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Chinese Control Conference (CCC)

作者： Zhongyan Liu Biao Lu Jing Zhang Institute of Robotics and Automatic Information System Nankai University Tianjin Institute of Intelligence Technology and Robotics Systems Shenzhen Research Institute of Nankai University Shenzhen

The traditional measurement method of the load swing angle for the bridge crane is easily disturbed by the environmental noise, which leads to inaccurate measurement and delayed feedback. In this paper, the computer vision method is used to realize the non-contact measurement of the load swing angle. A rectangular light source is used as a marker and it is detected using an industrial camera. In order to improve the accuracy and real-time performance of the detection, the method of color separation combined with rectangular contour detection is used to quickly identify the marker. Then the swing angle is calculated according to the recognition results by using the camera geometry. It has been demonstrated by experiments that, the algorithm can quickly identify the marker and can accurately calculate the swing angle. In particular, the proposed method has strong independence and anti-interference ability, which is very suitable for bridge crane system.

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A Novel Gas Classification Method Based on The Improved GasNet

A Novel Gas Classification Method Based on The Improved GasN...

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第43届中国控制会议

作者： Xiaolin Qin Lei Cheng Xiang Tao Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology School of Information Science and Engineering Wuhan University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Accurately and promptly identifying harmful gases in the environment can effectively prevent many accidents. Most existing pattern recognition methods are focused on addressing gas classification problems in static scenarios, but the real world is a dynamic and changing environment. Therefore, researching gas classification in continuous learning scenarios will better meet the needs of suddenly emerging gas classification tasks in real-world environments. To address this issue, an incremental learning model was introduced into gas classification, and an improved deep convolutional network was proposed for gas image feature extraction. The proposed model and other baseline methods experimented on an open-source gas dataset. The proposed model reduces knowledge loss due to catastrophic forgetting and effectively classifies the 10 gas samples in the dataset in continuous learning scenarios compared to non-incremental methods. It outperforms other baseline incremental methods by optimizing the network's shortcut connections, making the feature extraction network easier to train and improving gas classification performance. In dynamic learning scenarios, the proposed model achieved a classification accuracy of 86.30% for 10 gas *** experimental results demonstrate that the proposed method effectively mitigates catastrophic forgetting interference and completes gas classification tasks in continuous learning scenarios.

关键词： Gas classification Incremental learning Shortcut connection Catastrophic forgetting

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Low-light image enhancement based on Transformer and CNN architecture

Low-light image enhancement based on Transformer and CNN arc...

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第35届中国控制与决策会议

作者： Keyuan Chen Bin Chen Shiqian Wu Institute of Robotics and Intelligent System School of Information Science and Engineering Wuhan University of Science and Technology

Affected by low photon counts and noise,images captured in low-light environments commonly have low signalto-noise ratios,details losing,low contrast and other ***-light image enhancement which restores clear images from low-light images with unsatisfactory quality is *** this paper,we propose a strong baseline model based on transformer and CNN architecture for low light image enhancement(TrCLLE).In TrCLLE,the advantages of transformer in effectively modeling long-distance dependence and convolutional neural network in modeling local features through inductive bias are *** consists of three parts:shallow feature extraction module,deep feature extraction module and high-quality image reconstruction *** last,the factors affecting performance are analyzed in detail and the experimental results demonstrate that the proposed method achieves enhanced images with higher quality compared to other popular approaches.

关键词： transformer self-attention mechanism convolutional neural network low-light image enhancement

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Gas Source Localization for UAV Using Infotaxis-Based Three-Dimensional Algorithm and Particle Filtering

Gas Source Localization for UAV Using Infotaxis-Based Three-...

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第43届中国控制会议

作者： Duan He Cheng Lei Wang Rong Zhang Yu Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology School of Information Science and Engineering Wuhan University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Unmanned aerial vehicles(UAVs) are extensively utilized in gas source localization tasks due to their payload capacity and maneuverability. This paper presents a UAV gas source localization algorithm that utilizes three-dimensional Infotaxis and particle filter. The algorithm is designed for gas source localization of UAVs in dynamic outdoor airflow *** source localization process comprises UAV search area partitioning, particle filter initialization, observation and update of probability map, entropy calculation and direction selection, particle direction and weight update, and UAV movement. To validate the effectiveness of the algorithm, we build a CFD gas environment and UAV source localization platform, and compare it with traditional Infotaxis-based source localization algorithms. The proposed algorithm based on three-dimensional Infotaxis and particle filter demonstrates higher accuracy.

关键词： infotaxis particle filtering gas source localization olfactory drone path planning

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Point cloud segmentation network based on multi-scale feature fusion

Point cloud segmentation network based on multi-scale featur...

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2024 China Automation Congress, CAC 2024

作者： Deng, Hao Cheng, Peng Cheng, Shengmei Liu, Cheng Du, Shaoyi Wang, Lin State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Service School of Information Science and Technology Northwest University Xi’an China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9798350368604

Aiming at improving the local feature extraction for point cloud learning, we introduce a point cloud segmentation network that enhances the PointNet++ framework with a local feature transformation module and a multi-scale feature fusion module. The local feature transformation module employs operations akin to convolution to restructure the point cloud's local feature dimensions, thus boosting the network's ability to extract detailed local features. Additionally, the multi-scale feature fusion module integrates semantic features of point clouds across various scales using a layered network design, aiming to heighten segmentation precision. Experimental results indicate that the proposed network can achieve a robust point cloud segmentation accuracy, reaching an accuracy rate of 85.8%. © 2024 IEEE.

关键词： Cloud platforms

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Double-Modal Locomotion of a Hydrogel Ultra-Soft Magnetic Miniature Robot with Switchable Forms

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Cyborg and Bionic systems 2024年第1期5卷 817-825页

作者： Shihao Zhong Zhengyuan Xin Yaozhen Hou Yang Li Hen-Wei Huang Tao Sun Qing Shi Huaping Wang Intelligent Robotics Institute School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing 100081China Peking University First Hospital Beijing 100034China Laboratory for Translational Engineering Harvard Medical SchoolCambridgeMA 02139USA Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of TechnologyBeijing 100081China Key Laboratory of Biomimetic Robots and Systems(Beijing Institute of Technology) Ministry of EducationBeijing 100081China

Flexible miniature robots are expected to enter difficult-to-reach areas in vivo to carry out targeted operations,attracting widespread ***,it is challenging for the existing soft miniature robots to substantially alter their stable shape once the structure is *** limitation leads to a fixed motion mode,which subsequently restricts their operating *** this study,we designed a biocompatible flexible miniature robot with a variable stable form that is capable of adapting to complex terrain environments through multiple movement *** by the reversible stretching reaction of alginate saline gel stimulated by changes in environmental ion concentration,we manufactured a morphologically changeable super-soft hydrogel miniature robot *** to the stretch and contraction shapes of the flexible hydrogel miniature robot,we designed magnetic fields for swing and rolling motion modes to realize multishape *** experimental results demonstrate that the deflection angle of the designed flexible miniature robot is reversible and can reach a maximum of 180°.The flexible miniature robot can complete forward swinging in the bar stretch state and tumbling motion in the spherical *** anticipate that flexible hydrogel miniature robots with multiple morphologies and multimodal motion have great potential for biomedical applications in complex,unstructured,and enclosed living environments.

关键词： double modal locomotion flexible miniature robots adapting co targeted operationsattracting magnetic miniature robot hydrogel switchable forms soft miniature robots

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Probing Local Cellular Mechanics by Atomic Force Microscopy with Modified Spherical Tip

Probing Local Cellular Mechanics by Atomic Force Microscopy ...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Zhaofeng Zuo Xiaoming Liu Xiaoqing Tang Fengyu Liu Dan Liu Yuyang Li Qiang Huang Tatsuo Arai Beijing Advanced Innovation Center for Intelligent Robots and Systems and the Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China

Abstract-The mechanical characterization of cell is important for knowing the physiological state and studying diseases of organism. While many approaches are available for measuring cellular elasticity, distinguishing the stiffness variation among different cellular areas is still a challenge. In this paper, we reported a method to modify spherical atomic force microscopy (AFM) tip for accurate measurement of Young’s modulus in several areas on single adherent living cancer cells (Hela cells). The micrometer size spheres were transported to an AFM probe tip by dual micropipettes and fixed by ultraviolet (UV) curable glue, which is reproducible and nondestructive to the cantilever. The force-displacement curves were measured along major axis of cells with the modified AFM probe in indentation experiments. The results demonstrate that the modulus value increased with the detected point approaching the nucleus center, and areas closed to nucleus showed have higher stiffness. Our study provides a quantitative method for static measurement of different locations of cell, and the result has the potential to reveal how intracellular structures have effect on the cell mechanical characterization.

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Automated Food Intake Monitoring System to Prevent Malnutrition Using the Tiago Robot Camera

Automated Food Intake Monitoring System to Prevent Malnutrit...

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2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Konstantakopoulos, Fotios S. Plati, Daphni K. Pliakou, Labrina A. Di Luzio, Francesco Scotto Tagliamonte, Nevio Luigi Zollo, Loredana Tsiknakis, Manolis Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department Greece Forth Biomedical Research Institute IoanninaGR 45110 Greece Campus Bio-Medico University Research Unit of Advanced Robotics and Human-Centred Technologies Rome Italy Forth Institute of Computer Science Crete HeraklionGR 70013 Greece

ISBN: (纸本)9798350383386

The management of a daily diet is a significant concern among individuals in modern culture. The utilization of dietary assessment systems has significantly contributed to the efficient management of malnutrition and dietary habits over a period of time. In order to determine the nutritional value of the food that individuals consume, this study introduces a novel food monitoring system and its architecture. By creating a dataset of food images in a tray and using image processing, machine learning, and computer vision techniques, the nutrients and calories consumed by a patient are calculated. The proposed system achieves a 95.6% Intersection over Union score in the segmentation task, 92.3% top-1 accuracy in the classification task, and 4.6 g mean weight absolute error in the weight estimation *** Relevance: The proposed system has the ability to calculate the nutritional composition, encompassing calories, proteins, fats, and carbs, of food consumed by a patient, making it suitable for nutritional monitoring applications and healthcare systems that aim to monitor malnutrition in hospital patients. © 2023 IEEE.

关键词： Nutrition

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Dynamics Modeling and Controller Design for a Novel Indoor Blimp

Dynamics Modeling and Controller Design for a Novel Indoor B...

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Chinese Control Conference (CCC)

作者： Jinyang Dong Yongchun Fang Institute of Robotics and Automatic Information System Nankai University Tianjin Institute of Intelligence Technology and Robotics Systems Shenzhen Research Institute of Nankai University Shenzhen

Due to its safety and endurance, the indoor blimp robot is an ideal choice for cruise monitoring and inspection. In order to adapt to the complex indoor scene, a helium-filled blimp is studied in this paper. The dynamic model of the aerial robot is established by using Newton Euler equation, and the main characteristics are then analyzed based on the derived model. Furthermore, the linear quadratic regulator controller is designed to control the airship motion in the vertical plane. Based on the model, a simulation platform is constructed for further analysis and controller verification. Finally, simulation results are presented to show the correctness of the dynamic model.

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Design of RBF Neural Network Based on Entropy and Sensitivity Analysis

Design of RBF Neural Network Based on Entropy and Sensitivit...

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第35届中国控制与决策会议

作者： Zhenlin Dong Shiqian Wu Institute of Robotics and Intelligent System School of Information Science and Engineering Wuhan University of Science and Technology

Aiming at the problem of how to determine the hidden layer structure of radial basis function(RBF) neural network,an RBF neural network algorithm based on entropy and sensitivity analysis is proposed to optimize the network ***,an entropy-based initial clustering is proposed to get the number and position of the initial clustering centers which serve as the input to the K-means ***,a hidden node deletion method based on sensitivity analysis is proposed to analyze the weight and standard deviation between the hidden layer with the output layer,and the hidden nodes which have low contributions to the network will be *** experiments on the nonlinear function approximation and the nonlinear dynamic system identification are set to verify the proposed *** results show that the proposed method achieves stronger approximation ability and better identification effect than the state-of-the-art methods.

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