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A High-Precision Edge Detection Algorithm Based on Improved Sobel Operator-Assisted HED 14

A High-Precision Edge Detection Algorithm Based on Improved ...

引用

14th IEEE International Conference on Cyber Technology in automation, Control, and Intelligent Systems, CYBER 2024

作者： Yuan, Yeborui Chen, Wenyuan Tang, Jie Yang, Jia School of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang State Key Laboratory of Robotics 110016 China University of Chinese Academy of Sciences Beijing100049 China School of Electrical Engineering and Automation Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin300384 China

ISBN: (纸本)9798331506056

In response to the problem of poor detection performance of the traditional Sobel operator in edge detection, a high-precision edge detection algorithm based on Sobel operator-assisted Holistically-nested Edge Detection (HED) network was proposed, which is named ISAHED here. The algorithm is designed as follows. Firstly, an improved Sobel operator based on the traditional Sobel operator was proposed through adding gradient direction. Then, the image output by the improved Sobel operator is taken as the ground-truth image and as the end-to-end comparison object in a HED-like network. Finally, the ISAHED algorithm outputs edge images with better quality through iterative learning of the HED-like neural network. The experimental results prove that the proposed ISAHED algorithm has better edge detection performance by visually observing the final edge images and further evaluating the values of FOM. This work provides an important reference for the further development of the edge detection technology. © 2024 IEEE.

关键词： Contrastive Learning

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A deep learning model-based framework for abnormal state monitoring of blast furnace heat loads 14

A deep learning model-based framework for abnormal state mon...

引用

14th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2024

作者： Xu, Hong-Wei Sun, Yan-Ning Liu, Li-Lan Department of Mechanical and Automation Engineering Shanghai University Shanghai200444 China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200444 China

ISBN: (纸本)9781837242672

Heat load is a pivotal metric for ensuring the safety of blast furnace equipment in the iron and steel smelting industry, crucial for monitoring the heat exchange process of blast furnace cooling walls and evaluating the erosion of furnace bodies. The variability of blast furnace heat load, influenced by factors such as feed and oxygen supply, often results in abnormal internal furnace temperatures and drastic heat load fluctuations, hindering effective preventive maintenance. This study introduces a deep-learning-based framework for monitoring the abnormal state of blast furnace heat load. Initially, a two-stage data preprocessing method for monitoring ironmaking parameters is proposed: the first stage eliminates missing and abnormal values from the monitoring parameters, while the second stage employs a feature selection model to identify key influencing parameters. Subsequently, a real-time prediction model of blast furnace heat load is designed, integrating one-dimensional convolutional neural networks (1DCNN) with bidirectional long short-term memory (BiLSTM) networks. This model uses a two-layer 1DCNN for automatic feature extraction from time series data, complemented by a single-layer BiLSTM network to accurately predict anomalous trends in heat load. This deep learning framework has demonstrated superior performance in prediction accuracy and abnormal trend identification compared to alternative models. © The Institution of Engineering & Technology 2024.

关键词： Iron and steel industry

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Fixed-time consensus control for heterogeneous multi-agent systems without velocity measurements of neighbors 19th

Fixed-time consensus control for heterogeneous multi-agent s...

引用

19th Chinese Intelligent Systems Conference, CISC 2023

作者： Ao, Yichao Zhang, Qifeng Guangzhou511458 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9789819968817

This paper investigates fixed-time consensus control problems for heterogeneous multi-agent systems, which consist of single and double integrators, without using the measurements of neighbors’ velocities. Distributed control protocols are designed for single and double integrators, respectively, based on the ideas of backstepping approach and adding a power integrator method. The convergence performance of the closed-loop systems is analyzed by using Lyapunov theory and fixed-time stability theory. Moreover, numerical examples are provided to show the effectiveness of our results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Multi agent systems

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Periodic orbits of solar sails in the Sun-Earth system

Periodic orbits of solar sails in the Sun-Earth system

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2024 International Conference on Aerospace and Mechanics, ICAM 2024

作者： Liu, Lei Zhang, Xin Stata Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

This paper studies the periodic orbits of solar sails using reflectivity control devices (RCDs) in the circular Sun-Earth system. Significantly, the dynamical equations about the solar sail are given separately in the rotational coordinate system and adjoint coordinate system. Analytical solutions about the periodic orbits of solar sails are given. So as to gain the periodic orbits of solar sails, we discuss the conditions of the attitude angles and reflectivity modulation ratio of solar sails. This paper simulates some periodic orbits of solar sails and analyzes the stability of a periodic orbit of a solar sail by using the Floquet theory. The outcomes show that it is unstable for the periodic orbit of a solar sail. Based on the linear quadratic regulator (LQR), this paper designs a control algorithm to achieve tracking in the target orbit of solar sails. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Solar sails

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Innovative Design and Validation of an Ultrasound Examination Robot's End Effector with Passive Force Limitation Mechanism 14

Innovative Design and Validation of an Ultrasound Examinatio...

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14th IEEE International Conference on Cyber Technology in automation, Control, and Intelligent Systems, CYBER 2024

作者： Gao, Ziqi Luo, Yang Jiang, Yunxiang Zhao, Xingang Zhao, Yiwen Li, Ning Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China University of the Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798331506056

In the field of medical automation, the safety of ultrasound examination robots is crucial for patient experience. Although traditional sensor-based force feedback systems are effective, they carry potential risks of failure. This study proposes an innovative passive maximum force-limited end effector (MFLEE), which relies on a physical mechanism instead of electronic sensing systems to limit force, thereby enhancing system reliability and safety. Initially, a detailed mechanical design was developed, incorporating a spring-ball limiting mechanism, and optimal parameters were selected based on theoretical analysis. Finite element analysis was then utilized to simulate the design, yielding force limits at various operational angles. In the experimental phase, actual measurements were compared with simulation results, confirming the efficacy of the MFLEE in practical applications. The results showed that the actual measured maximum force limit deviated by only 0.5N from theoretical predictions, indicating that the design could reliably control force output, offering a new safety assurance solution for ultrasound examination robots. © 2024 IEEE.

关键词： End effectors

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Uni-YOLO: Vision-Language Model-Guided YOLO for Robust and Fast Universal Detection in the Open World 24

Uni-YOLO: Vision-Language Model-Guided YOLO for Robust and F...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Xudong Ren, Weihong Chen, Xi'Ai Fan, Huijie Tang, Yandong Han, Zhi State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Shenzhen China

ISBN: (纸本)9798400706868

Universal object detectors aim to detect any object in any scene without human annotation, exhibiting superior generalization. However, the current universal object detectors show degraded performance in harsh weather, and their insufficient real-time capabilities limit their application. In this paper, we present Uni-YOLO, a universal detector designed for complex scenes with real-time performance. Uni-YOLO is a one-stage object detector that uses general object confidence to distinguish between objects and backgrounds, and employs a grid cell regression method for real-time detection. To improve its robustness in harsh weather conditions, the input of Uni-YOLO is adaptively enhanced with a physical model-based enhancement module. During training and inference, Uni-YOLO is guided by the extensive knowledge of the vision-language model CLIP. An object augmentation method is proposed to improve generalization in training by utilizing multiple source datasets with heterogeneous annotations. Furthermore, an online self-enhancement method is proposed to allow Uni-YOLO to further focus on specific objects through self-supervised fine-tuning in a given scene. Extensive experiments on public benchmarks and a UAV deployment are conducted to validate its superiority and practical value. © 2024 Owner/Author.

关键词： Zero-shot learning

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Garbage classification recognition based on improved YOLOv8 model 2

Garbage classification recognition based on improved YOLOv8 ...

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2nd International Conference on Signal Processing and Intelligent Computing, SPIC 2024

作者： Qin, Lijuan Tang, Xiaoyu Zhang, Feng Shenyang Ligong University School of Information Science and Engineering Shenyang China Chinese Academy of Science State Key Laboratory of Robotics Shenyang Institute of Automation China

ISBN: (纸本)9798350368888

The use of intelligent garbage classification methods and devices for waste sorting is set to become the future trend in waste management. With the development of high-precision image recognition algorithms, research in intelligent garbage classification has gradually matured. The core lies in utilizing image recognition technology to analyze garbage image information, thereby surpassing the limitations of manual sorting and achieving higher standards of speed and accuracy. This paper proposes a target detection scheme based on the introduction of the C2f_RepGhost module into YOLOv8, aiming to construct a model system for efficient garbage identification and classification. Thanks to its streamlined design and real-time response characteristics, this model can operate efficiently on platforms such as smart mobile devices, enabling real-time intelligent automatic classification of various types of waste. © 2024 IEEE.

关键词： Data Augmentation Garbage classification YOLOv8 Improvements

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Full-state Physical Mirroring Method of Space Manipulator Based on the Multiple Decoupling Principles 2

Full-state Physical Mirroring Method of Space Manipulator Ba...

引用

2nd International Conference on Artificial Intelligence, Human-Computer Interaction and robotics, AIHCIR 2023

作者： Yang, Tao Qin, Shize Xu, Fang Deng, Songbo Zhang, Hao Shi, Yan Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing University of Chinese Academy of Sciences Beijing Institute of Precision Mechatronics Controls Laboratory of Aerospace Servo Actuation and Transmission State Key Laboratory of Robotics Chinese Academy of Sciences Beijing China Beijing Institute of Precision Mechatronics and Controls Laboratory of Aerospace Servo Actuation and Transmission PR Beijing China Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics PR Shenyang China

ISBN: (纸本)9798350360363

To achieve the goal of an efficient full-state ground test of a 7DOF space manipulator, the kinematic and dynamical coupling characteristics of space manipulator are investigated, and a mirror test method based on the principle of multiple decoupling is proposed to solve the problem of highly dynamic three-dimensional motion conditions of space manipulator that are difficult to verify by ground simulation. It firstly decouples the space manipulator into a 3dof unit,1dof unit and another 3dof unit combination. It achieves joint load reduction through inter-joint dynamics coupling. Afterwards, it designs an air-floating support device to realize the joint space 7DOF motion, and proposes a constant damping joint dynamics compensation control algorithm to eliminate the dynamics disturbances introduced by inter-joint decoupling. Then, an industrial mirror robot arm with consistent kinematic parameters and a highly dynamic mirror control algorithm are designed to map the joint motion states of the space manipulator to the ground robotic arm, and a ground simulation of the Cartesian space motion state of the space robot arm is realized by decoupling the joint space from the Cartesian space motion. Finally, the ground experiment of target grasping based on the physical mirroring method is designed to realize the high-speed full-state ground experiment verification of complex tasks of space manipulator. © 2023 IEEE.

关键词： Robotic arms

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A Three-Stage Low-Illumination Image Enhancement Method Based on Feature Refining and Its Application in Inspection Robot for High-Voltage Substation Room 8

A Three-Stage Low-Illumination Image Enhancement Method Base...

引用

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

作者： Fu, Bo Wang, Hongguang Jiang, Yong Song, Yifeng Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9798350300178

To address color deviation and noise problem in some Retinex theory based image enhancement methods, this paper proposes a three-stage low-illumination image enhancement method based on feature refining. Additionally, a vision subsystem for an electric inspection robot is introduced based on this network to mitigate the impact of low illumination environments on vision detection tasks in high-voltage substation rooms. In the first stage, the proposed network decomposes the image using Retinex theory and leverages the Swin Transformer to perceive global information. In the second stage, the image undergoes preliminary enhancement. In the third stage, the feature refining network is introduced. Initially, a U-shaped network is employed to extract image features. Subsequently, a convolutional neural network and attention mechanism are applied to screen the extracted information, completing the feature refinement and improving the quality of the result. Through illumination assessment and algorithm selection, the proposed vision subsystem for the robot effectively addresses the challenges of low illumination enhancement and object detection in complex lighting conditions in high-voltage transformer rooms. Experimental results demonstrate that the proposed network outperforms some state-of-the-art methods in terms of peak signal-to-noise ratio and structural similarity index. Moreover, the vision subsystem presented in this paper significantly enhances the robot's detection capabilities, reducing the omission ratio and fallout ratio, thus validating the effectiveness of the proposed method. © 2023 IEEE.

关键词： Image enhancement

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Visual Guidance of Aerial Vehicle for Target Recognition and Pose Estimation 13

Visual Guidance of Aerial Vehicle for Target Recognition and...

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13th IEEE International Conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2023

作者： Zhang, Zhaoyang Zhang, Guangyu Yang, Liying He, Yuqing Li, Siliang Northeastern University Shenyang110819 China Shenyang Institute of Automation CAS State Key Laboratory of Robotics Shenyang110016 China

ISBN: (纸本)9798350315196

This paper proposes a deep learning-based target pose estimation method for aerial vehicle, a robotic system combining a rotary-wing UAV and a robotic arm, which is difficult to accurately acquire the pose of the target when performing tasks in the field environment. Firstly, in the offline stage, the YOLOv5 model is improved by adding angle prediction branches to make it capable of pose estimation;secondly, the offline model is used to complete target recognition, target segmentation and pose estimation in combination with online images, and the online visual point cloud is combined with statistical filtering to complete the position estimation of targets;Finally, the effectiveness and feasibility of this visual guidance method is verified through indoor static experiments and outdoor flight experiments, and this method successfully guided the aerial vehicle to complete the torch docking task. © 2023 IEEE.

关键词： Image segmentation

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