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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhou, Linqi Chen, Zhihua Liu, Jun Liu, Zhi Chen, Yumeng Zhang, Liting Nanchang Hangkong University Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Moe Key Lab of Nondestructive Testing Technology Nanchang330063 China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing100081 China

ISBN: (纸本)9798350307320

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot's foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper. © 2023 IEEE.

关键词： Robot programming

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Enabling Multi-scale Features and Double-Attention for Single-image 3D Urban Reconstruction

Enabling Multi-scale Features and Double-Attention for Singl...

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2021 China Automation Congress, CAC 2021

作者： Miao, Jun Shen, Ji Jiang, Ruixiang Chu, Jun Guo, Luanzheng The School of Aeronautical Manufacturing Engineering Nanchang Hangkong University Nanchang330063 China The Department of Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang330063 China The Pacific Northwest National Laboratory WA99354 United States

ISBN: (纸本)9781665426473

3D reconstruction from a single RGB image for urban scenes has been a foundation for safety-critical applications such as autonomous driving and city planning. It is essential to develop 3D reconstruction models that are accurate for these applications. The most recent single-image 3D reconstruction models leverage representative neural networks to address the problem and have achieved promising results. However, these approaches cannot correctly calculate the segmentation map and reconstruct depth for ambiguous scene regions and when scene information gets lost going through down-sampling layers. To solve this problem, we propose EMERALD, an end-to-end single-image 3D reconstruction framework, in which we empower the fully convolutional networks (FCN) with multi-scale contextual features and a novel double-attention mechanism to advance its discrimination capability on ambiguous scene regions and loss of scene details. The evaluation results on two leading urban- scene data sets, Cityscapes (Segmentation Covering (SC)1 of 61.2%) and SYNTHIA (SC of 81.9%), demonstrate the optimized network architecture's effectiveness. © 2021 IEEE

关键词： Network architecture

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Room Layout Estimation based on Planar Semantic Segmentation

Room Layout Estimation based on Planar Semantic Segmentation

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2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022

作者： Huang, Shizhong Miao, Jun Yao, Hui Zheng, Yilin Chu, Jun Zhang, Guoxiang Nanchang Hangkong University School of Aeronautical Manufacturing Engineering Nanchang330063 China Nanchang Hangkong University Department of Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang330063 China University of California MercedCA95343 United States

ISBN: (数字)9781665479882

ISBN: (纸本)9781509050017

Layout estimation is one of fundamental tasks for understanding indoor scenes. In a single image of indoor scenes, key information, such as key points and boundaries for inferring the layout is often severely occluded or completely invisible. In this paper, a novel end-to-end deep learning network, which is extended from Xception module, is constructed for segmentation of semantic planes to infer the room layout structure. Moreover, the network contains an optimized depth wise separable convolution (DSC) in Xception module and a reinforced spatial attention module (SAM), so that it obtained good learning ability in severely occluded areas. Tests on public datasets show that the proposed network has a lower pixel error (PE) and higher generalization compared with existing algorithms. Ablation experiments prove that optimized DSC and SAM in the network are effective in improving the accuracy of layout estimation. © 2022 IEEE.

关键词： Semantic Segmentation

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Improved global optical flow estimation with mean shift algorithm for target detection

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International Journal of Wireless and Mobile Computing 2015年第4期8卷 323-328页

作者： Wang, Zhonghua Li, Chunyong Liu, Qingping Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang Jiangxi Province330063 China School of Information and Engineering Nanchang Hangkong University Nanchang Jiangxi Province330063 China

In view of the problem that the global optical flow algorithm cannot acquire accurate motion parameter estimation at a low-gradient value, an improved method has been presented in order to enhance the self-adaptive ability. The proposed method is divided into three steps: firstly, the Gaussian filter is used to restrain the background noise;secondly, the optical basic constraint weighted function is modified to acquire targets area;finally, the morphological filtering and mean shift segmentation algorithm is adopted to reduce the false alarm rate. Compared with other algorithms, the experiment results show that the proposed method can improve the self-adaptive ability and the detection rate. © 2015 Inderscience Enterprises Ltd.

关键词： Optical flows

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An Improved Embedding Model for Zero-Shot Classification based on Attention Mechanism

An Improved Embedding Model for Zero-Shot Classification bas...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Zongfeng Wu Qishen Li Xiao Yu Xu Peng School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

It is well-known that the auxiliary information plays a key role in zero-shot classification. However, most of the existing popular methods do not make effective use of auxiliary information. To address this issue, we propose an improved embedding model for zero-shot classification based on attention mechanism, called EMAM. In the proposed EMAM, we first add an attention mechanism to effectively extract the key information of auxiliary information in zero-shot classification. Then optimizes the objective function to improve the recognition rate of this model. Finally the experimental comparison is implemented on the standard zero-shot learning datasets. The experimental results demonstrate that our proposed EMAM not only verifies its validity, but also achieves good results.

关键词： Automation Conferences Linear programming Data mining Information technology Standards

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Human Pose Estimation Based on Multi-resolution Feature Fusion Network

Human Pose Estimation Based on Multi-resolution Feature Fusi...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Weibai Duan Qishen Li Sihao Yuan Xiao Yu School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

This article focuses on the study of multi-resolution feature fusion methods for human pose estimation. Most of the existing pose estimation methods are based on high-resolution gradually reducing the resolution to learn advanced semantic features, and then gradually recovering high-resolution features from the low-resolution semantic features to locate keypoints of the person instance. The process without fusion of rich features will inevitably lose more spatial and semantic information. The purpose of the multi-resolution feature fusion method proposed in this paper is to fuse spatial and semantic information on multiple scale feature maps, in addition, our network use the multi-receptive field fusion module on the same scale features to enhance feature extraction. Our methods can utilize more feature information to accurately locate keypoints.

关键词： Fuses Semantics Pose estimation Feature extraction Spatial resolution Kernel Information technology

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A Control Method for Electric Cylinder Based on Three-Order ADRC

A Control Method for Electric Cylinder Based on Three-Order ...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Zhifan Jiang Zhihua Chen Lijun Tong Jiale Huang Yufei Che Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology Nanchang Hangkong University Nanchang

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

As an executive component of robots, the precise control of electric cylinders is crucial for achieving accurate control of robots. This article proposes a position closed-loop electric cylinder control method based on three-orders active disturbance rejection control (ADRC). Firstly, model the electric cylinder system and determine its transfer function. Secondly, PID controllers and ADRC controllers were designed separately, and their stability was verified through the Routh criterion and Lyapunov method, respectively. Finally, the control effects of PID and ADRC were compared through three sets of experiments. The results show that compared to PID, ADRC is less affected by the increase in input signal frequency, has stronger anti-interference ability, and can effectively resist the influence of internal uncertainty.

关键词： Robust control Automatic frequency control PI control Uncertainty Stability criteria Transfer functions Resists PD control Robots Lyapunov methods

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Scene Text Detection with Feature Aggregation and Receptive Field Enhancement

Scene Text Detection with Feature Aggregation and Receptive ...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Qin Tang Qishen Li Ruirui Wang Yanming Lai School of Information Engineering Nanchang Hangkong University Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

We propose a scene text detection with feature aggregation and receptive field enhancement. It mainly improves the problem of wrong text segmentation. Specifically, a Feature Aggregation and Receptive Field Enhancement Module (FARE) adopts a ladder structure and constructs multi-scale feature aggregation with multiple branches to enhance the feature representation ability of texts of different scales. We add dilated convolution to expand the range of receptive field, which increases the detection of large-scale texts in low-level feature and obtains more accurate position information. Extensive experiments on the ICDAR 2015, ICDAR 2017 MLT, and CTW1500 show that the proposed method effectively improves the detection performance of the network. Notably, our proposed method has detected a precision of 87.4% on the curved text dataset CTW1500.

关键词： Automation Convolution Fuses Conferences Benchmark testing Feature extraction Information technology

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Prototype-guided Unsupervised Domain Adaptation for Semantic Segmentation

Prototype-guided Unsupervised Domain Adaptation for Semantic...

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Control, Electronics and Computer Technology (ICCECT), IEEE International Conference on

作者： Shaoqi Xia Hua Huang Qishen Li Yifan He Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition School of Information Engineering Nanchang Hangkong University Nanchang China School of Information Engineering Nanchang Hangkong University Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition School of Software Nanchang Hangkong University Nanchang China

Semantic segmentation is one of the most important research directions in the field of computer vision, and has a wide range of applications for autonomous driving, medical imaging, intelligent security, etc. Unsupervised domain adaptation is the mainstream research topic in recent years, which can use a large number of labeled source samples to complete the segmentation task in target domain without labeled target samples. In this paper, we propose a prototype-guided unsupervised domain adaptation for semantic segmentation based on ProDA model. Due to lacking of labeled target samples and the prior probability, a prototype distance loss based on target domain is proposed to optimize the distribution of features by measuring the distance between features and the updated prototype and designing an adaptive threshold strategy. Meanwhile, a smoothing loss is proposed to alleviate the impact of source samples on our model and improve the prediction performance of the network. By conducting experiments on the GTA5 to Cityscapes scenarios, the results show that compared with the original model, the loss optimization improves mIoU by1.52.

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Motion Planning and Tracking Control Method Based on CoppeliaSim for UGV in Irregular Terrain

Motion Planning and Tracking Control Method Based on Coppeli...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Jiale Huang Zhihua Chen Yongbo Zhong Wenrui Zeng Chuanmin Ji key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology Nanchang Hangkong University Nanchang China

Path planning and tracking algorithms are one of the cores of autonomous navigation technology for unmanned ground vehicle (UGV). In this paper, a local obstacle avoidance method based on event-triggered control is proposed to ensure that the UGV avoids obstacles in the tracking process. First, the path planning and tracking control problem of the UGV is modeled and formulated. Second, the improvement and logical correlation of the relevant algorithms are described in detail. Finally, ajoint simulation of MATLAB and V-rep is performed. The simulation results show that event-triggered control can largely improve the reliability of the UGV path planning and tracking process, and verify the effectiveness of the algorithm.

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