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arXiv 2025年

作者： Wang, Jun Shen, Tongsheng Zhao, Dexin Zhang, Feitian Department of Advanced Manufacturing and Robotics The State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing100871 China The National Innovation Institute of Defense Technology Beijing China National Innovation Institute of Defense Technology Beijing100071 China

The artificial lateral line (ALL) is a bioinspired flow sensing system for underwater robots, comprising of distributed flow sensors. The ALL has been successfully applied to detect the undulatory flow fields generated by body undulation and tail-flapping of bioinspired robotic fish. However, its feasibility and performance in sensing the undulatory flow fields produced by human leg kicks during swimming has not been systematically tested and studied. This paper presents a novel sensing framework to investigate the undulatory flow field generated by swimmer’s leg kicks, leveraging bioinspired ALL sensing. To evaluate the feasibility of using the ALL system for sensing the undulatory flow fields generated by swimmer leg kicks, this paper designs an experimental platform integrating an ALL system and a lab-fabricated human leg model. To enhance the accuracy of flow sensing, this paper proposes a feature extraction method that dynamically fuses time-domain and time-frequency characteristics. Specifically, time-domain features are extracted using one-dimensional convolutional neural networks and bidirectional long short-term memory networks (1DCNN-BiLSTM), while time-frequency features are extracted using short-term Fourier transform and two-dimensional convolutional neural networks (STFT-2DCNN). These features are then dynamically fused based on attention mechanisms to achieve accurate sensing of the undulatory flow field. Furthermore, extensive experiments are conducted to test various scenarios inspired by human swimming, such as leg kick pattern recognition and kicking leg localization, achieving satisfactory results. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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I2EKF-LO: A Dual-Iteration Extended Kalman Filter Based LiDAR Odometry

I2EKF-LO: A Dual-Iteration Extended Kalman Filter Based LiDA...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Wenlu Yu Jie Xu Chengwei Zhao Lijun Zhao Thien-Minh Nguyen Shenghai Yuan Mingming Bai Lihua Xie State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Hangzhou Guochen Robot Technology Company Limited Hangzhou China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

LiDAR odometry is a pivotal technology in the fields of autonomous driving and autonomous mobile robotics. However, most of the current works focus on nonlinear optimization methods, and still existing many challenges in using the traditional Iterative Extended Kalman Filter (IEKF) framework to tackle the problem: IEKF only iterates over the observation equation, relying on a rough estimate of the initial state, which is insufficient to fully eliminate motion distortion in the input point cloud; the system process noise is difficult to be determined during state estimation of the complex motions; and the varying motion models across different sensor carriers. To address these issues, we propose the Dual-Iteration Extended Kalman Filter (I 2 EKF) and the LiDAR odometry based on I 2 EKF (I 2 EKF-LO). This approach not only iterates over the observation equation but also leverages state updates to iteratively mitigate motion distortion in LiDAR point clouds. Moreover, it dynamically adjusts process noise based on the confidence level of prior predictions during state estimation and establishes motion models for different sensor carriers to achieve accurate and efficient state estimation. Comprehensive experiments demonstrate that I 2 EKF-LO achieves outstanding levels of accuracy and computational efficiency in the realm of LiDAR odometry. Additionally, to foster community development, our code is open-sourced. 1

关键词： Point cloud compression Laser radar Accuracy Nonlinear distortion Noise Robot sensing systems Mathematical models Odometry Kalman filters State estimation

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Dynamic Identification for a Manipulator Model based on Stribeck Friction using the Quasi-Newton Iterative Method

Dynamic Identification for a Manipulator Model based on Stri...

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IEEE International Conference on robotics and Biomimetics

作者： Feng Xiao Feilong Zhang Bing Han Hualiang Zhang School of Information Science and Engineering Shenyang University of Technology Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China

The performance of dynamic control is intimately tied to modeling accuracy. However, traditional estimation methods and friction models, such as the least squares method and the Coulomb plus viscous model, fail to reflect the actual characteristics accurately. Particularly, the linear nature of the Coulomb plus viscous model overlooks the nonlinear static features of joint friction at slower velocities. To improve the rationality of the model structure, we integrate the Stribeck friction model into the Coulomb plus viscous model. However, introducing such nonlinearities compromises the applicability of the least squares method. As a countermeasure, we propose a new strategy that combines the least square method and the Quasi-Newton iterative method to identify the parameters of the modified nonlinear model. Additionally, the design of the excitation trajectory is critical to achieve high identification accuracy. We utilized the inverse of the smallest singular value of the observation matrix as the objective function. By minimizing it with the interior point method, we generate the excitation trajectory well-suited to stimulate dynamic characteristics. Then we leverage the discrepancies between the measured and estimated torques to assess the precision of the dynamic parameters of the manipulator. Remarkably, our proposed algorithm reduces the mean absolute error of the estimated torque by over 20.40%. Finally, an experiment of the industrial manipulator by hand guiding grab and drag is performed and shows that the proposed approach can provide the manipulator with comprehensive torque compensation.

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A Novel Way for Microvascular Network Pattern Formation Based on a Pre-Pattern Guidance Mechanism

SSRN

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SSRN 2024年

作者： Fu, Ying Sun, Mingzhu Zhao, Xin Guo, Shan Engineering Research Center of Trusted Behavior Intelligence Ministry of Education Nankai University Tianjin300350 China China Institute of Robotics and Automatic Information Systems Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin300350 China Nankai University Eye Institute Nankai University Tianjin300350 China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen518083 China

The microvascular network is an important tissue structure in animals, and its high spatial utilization of reticular pattern structure is an important guarantee for material exchange and nutrient transmission, which is crucial for tissue development, maintenance of metabolism and other life activities. However, the formation process of microvascular network pattern completion is still unclear. In this paper, based on the Turing reaction diffusion theory, we propose a network pattern formation method in the matrix environment, which is guided by morpholino pre-patterns to form the microvascular network structure by cell differentiation, and realize the morpholino network pattern at the molecular level, the differentiated cell network pattern at the cell level, and the formation of vascular network pattern under the influence of matrix environment at the tissue level, respectively, so as to establish a complete process of the formation of the microvascular network from the molecule to cells and to tissues, and explore the process of microvascular network formation. The complete process of microvascular network formation from molecules to cells to tissues was established, and the parameter conditions suitable for the formation of microvascular network patterns were explored. The network pattern formation method proposed in this paper provides a new research perspective for the study of reticular biological pattern development and pathology in the biomedical field. © 2024, The Authors. All rights reserved.

关键词： Molecules

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A Machine Learning Clustering Algorithm for Sensorless reduced Device Multilevel Inverter based active power filter control

A Machine Learning Clustering Algorithm for Sensorless reduc...

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2022 IEEE Information Technologies and Smart Industrial systems, ITSIS 2022

作者： Manai, Lazhar Rihani, Achref Besbes, Mongi National Engineering School of Tunis University of Tunis Laboratory of Robotics Informatics and Complex Systems El Manar B.P No. 37 Tunis Tunisia Higher Institute of Information and Communication Technologies University of Carthage B.P No123 Hammam Chatt1164 Tunisia

ISBN: (纸本)9781665477017

This article's primary goal is to minimize P.I regulators and maximize measurement tools while maintaining inverter capacitor voltage self-balance in order to lower costs and simplify controls. The second goal is to offer a reliable preventative solution for power grid issues. Numerical simulations and practical testing will be used to evaluate the effectiveness of the machine learning control of reduced device multilevel inverter-based active power filter control, and comparisons with alternative strategies are required to show its advantages... © 2022 IEEE.

关键词： Clustering algorithms

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A New Workspace and Structural Parameter Optimization Method of Parallel Robot

A New Workspace and Structural Parameter Optimization Method...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Haiyun Zhang Weijun Hu Renge Zhang Jin Wang Guodong Lu School of Robotics Ningbo University of Technology Ningbo China Robotics Institute of Zhejiang University Hangzhou China Zhejiang Qiantang Robot and Intelligent Equipment Reserch Co. LTD Hangzhou China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Robotics Research Center of Yuyao City Ningbo China

The parallel robot workspace refers to the set of points and boundary that can be reached by the parallel mechanism. With the continuous development and industrial application of parallel robots, the workspace analysis and optimization becomes a critical link in this process. In this paper, a workspace solution approach based on seed space segmentation and principal component analysis is proposed. In order to improve the working performance of parallel robots, a flexible workspace evaluation method based on quadratic fourth moment is proposed, and a relationship model between flexible workspace and structural parameters is established. Furthermore, based on the multi-objective genetic algorithm, the structure parameters of the parallel robot are optimized by the workspace and flexible workspace indexes. Finally, the advantage and effectiveness of proposed method are verified by experiment and comparison with traditional methods.

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Research on Industrial Control Network Security Measurement Method Based on GRU-SVM

Research on Industrial Control Network Security Measurement ...

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2022 Chinese Automation Congress, CAC 2022

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

ISBN: (纸本)9781665465335

Since the industrial Internet entered people's field of vision, industrial control network security has gradually received attention. Traditional network security measurement methods rely on the accuracy of situation sequences and historical situation values, and various factors in the network have a great influence on the results. However, compared with industrial control networks, the number and types of affected factors are less than those of the modern Internet. Therefore, based on the research on network security situational awareness, this paper proposes an industrial control network security measurement method model based on GRU-SVM. First, the data sets with threat type labels are classified, and then the similarity scoring mechanism and softmax function are introduced, to process the classification results. For the GRU-SVM classification module, the analysis of the simulation results shows that the convergence is better and the robustness is stronger. For the safety prediction measurement part, by comparing with different models, the mean absolute error and root mean square error are smaller and the reliability is higher. © 2022 IEEE.

关键词： Network security

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E-Nose-Based Cross-Domain Recognition of Combustion-Supporting Agents via M-BFFNet

E-Nose-Based Cross-Domain Recognition of Combustion-Supporti...

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Chinese Control Conference (CCC)

作者： De-Xiu Wang Qing-Hao Meng Rui-Xue Han Hong-Yue Li Hui-Rang Hou Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin China Tianjin Navigation Instruments Research Institute Tianjin China

Identification of the type of combustion-supporting agents (CSAs) by an electronic nose (e-nose) is severely limited due to the absence of untested gas concentration in the e-nose training set. In order to solve this problem, this study designs an e-nose for classifying four CSAs (Diesel, Gasoline, Ethanol and Thinner), and builds cross-concentrations data set. This study uses Gramian Angular Field method that converts one-dimensional time series data to two-dimensional data, and proposes an algorithm named the Multi-Branch Feature Fusion Network (M-BFFNet). This algorithm passes the one-dimensional and two-dimensional data through two different networks and obtains two types of features. First, the algorithm can strength the one-dimensional raw data, integrate the advantages of different networks, and fuse the characteristics of different networks. Second, it can learn the characteristics of volatile gas with specific concentrations to identify the unknown concentrations. Last, a feature fusion module is designed to improve the algorithm ability of learning odor fingerprint. In order to verify the superiority of the algorithm, this study tests the recognition performance of the e-nose based systems and compares the proposed M-BFFNet with traditional algorithms. The results show that the proposed M-BFFNet improves the identification accuracy compared to other algorithms. According to the analysis of this work, the M-BFFNet can be used to identify CSAs with unknown concentrations.

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Generative Graphical Inverse Kinematics

arXiv

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arXiv 2022年

作者： Limoyo, Oliver Marić, Filip Giamou, Matthew Alexson, Petra Petrović, Ivan Kelly, Jonathan Space and Terrestrial Autonomous Robotic Systems Laboratory University of Toronto Institute for Aerospace Studies Toronto Canada Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

Quickly and reliably finding accurate inverse kinematics (IK) solutions remains a challenging problem for many robot manipulators. Existing numerical solvers are broadly applicable but typically only produce a single solution and rely on local search techniques to minimize nonconvex objective functions. More recent learning-based approaches that approximate the entire feasible set of solutions have shown promise as a means to generate multiple fast and accurate IK results in parallel. However, existing learning-based techniques have a significant drawback: each robot of interest requires a specialized model that must be trained from scratch. To address this key shortcoming, we propose a novel distance-geometric robot representation coupled with a graph structure that allows us to leverage the sample efficiency of Euclidean equivariant functions and the generalizability of graph neural networks (GNNs). Our approach is generative graphical inverse kinematics (GGIK), the first learned IK solver able to accurately and efficiently produce a large number of diverse solutions in parallel while also displaying the ability to generalize—a single learned model can be used to produce IK solutions for a variety of different robots. When compared to several other learned IK methods, GGIK provides more accurate solutions with the same amount of data. GGIK can generalize reasonably well to robot manipulators unseen during training. Additionally, GGIK can learn a constrained distribution that encodes joint limits and scales efficiently to larger robots and a high number of sampled solutions. Finally, GGIK can be used to complement local IK solvers by providing reliable initializations for a local optimization process. Copyright © 2022, The Authors. All rights reserved.

关键词： Inverse kinematics

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Design and Experimental Evaluation of a Hexapod Mobile Robot with C-Shaped Legs 8

Design and Experimental Evaluation of a Hexapod Mobile Robot...

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8th IEEE International Conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Wu, Yujie Li, Dangchao Dong, Xia Wang, Xin Zheng, Chunli Zhu, Aibin Zhang, Zheng Zhou, Xu Zhang, Yulin Institute of Robotics & Intelligent Systems Xi'An Jiaotong University Shaanxi Key Laboratory of Intelligent Robots Xi'an710049 China Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi'an710049 China School of Construction Machinery Chang'An University Xi'an710064 China Xi'An Jiaotong University Xi'an710049 China School of Energy and Power Engineering xi'An Jiaotong University Xi'an710049 China School of Mechanical Engineering Northwestern Polytechnical University Xi'an710072 China Taiyuan Research Institute of China Coal Technology & Engineering Group Co. Ltd Taiyuan030006 China

ISBN: (纸本)9798350300178

Drawing on extant literature on hexapod robots, we present a novel obstacle-crossing hexapod robot that can adapt to multiple terrains. We designed the robot's entire mechanical structure, established a mathematical model for the robot leg, deduced the kinematic equation of the leg, and determined the relationship between the robot's motion speed and the motor angle. Through force analysis of the C-shaped leg, we obtained the function between motor output torque and motion phase. Subsequently, we constructed the physical hexapod robot and conducted performance testing. Our experimental results demonstrate that the hexapod robot can move at a speed of 0.5 m/s and successfully cross a 10 cm obstacle. The robot exhibits excellent movement performance and satisfies the functional requirements. Overall, our design offers a promising approach for developing hexapod robots that can navigate a variety of terrains. © 2023 IEEE.

关键词： Machine design

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