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第三十九届中国控制会议

作者： Guan-nan Li Ming Zeng Yu Ma Qi Li Wen-kang Xu Institute of Robotics and Automation Systems School of Electrical and Information Engineering Tianjin University

Transformable robots are attracting increasing interest because of their ability of strong environmental *** this paper, a double-body car-snake hybrid transformable robot with flexible movement and strong environmental adaptability was developed to meet the needs of automatic inspection in complex industrial environments. A special connecting rod with aid of the digital steering gear was designed for the transformable robot, which enables it to quickly switch between car-shaped and snake-shaped moving postures. The transformable robot was also equipped with special mecanum wheels, which allow it to move quickly and omnidirectionally in open areas with the car-shaped posture. In addition, the mecanum wheels can provide the critical axial driving force for the robot in the snake-shaped posture, and with the help of angle control of the digital steering gear,the robot can easily cross the narrow curved channels with different radians. The details of the mechanical and electrical control system design were provided in this paper. Moreover, various motion modes of the transformable robot in different scenarios were demonstrated through experiments.

关键词： Transformable structure Mobile robot Car-snake hybrid Double-body Mecanum wheel

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Weighted Complex Network Based on Visibility Angle Measurement

Weighted Complex Network Based on Visibility Angle Measureme...

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第三十九届中国控制会议

作者： Ming Zeng Wenkang Xu Chunyu Zhao Qi Li Jingjing Han Institute of Robotics and Automation Systems School of Electrical and Information Engineering Tianjin University

Visibility graph（VG） is a widely applicable tool for studying complex time series due to its advantages of few parameters and easy implement. However, the topological links of VG based methods are roughly represented as binary *** order to better inherit the advantages of visibility graph and more accurately dig out the internal characteristics of time series,a novel method of constructing an weighted complex network based on visibility angle measurement, i.e., visibility angle based weighted visibility graph（VA-WVG）, is proposed. Firstly, the data points of the original time series are represented as the nodes of the complex network. Secondly, after judging that the two data points satisfying the visibility criterion, visibility angle between the two data points is measured, which is used as the weight of the network connection. Mapping logistic signals under different parameter settings into VA-WVG, and extracting the network feature of strength distribution entropy, we find that the VA-WVG can not only accurately discriminate periodic signals from the chaotic signals, but also can precisely reflect the number of periodic state solutions and complexity of chaotic state, while the traditional VG can only roughly distinguish between periodic states and chaotic states. Next, the proposed method is applied to classify different types of EEG signals. Combining the extracted features and support vector machine, we identify epileptic EEG signals from healthy EEG signals with classification accuracy of100%, while the accuracy of the VG method is only 83.84%.

关键词： visibility graph time series complex network

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PublicGarbageNet : A Deep Learning Framework for Public Garbage Classification

PublicGarbageNet : A Deep Learning Framework for Public Garb...

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第三十九届中国控制会议

作者： Ming Zeng Xiangzhe Lu Wenkang Xu Tongxi Zhou Yinbo Liu Institute of Robotics and Automation Systems School of Electrical and Information EngineeringTianjin University

Intelligent garbage classification is an important technique for garbage harmless, reducing and recycling treatment. In this paper, we propose a public garbage classification algorithm based on the CNN architecture, namely Public Garbage Net. The proposed algorithm is a multi-task classification algorithm in which one task identifies four major categories of domestic garbage,and the other task achieves recognition of 10 subclasses garbage. The two classification tasks are related to each other, and the joint loss function is helpful to improve the accuracy of garbage recognition. Considering the fact that the existing garbage datasets are incomplete in classes and small in quantity, we constructed a new pubic garbage dataset including 10 subclasses and a total of 10624 images. In order to obtain better performance, systematically studies such as backbone optimization selection,data augmentation, learning rate optimization, and label smoothing have been made, and finally the accuracy of the optimized model reaches 96.35%.

关键词： garbage classification public garbage dataset multi-task learning data augmentation

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Enhanced Autonomous Exploration and Mapping of an Unknown Environment with the Fusion of Dual RGB-D Sensors

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Engineering 2019年第1期5卷 164-172页

作者： Ningbo Yu Shirong Wang Institute of Robotics and Automatic Information Systems Nankai University Tianjin 300353 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin 300353 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China

The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate twodimensional (2D) grid maps. Red/green/blue-depth (RGB-D) sensors provide both color and depth information on the environment, thereby enabling the generation of a three-dimensional (3D) point cloud map that is intuitive for human perception. In this paper, we present a systematic approach with dual RGB-D sensors to achieve the autonomous exploration and mapping of an unknown indoor environment. With the synchronized and processed RGB-D data, location points were generated and a 3D point cloud map and 2D grid map were incrementally built. Next, the exploration was modeled as a partially observable Markov decision process. Partial map simulation and global frontier search methods were combined for autonomous exploration, and dynamic action constraints were utilized in motion control. In this way, the local optimum can be avoided and the exploration efficacy can be ensured. Experiments with single connected and multi-branched regions demonstrated the high robustness, efficiency, and superiority of the developed system and methods.

关键词： Autonomous exploration Red/green/blue-depth Sensor fusion Point cloud Partial map simulation Global frontier search

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Online Triangular Constraint Calibration for LiDAR and Cameras in Open-Pit Mines

IEEE Transactions on Field Robotics

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IEEE Transactions on Field robotics 2024年 2卷 131-146页

作者： Yuchen Li Luxi Li Siyu Teng Zhenshan Bing Alois Christian Knoll Zhe Xuanyuan Long Chen Guangdong Provincial/Zhuhai Key Laboratory of IRADS BNU-HKBU United International College Zhuhai China Faculty of Science Hong Kong Baptist University Hong Kong SAR China Research and Development Center WAYTOUS Inc. Beijing China Department of Information Technische Universität München Munich Germany State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Department of Artificial Intelligence Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China

Extrinsic calibration between a LiDAR and a camera is a crucial task for intelligent vehicles and mobile robots. However, most of the current extrinsic calibration methods are offline and target-based, making it hard to achieve real-time calibration depending on the characteristics of the environments. Although a few online calibration strategies can solve the above drawbacks, they still rely on a large amount of labeled data and struggle to achieve satisfactory performance in extrinsic calibration in unstructured scenarios where valid targets are scarce. In this work, we propose an effective online calibration strategy for mining scenarios, one of the typical unstructured scenarios. Based on the analysis of mining area data, we adopt an approach that integrates image and point cloud segmentation to accomplish the entire calibration process. An initialization method supporting a single rigid target is proposed, effectively addressing the initialization failure caused by the scarcity of targets in mining areas. Incorporating the segmentation results from the right camera to establish a left-right camera-LiDAR triangular constraint effectively reduces translation errors. Temporal data with confidence scores are then used to update and optimize the extrinsic parameters. Our online extrinsic calibration method has been validated on mining scenarios data and has achieved less than 0.2 m in data collected from an SUV and 0.3 m from trucks in translation errors within 10 frames. Besides, we provide a detailed discussion on the challenges of calibration in mining areas and the limitations of the proposed method.

关键词： Calibration Image segmentation Cameras Sensors Point cloud compression Laser radar Translation Image edge detection Accuracy Mining industry

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Enhanced Wi-Fi indoor positioning real application based on access points optimization ensemble model

Enhanced Wi-Fi indoor positioning real application based on ...

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Chinese Control and Decision Conference, CCDC

作者： Zheng Yao Huaiyu Wu Yang Chen Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781665478977

When designing a Wi-Fi indoor positioning system in large, real-world environments, the problem we have to face is that available access points of indoor location scenes are very extensive, and the signal interference is very serious, leading to weak positioning accuracy. Besides, too many access points also increase the computation complexity and the storage size of the fingerprint database. To remedy those problems, we propose a new ensemble model consisting of Gaussian Mixture Model (GMM) region classifier and Random Forest feature learner using best-discriminating access points optimization to enhance Wi-Fi indoor positioning real application. The GMM classifier is applied to get better regional classification, and for the same area, the Random Forest feature learner is trained to obtain best-discriminating access points optimization. To fully reflect the performance of the proposed method, experiments have been carried out in the real environment of indoor parking lots and the performance of the proposed algorithm was compared with the other three existing access points optimization methods. The results show that the proposed access points optimization ensemble model successfully enhances Wi-Fi indoor positioning real application of indoor parking lots.

关键词： Optimization methods Estimation Interference Fingerprint recognition Planning Reliability Wireless fidelity

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The generalized synchronization for double-layer delayed coupling complex networks by dynamic soft variable structure control method

The generalized synchronization for double-layer delayed cou...

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Chinese automation Congress (CAC)

作者： Kang Qian Zhihuan Chen Yanqin Sun Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

This study focuses on the synchronization problem of double-layer complex networks with time delayed coupling. Distinguish from most of previous works, a more general network model is introduced, where the number and the dimension of the nodes can be different between the drive network and the response network. This network model is more close to the practice. In order to obtain the synchronization of double-layer complex networks, an ingenious dynamic soft variable structure controller is proposed, where three sub-controllers and one adaptive observers are designed, and the stability of the controller is demonstrated by a strict mathematical proof. Numerical simulations verify the effectiveness of proposed strategy.

关键词： Couplings Complex networks Observers Drives Numerical simulation Stability analysis Mathematical models

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Optimization of input shaping parameters based on genetic algorithm 4

Optimization of input shaping parameters based on genetic al...

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4th International Symposium on Power Electronics and Control Engineering, ISPECE 2021

作者： Lu, Xu Jia, Danping Zhang, Hualiang Zhao, Bingjie Zhang, Tao Liu, Yang Yan, Hongyu School of Artificial Intelligence Shenyang University of Technology Shenyang China School of Information Science and Engineering Shenyang University of 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: (数字)9781510650374

ISBN: (纸本)9781510650367

In view of the position control vibration problem of permanent magnet synchronous motor, the control model of permanent magnet synchronous motor is simulated by using Simulink software. At the same time, according to the structure of the system model, an input shaper is added to the input end of the system to suppress the vibration of the system. However, the input parameters of the shaper are highly dependent on the parameters of the system model, so it is usually necessary to accurately identify the system model. However, the identification of permanent magnet synchronous motor is often not accurate enough, which makes it difficult to determine the input parameters of the shaper. Aiming at the problem that the input shaper is too dependent on the system, a method of input shaper parameter optimization based on genetic algorithm is designed and verified by experiments. The results show that the parameters of the input shaper with better effect can be selected by genetic optimization, and the dependence of the input shaper on the system model is reduced. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Computer control systems

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Fault Diagnosis Based on Multi-scale LSTM-FCNs for Industrial Process

Fault Diagnosis Based on Multi-scale LSTM-FCNs for Industria...

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Yang, Shaojie Li, Peng Li, Shuai Zhou, Xiaofeng Jiang, Shanghong Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and IntelligentManufacturing Chinese Academy OfSciences Shenyang110169 China University of Chinese Academy OfSciences Beijing100049 China Information Center of Ansteel Mining Limited Company Anshan114031 China

ISBN: (纸本)9781665401159

Aiming at the characteristics of multi-scale, time series and nonlinear of the complex industrial process data, in order to extract process information more efficiently for improving the diagnosibility, a novel fault diagnosis method based on multi-scale long short term memory- fully convolutional networks (LSTM-FCNs) for industrial process is proposed. Firstly, variational mode decomposition (VMD) is used to obtain the multi-scale components of the process data. Then a parallel LSTM and FCNs optimized by batch normalization and random inactivation is built to extract features for each component. Finally, based on the fusion of multi-scale features, a fully connected layer is used for diagnosis. The LSTM-FCNs method is evaluated by the Tennessee Eastman (TE) benchmark industrial process, and compared with other methods, its effectiveness and superiority are verified. © 2021 IEEE.

关键词： Deep neural networks

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Curved path planning based on 3D vision water immersion ultrasonic nondestructive testing 2

Curved path planning based on 3D vision water immersion ultr...

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2nd International Conference on Computer Vision and Data Mining, ICVDM 2021

作者： Chen, Zheng Wang, Jian Zhang, Hualiang School of Information Engineering Shenyang University of Chemical Technology Liaoning Shenyang110142 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Liaoning Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning Shenyang110169 China

Compared with traditional 2D image processing, 3D point cloud processing has become a hot technology in the industry, but there are few researches applied to nondestructive testing of curved workpieces. This article aims at the non-destructive testing of curved workpieces, using 3D point cloud and robotic arm for water immersion ultrasonic nondestructive testing. Aiming at the 3D point cloud with many outliers, using the commonly used filter in two-dimensional images-the guide filter, the algorithm is improved and used for the filtering and downsampling of the 3D point cloud, and the adjustment of the curved surface workpiece and the robot arm's pose is introduced. Finally, the workpiece was scanned by a robotic arm with a water immersion ultrasonic nondestructive testing system to prove the feasibility of the experiment and improve the detection efficiency of traditional nondestructive testing technology. © 2021 institute of Physics Publishing. All rights reserved.

关键词： Robotic arms

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