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43rd Chinese control Conference, CCC 2024

作者： Yu, Wangchao Yang, Jun Su, Jinya Southeast University School of Automation Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Cse Ministry of Education Nanjing210096 China Loughborough University Department of Aeronautical and Automotive Engineering LeicestershireLE11 3TU United Kingdom

ISBN: (纸本)9789887581581

Simultaneous Localization and Mapping (SLAM) plays a vital role in ensuring port safety and efficiency by augmenting accurate, robust, and real-time perception. SLAM has been widely applied in automated guided vehicles in ports, there is still limited exploration into rubber tire gantry (RTG) crane applications. To address this gap, this paper introduces a robust SLAM framework for RTGs, leveraging a tightly coupled Iterated Error-State Kalman Filter to integrate multiple solid-state LiDARs with IMU and encoder measurements. The proposed approach, mainly encompassing preprocessing for sensor synchronization and a novel feature point based on LiDAR point cloud intensity, can significantly enhance system robustness. During prediction, the IMU first infers the system's state and de-skews the LiDAR point cloud with an initial estimate, which is then optimized using LiDAR observations in the update stage. Tested on extensive self-collected port datasets against the SOTA FAST-LIO2, the proposed method achieves considerable accuracy and robustness in RTG applications. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Gantry cranes

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Container Distance Estimation for Automated RTG via Improved SOLOv2 Instance Segmentation 43

Container Distance Estimation for Automated RTG via Improved...

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43rd Chinese control Conference, CCC 2024

作者： Yu, Zhengtao Yang, Jun Su, Jinya Southeast University School of Automation Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of CSE Ministry of Education Nanjing210096 China Loughborough University Department of Aeronautical and Automotive Engineering LoughboroughLE11 3TU United Kingdom

ISBN: (纸本)9789887581581

This study develops a simple real-time algorithm inferring the relative distance between containers stacked in operational yards and spreaders of Rubber Tire Gantry (RTG) in support of fully automated RTG operations in ports. The proposed scheme is based on SOLOv2 instance segmentation network with image captured by camera mounted on RTG driver's cab. Specifically, distance calculation is achieved by containers' surface recognition via instance segmentation and geometric information pertaining to the containers. To meet the dual demands of accuracy and reduced resource consumption, the original backbone network of SOLOv2, namely resnet18, is replaced by GhostNet. This substitution enables the extraction of multi-level feature maps for fusion, significantly reducing network complexity without sacrificing accuracy. The proposed algorithm is validated by comparative experiments on a self-curated real-life dataset, demonstrating a remarkable reduction of 91.6% in FLOPS and 55.7% in Params for the improved SOLOv2 framework. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Image segmentation

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MFANet: Mixed Feature Attention Network for Port Lane Detection 42

MFANet: Mixed Feature Attention Network for Port Lane Detect...

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42nd Chinese control Conference, CCC 2023

作者： Zhang, Jinwei Su, Jinya Yi, Dewei Yang, Jun Ministry of Education The School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China University of Aberdeen Department of Computing Science AberdeenAB24 3UE United Kingdom

ISBN: (纸本)9789887581543

With the advancement of industrial automation and artificial intelligence technology, unmanned port autonomy has gained increasing attention. Port automatic driving technology is a critical component of unmanned autonomous systems, enabling rubber tire gantry cranes (RTGs) to autonomously navigate by detecting port lanes and implementing control commands. In this study, a deep learning-based method is proposed for detecting port lanes in complex port scenes. The method employs the Inception module and attention mechanism to enhance lane feature extraction, and a structural loss is introduced to explicitly constrain the detection results and incorporate the prior characteristics of port lanes. A novel Mixed Feature Attention Network (MFANet) is proposed to implement the method for port lane detection. Experimental results demonstrate that MFANet effectively improves the accuracy of port lane detection while maintaining high computational efficiency. Furthermore, the performance of MFANet is evaluated in a real-world application scenario. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Computational efficiency

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Data Augmentation Integrated with Feature-Enhanced Convolutional Neural Network for Imbalanced Fault Diagnosis in Rolling Bearings 13

Data Augmentation Integrated with Feature-Enhanced Convoluti...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Wang, Li Qu, Qilin Wang, Yanwei Wong, David Shan-Hill Zheng, Ying School of Artificial Intelligence and Automation Huazhong University of Science and Technology China -Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China School of Mechanical & Electrical Engineering Wuhan Institute of Technology 693 Chuxiong Road Wuhan430073 China National Tsing-Hua University Department of Chemical Engineering Hsinchu30071 Taiwan

ISBN: (纸本)9798350361674

Imbalance in fault samples is a key problem with most rolling bearing datasets in real industrial processes, limiting the performance of fault detection. In addition, existing fault diagnosis methods for rolling bearings do not adopt a modular division between the initial layer and the classification layer. This lack of modularization inhibits the full activation of the network's feature extraction capabilities and impedes subsequent model improvements. In this paper, a novel fault diagnosis method based on data augmentation integrated with a feature-enhanced convolutional neural network (DA-FECNN) is proposed. First, Variational Autoencoder-Generative Adversarial Network (VAE-GAN) is utilized to expand the number of fault samples and improve the diversity of few-shot. Second, a uniform feature-enhanced structure is designed with a feature extraction module, a multi-scale classification module, and a reconstruction module. The network's ability on feature extraction is improved by focusing on high quality features. Thereby, accurate fault classification is realized simultaneously. Experiments on real world data show the effectiveness of the proposed method on fault diagnosis task. © 2024 IEEE.

关键词： Convolutional neural networks

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LiDAR-Inertial-Encoder Fusion for Real-Time Simultaneous Localization and Mapping of Rubber Tire Gantry Cranes

LiDAR-Inertial-Encoder Fusion for Real-Time Simultaneous Loc...

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第43届中国控制会议

作者： Wangchao Yu Jun Yang Jinya Su School of Automation Southeast University Key Laboratory of Measurement and Control of CSE Ministry of Education Southeast University Department of Aeronautical and Automotive Engineering Loughborough University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Simultaneous Localization and Mapping(SLAM) plays a vital role in ensuring port safety and efficiency by augmenting accurate, robust, and real-time perception. SLAM has been widely applied in automated guided vehicles in ports, there is still limited exploration into rubber tire gantry(RTG) crane applications. To address this gap, this paper introduces a robust SLAM framework for RTGs, leveraging a tightly coupled Iterated Error-State Kalman Filter to integrate multiple solid-state LiDARs with IMU and encoder measurements. The proposed approach, mainly encompassing preprocessing for sensor synchronization and a novel feature point based on LiDAR point cloud intensity, can significantly enhance system robustness. During prediction,the IMU first infers the system's state and de-skews the LiDAR point cloud with an initial estimate, which is then optimized using Li DAR observations in the update stage. Tested on extensive self-collected port datasets against the SOTA FAST-LIO2,the proposed method achieves considerable accuracy and robustness in RTG applications.

关键词： SLAM Multi-LiDARs Multi-sensor fusion Rubber Tire Gantry Cranes

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Container Distance Estimation for Automated RTG via Improved SOLOv2 Instance Segmentation

Container Distance Estimation for Automated RTG via Improved...

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第43届中国控制会议

作者： Zhengtao Yu Jun Yang Jinya Su School of Automation Southeast University Key Laboratory of Measurement and Control of CSE Ministry of Education Southeast University Department of Aeronautical and Automotive Engineering Loughborough University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This study develops a simple real-time algorithm inferring the relative distance between containers stacked in operational yards and spreaders of Rubber Tire Gantry(RTG) in support of fully automated RTG operations in ports. The proposed scheme is based on SOLOv2 instance segmentation network with image captured by camera mounted on RTG driver's ***, distance calculation is achieved by containers' surface recognition via instance segmentation and geometric information pertaining to the containers. To meet the dual demands of accuracy and reduced resource consumption, the original backbone network of SOLOv2, namely resnet18, is replaced by GhostNet. This substitution enables the extraction of multi-level feature maps for fusion, significantly reducing network complexity without sacrificing accuracy. The proposed algorithm is validated by comparative experiments on a self-curated real-life dataset, demonstrating a remarkable reduction of 91.6% in FLOPS and 55.7% in Params for the improved SOLOv2 framework.

关键词： Distance Estimation Instance Segmentation Rubber Tire Gantry Network lightweight

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A Two-layer Adaptive control Strategy for Photovoltaic Power in Different Scenarios 3

A Two-layer Adaptive Control Strategy for Photovoltaic Power...

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3rd International Conference on Energy engineering and Power Systems, EEPS 2023

作者： Wan, Yanghui Yan, Jiahao Chu, Tianfeng Li, Yaping Yuan, Kun Zhang, Kaifeng Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Nanjing China Power Automation Department Nanjing China State Grid Liaoning Electric Power Co. Ltd. Electric Power Research Institute Shenyang China

ISBN: (纸本)9798350313857

Currently, control strategies for distributed photovoltaic (PV) power generation are relatively single and rigid, and cannot flexibly adapt to the diverse demands of power grids in different scenarios. Firstly, this paper analyzes the adaptive relationship between different dispatching demands, the regulation ability of distributed PV, and the characteristics of the local voltage. Secondly, for different scenarios, the coordinated controller determines the reference values of active power and reactive power given to the inverter. Thus, distributed PV power control modes which can adapt to different scenarios have been established. Then, these control modes can be classified into single control modes and compound control modes according to the complexity of scenario demands. On this basis, a specific two-layer adaptive control strategy and process of distributed PV power generation in a low-voltage distribution network are further proposed, which takes into account both external demands of the distribution network and the regulation ability of distributed PV. Finally, a case study is given to verify the rationality and effectiveness of the two-layer adaptive scenarios control strategy. This strategy can effectively improve the comprehensive support ability of distributed PV power generation towards the power grid in multiple scenarios, and improve the stable operation level of the regional power grid. © 2023 IEEE.

关键词： Reactive power

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A Semi-Supervised Method Using Cycle Consistency and Multi-Perspective Dilated for Sar-to-Optical Translation

SSRN

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SSRN 2024年

作者： Wang, Jinyu Yang, Haitao Liu, Zhengjun Chen, Hang Department of Space Equipment The Academy of Equipment Beijing101416 China Department of Automation Measurement and Control Harbin Institute of Technology Harbin150001 China Space Engineering University China

Synthetic Aperture Radar (SAR) is a powerful imaging sensor capable of operating in any weather and at any time, despite its image interpretation poses challenges. Translating SAR images to optical (S2O) can serve as a beneficial supplement for target detection, image alignment, and image fusion. The existing S2O translation employs solely unsupervised or supervised techniques. However, the supervised method requires a substantial quantity of aligned data support, while the unsupervised method fails to guarantee the quality of image generation. This research presents a novel semi-supervised S2O generating approach that can obtain domain-level and pixel-level mapping relationships. A dual-branch cycle consistency loss has been developed to independently constrain supervised and unsupervised modules. To improve global and sparsity feature extraction, the generator combines sliding dilated convolution with multi-respective parallel branching. Using just 1100 pairs of training sets, extensive experiments have demonstrated that the suggested strategy outperforms baseline methods in both subjective and objective standards. Where FID is optimized by 23.41%, 49.25%, 48.57%, 16.73%, and 42.32% on the five land classes. © 2024, The Authors. All rights reserved.

关键词： Radar imaging

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Finite-time coordinated path-following control of leader-following multi-agent systems

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Frontiers of Information Technology & Electronic engineering 2022年第10期23卷 1511-1521页

作者： Weibin CHEN Yangyang CHEN Ya ZHANG School of Automation Southeast UniversityNanjing 210096China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast UniversityNanjing 210096China Robot Department Jiangsu Automation Research InstituteLianyungang 222061China

This paper presents applications of the continuous feedback method to achieve path-following and a formation moving along the desired orbits within a finite *** is assumed that the topology for the virtual leader and followers is *** additional condition of the so-called barrier function is designed to make all agents move within a limited area.A novel continuous finite-time path-following control law is first designed based on the barrier function and *** a novel continuous finite-time formation algorithm is designed by regarding the path-following errors as *** settling-time properties of the resulting system are studied in detail and simulations are presented to validate the proposed strategies.

关键词： Finite-time Coordinated path-following Multi-agent systems Barrier function

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FlowDSnet: An End-to-End Scene Flow Based LiDAR De-Skewing Network

FlowDSnet: An End-to-End Scene Flow Based LiDAR De-Skewing N...

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2023 China automation Congress, CAC 2023

作者： Wu, Xinrui Zhu, Yixiang Wang, Siyi Yu, Zhenjun Wang, Guangming Wang, Hesheng Shanghai Jiao Tong University Department of Automation Shanghai China Shanghai Jiao Tong University Department of Electrical Engineering Shanghai China Shanghai Jiao Tong University Department of Measurement Control Technology Shanghai China SJTU-ParisTech Elite Institude of Technology Shanghai Jiao Tong University Shanghai China University of Cambridge Department of Engineering CambridgeCB2 1PZ United Kingdom

ISBN: (纸本)9798350303759

The motion distortion of LiDAR is the phenomenon caused by the movement of the LiDAR carrier. Such phenomenon makes the LiDAR point cloud information unreliable and seriously degrades the tasks that rely on LiDAR such as 3D object perception, localization, and mapping. In this work, a novel end-to-end network named FlowDSnet is proposed to compensate for the distortion of LiDAR point clouds in an end-to-end way. The proposed pipeline takes the original adjacent two frames of point clouds as input and recovers the undistorted coordinates of each point by predicting the scene flow in 3D space. Different from the traditional distortion removal algorithm, our method does not rely on the information of other sensors, such as the Inertial measurement unit (IMU), but applies a novel flow embedding algorithm combined with spatiotemporal interpolation to conduct distortion correction. And the input is two adjacent frames of raw LiDAR measurement. Besides, both qualitative and quantitative experiments on automative driving datasets are conducted to prove the effectiveness of the proposed pipeline. The experimental results show that this network can accomplish the LiDAR de-skewing task well, especially in a scene with many dynamic objects. © 2023 IEEE.

关键词： Pipelines

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