive Control (NMPC) is implemented to control the robotic arm for trajectory tracking, achieving real-time control. Simulation results demonstrate that the proposed Discrete Physics-Informed Predictive Control (DPIPC) method exhibits smaller position and velocity errors with less fluctuation compared to the method in the reference, indicating superior control capabilities.

关键词： Fluctuations Trajectory tracking Simulation Noise Neural networks Manipulators Real-time systems Trajectory Manufacturing Predictive control

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A DDOS Attack Traffic Classification Model for Industrial Internet Based on CNN-LSTM

A DDOS Attack Traffic Classification Model for Industrial In...

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2022 Chinese automation Congress, CAC 2022

作者： Wei, Weixuan Liu, Xianda Sheng, Chuan Feng, Ansong Shenyang University of Chemical Technology College of Information Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665465335

With the continuous development and application of industrial Internet, the weakness of the security defense capability of industrial Internet has become increasingly prominent. This paper presents an end-to-end classification model combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network for DDoS attacks over industrial Internet. By self-learning the spatial and temporal characteristics of the original attack traffic, the model can avoid subjective errors that may occur due to manual selection of features. This method is a feasible means, with an accuracy rate of 99%. © 2022 IEEE.

关键词： Long short-term memory

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Fault Diagnostic Opportunities for Electromagnetic Coils of Active Magnetic Bearings using Physics-of-Failure Analysis 5

Fault Diagnostic Opportunities for Electromagnetic Coils of ...

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5th International Conference on System Reliability and Safety Engineering, SRSE 2023

作者： Li, Ruiqi Wang, Kai Zeng, Peng Cao, Jiwei Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110016 China Harbin Institute of Technology Department of Electrical Engineering China

ISBN: (纸本)9798350305944

Active magnetic bearings (AMBs) have many advantages over traditional oil bearings due to their non-contact characteristics. They are environmentally friendly solution and have been proven to be highly reliable and available. Electromagnetic coils are vital components of AMB and they are failure prone. In this paper, the failure mode of magnetic bearing coil and its corresponding failure mechanism are reviewed by FMMEA method, and the existing coil fault diagnosis methods are introduced. Future directions for coil insulation health monitoring aiming at the application characteristics of AMBs are also discussed. © 2023 IEEE.

关键词： Failure analysis

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MODALPROMPT:DUAL-MODALITY GUIDED PROMPT FOR CONTINUAL LEARNING OF LARGE MULTIMODAL MODELS

arXiv

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arXiv 2024年

作者： Zeng, Fanhu Zhu, Fei Guo, Haiyang Zhang, Xu-Yao Liu, Cheng-Lin State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation CAS China School of Artificial Intelligence UCAS China Centre for Artificial Intelligence and Robotics HKISI-CAS Hong Kong

Large Multimodal Models (LMMs) exhibit remarkable multi-tasking ability by learning mixed datasets jointly. However, novel tasks would be encountered sequentially in dynamic world, and continually fine-tuning LMMs often leads to performance degrades. To handle the challenges of catastrophic forgetting, existing methods leverage data replay or model expansion, both of which are not specially developed for LMMs and have their inherent limitations. In this paper, we propose a novel dual-modality guided prompt learning framework (ModalPrompt) tailored for multimodal continual learning to effectively learn new tasks while alleviating forgetting of previous knowledge. Concretely, we learn prototype prompts for each task and exploit efficient prompt selection for task identifiers and prompt fusion for knowledge transfer based on image-text supervision. Extensive experiments demonstrate the superiority of our approach, e.g., ModalPrompt achieves +20% performance gain on LMMs continual learning benchmarks with ×1.42 inference speed refraining from growing training cost in proportion to the number of tasks. The code will be made publically available. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge transfer

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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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Towards Automated Ultrasound Scanning Using Vision-Based Navigation: From Physician Skill Learning to Robotic Reproduction 5

Towards Automated Ultrasound Scanning Using Vision-Based Nav...

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5th World Robot Conference Symposium on Advanced robotics and automation, WRC SARA 2023

作者： Hao, Mingrui Zhang, Pengcheng Chen, Chen Wang, Shuangyi University of Chinese Academy of Sciences School of Artificial Intelligence 19 Yuquan Road Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems 95 Zhongguancun East Road Beijing China Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong

ISBN: (纸本)9798350307320

Ultrasound scanning is widely employed for disease diagnosis and procedure guidance. However, conventional ultrasound depends on the sonographer's manual operation, resulting in poor repeatability. Although robot-assisted ultrasound has been extensively studied, it has been a challenge in the field to further automate the examination and make the robotic system more practical for primary use. In this study, we propose a visual navigation method based on the skill learning of the sonographer. The key information about the probe's movement, including position and angle, was recorded using vision-based methods on 14 cases based on a professional sonographer's practice. Then, an automatic scanning method based on visual guidance, human-machine collaboration, and path planning was designed using a novel parallel mechanismbased ultrasound robot. The strategy of traversal scanning is a key consideration in facilitating efficient coverage of desired ranges and involves two primary steps: waypoint selection and waypoint planning. To compare the efficiency of different schemes proposed for these two respective stages, a simulation study and an actual running time test of the robot were conducted to assess the weighted coverage, energy, and time consumption. The results demonstrate that the proposed traversal strategy can effectively cover the statistical area formed by the physician's scanning experience, and the new workflow is promising to be tested in-vivo in our future work. © 2023 IEEE.

关键词： Ultrasonics

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2D-3D Object Shape Alignment for Camera-Object Pose Compensation in Object-Visual SLAM

2D-3D Object Shape Alignment for Camera-Object Pose Compensa...

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IEEE International Conference on robotics and automation (ICRA)

作者： Hanyeol Lee Jae Hyung Jung Chan Gook Park Department of Aerospace Engineering and Automation and Systems Research Institute Seoul Natioinal University Seoul South Korea Smart Robotics Lab School of Computation Information and Technology Technical University of Munich Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this study, we propose an object shape alignment method through a robust optimization scheme for 6-degrees-of-freedom (DOF) object pose compensation. Although the pose estimation of the 3D object by the camera has been rapidly improved in recent years with the development of deep learning, the estimate still contains errors due to several factors. To compensate for this, we perform a shape alignment between the 2D segmentation of the object and the projection of the 3D object in the image plane. To avoid convergence to a local minimum in nonlinear optimization, we separate the pose into translation and rotation. This approach derives the optimization of a linear form in terms of a translation with reduced computational cost. For the rotation, the parallel optimization is performed with multiple initial values, reflecting to the uncertainty of an initial value. We formulate an invariant extended Kalman filter (EKF)-based object-visual simultaneous localization and mapping (SLAM) with a camera-object relative pose as the measurement model. To verify the performance of the proposed algorithm, we present the improved results of camera-object relative pose accuracy and localization and mapping accuracy in the several sequences of YCB-video dataset.

关键词： Location awareness Image segmentation Three-dimensional displays Simultaneous localization and mapping Uncertainty Accuracy Shape

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Collaborative turning and jumping control of a cyborg locust via sensory stimulation 3

Collaborative turning and jumping control of a cyborg locust...

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3rd IEEE International Conference on Electrical, Control and Instrumentation engineering, ICECIE 2021

作者： Liu, Peng Ma, Songsong Liu, Shen Li, Yao Li, Bing Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen518055 China Harbin Institute of Technology State Key Laboratory of Robotics and Systems Shenzhen518055 China

ISBN: (纸本)9781665449663

In this study, we describe the first-ever demonstration of the turning and jumping control of locusts based on electrical stimulation, which can realize a cyborg jumper. In terms of perception, walking distance, speed, payload, maneuverability, and adaptability to complex environments, cyborgs can reveal unmatchable advantages to insect-scale artificial robots. Herein, live locusts (Locusta migratoria) were selected as the study platform. Antennae and cercus stimulations using electrical pulses were applied to elicit the insect to turn and jump. We examined both the turning angle and the angular velocity by tuning the stimulus amplitude or frequency. It is found the turning rate induced by antenna stimulation can be graded. Besides, the stimulation of the cercus at the distal abdomen triggered the jump reliably. Moreover, the directional jumping control of the locust was achieved by the collaboration of turning and jumping control. Such results provide the potential to establish a more precise and more efficient model to develop a direction-controllable cyborg jumper. © 2021 IEEE.

关键词： Antennas

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Leg Transmission Mechanism for Enhanced Load-Carrying Capacity: Knee Joint Torque Optimization Design

Leg Transmission Mechanism for Enhanced Load-Carrying Capaci...

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IEEE International Conference on Development and Learning, ICDL

作者： Quan Huang Yixuan Guo Haoyun Yan Yuquan Leng Shoubin Liu Chenglong Fu School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and Guangdong Provincial Key Laboratory of Human Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China

This study proposes an optimized design for the leg transmission structure of a quadruped robot, aiming to enhance the load-bearing capacity of the knee joint. Firstly, the dynamic simulation of the single leg's load-lifting process in the quadruped robot is conducted, indicating higher torque demands on the knee joint during this process. Therefore, a leg transmission mechanism, combining a ball screw mechanism and a crank-slider mechanism, is designed to effectively amplify the torque of the knee joint motor. Subsequently, a virtual prototype of the quadruped robot, incorporating this design, is constructed using Adams software. The foot trajectory of the robot is pre-planned, and the joint trajectories are obtained through inverse kinematics, using them as the driving functions for the quadruped robot to perform actions such as load lifting, static standing, and diagonal walking gait. Finally, the knee joint torque and corresponding motor driving torque of the quadruped robot's legs are measured and compared. The results demonstrate that the driving torque of the knee joint motor is effectively amplified by the leg transmission mechanism, with the maximum amplification observed during the initial stage of load lifting, reaching a magnification factor of nearly 7.66 times. This verifies the effectiveness of the proposed leg transmission mechanism in enhancing the load-bearing capacity of the knee joint.

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