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检索条件"机构=Key Laboratory of Industrial Automation Control Technology and Information Processing"

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Lidar-Inertial and Downward Vision Based Mobile Robot SLAM in Digital Workshops 13

Lidar-Inertial and Downward Vision Based Mobile Robot SLAM i...

引用

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

作者： Zhang, Yanchao Geng, Hao Liu, Bin Zhang, Yinlong Yuan, Xudong Zhang, Sichao School of Information Science and Engineering Shenyang University of Technology Shenyang110020 China Guangzhou Institute of Industrial Intelligence Guangzhou511458 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9798350361674

In order to solve the problems of long-term cumulative error and the absence of absolute pose in the existing Lidar-Inertial Measurement Unit (IMU) fusion methods for Automated Guided Vehicle (AGV) Simultaneous Localization and Mapping (SLAM), we propose a new SLAM method for mobile robot, integrating Lidar, IMU, and a downward vision camera. Firstly, we tightly couple Lidar-IMU and downward vision camera using a multimodal fusion framework. Downward visual measurements are integrated as QR factors into the factor graph, providing absolute pose information to correct cumulative error in the Lidar-IMU system and enable the joint pose estimation of the AGV. Secondly, an enhanced Iterative Closest Point (ICP) point cloud matching algorithm has been developed to correct the incorrect pose. This approach achieves Scan-to-Map alignment by employing a coarse-to-fine matching strategy, thereby enhancing the accuracy of point cloud alignment. Finally, the extended Kalman filter is used to optimize the Lidar pose drift, as well as the Lidar mapping effectively by using the Lidar-IMU as the measurements and the downward visual measurements as the updates. The algorithm has been extensively evaluated on the developed AGV experimental platform. The experimental results show that the position error is less than 0.02 meters and the orientation error is less than 1°, which solves the problems of long-term error accumulation and the lack of absolute pose information for AGV navigation. © 2024 IEEE.

关键词： Extended Kalman filters

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Early Forest Fire Recognition Method Based on C-GhostNet Network 5

Early Forest Fire Recognition Method Based on C-GhostNet Net...

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5th International Symposium on Autonomous Systems, ISAS 2022

作者： Li, Mengna Zhang, Youmin Mu, Lingxia Xin, Jing Yu, Ziquan Jiao, Shangbin Liu, Han Xie, Guo Yi, Yingmin Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Shaanxi Xi'an710048 China Concordia University Industrial and Aerospace Engineering Department of Mechanical MontrealQCH3G 1M8 Canada Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing211106 China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an Shaanxi 710048 China

ISBN: (纸本)9781665487085

Early forest fire image recognition plays an important role in timely fire fighting. This paper proposes an early forest fire recognition method based on C-GhostNet network. The C-GhostNet network is an improved version of GhostNet, which mainly consists of a series of c-ghost bottlenecks, each c-ghost bottleneck mainly consists of two c-ghost modules. Through the concat operation and channel shuffle, the c-ghost module establishes the relationship between the input data and the output features generated by the original ghost module, so that GhostNet network has the ability to capture contextual features. The experimental results show that the C-GhostNet network achieves better recognition performance than GhostNet without introducing additional computational load, and the running speed reaches about 20FPS, which basically realizes real-time recognition. In addition, like GhostN et, the C-GhostNet network is also a lightweight network. © 2022 IEEE.

关键词： Image recognition

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Triggered Mechanism for Strict Feedback Systems Under Unknown control Directions

Triggered Mechanism for Strict Feedback Systems Under Unknow...

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Asian control Conference

作者： Guilong Liu Hongda Wu Yongliang Yang Wei Xie Weidong Zhang School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper presents an event-based asymptotic tracking control scheme designed for strict feedback systems with unknown control directions. The control directions directly determine how the controller acts on the system, thus impacting the system's stability and convergence speed. When the control directions are unknown, it presents a significant challenge in controller design. To tackle this issue, the Nussbaum-type gains are introduced. However, this method increases communication overhead. Hence, an event-triggered mechanism is introduced to alleviate this burden. Additionally, to achieve and maintain desired target states, satisfying control requirements and performance demands, this paper improves the event-triggered mechanism to achieve asymptotic tracking. Finally, the feasibility and effectiveness of the proposed design are validated through a simulation example.

关键词： Asymptotic stability Target tracking Stability criteria control systems Optimization Convergence

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LI-DPS: A Long Sequence Dual Prediction Scheme Based on Informer for Efficient High-Frequency Data Transmission

LI-DPS: A Long Sequence Dual Prediction Scheme Based on Info...

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Annual Conference of industrial Electronics Society

作者： Yu Wu Bo Yang Dafeng Zhu Cailian Chen Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai China

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

High-frequency time-series data such as vibration signals will consume a lot of communication resources and require very high network bandwidth. Reducing the amount of data transmission while ensuring its availability is particularly important and challenging in high-frequency scenarios. Dual prediction scheme (DPS) can significantly reduce the amount of data transmission while ensuring data accuracy. However, traditional DPS cannot be applied to high-frequency scenarios due to the limitation of the mechanism design and model inference speed. We propose a novel long sequence DPS to efficiently reduce the volume of high-frequency data transmission in real time. To overcome the accuracy degradation due to gradient vanishing in long sequence prediction, we also propose an attention-based prediction model. Furthermore, we propose Informer to reduce the model’s computational complexity and enable fast inference and deployment on resource-constrained edge gateways. Experiments based on the real-world dataset show that the proposed scheme can efficiently cope with high-frequency data, reduce its transmission volume, and ensure data accuracy.

关键词： Vibrations industrial electronics Accuracy Computational modeling Mechanism design Predictive models Logic gates Real-time systems Data communication industrial Internet of Things

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Offline Reinforcement Learning via Sequence Modeling for Vision-Based Robotic Grasping

Offline Reinforcement Learning via Sequence Modeling for Vis...

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IEEE International Conference on Cyber technology in automation, control, and Intelligent Systems

作者： Sheng Zeng Xiao Sun Kang Chen Weibing Huang Yi Chen Dan Chen Zhezhuang Xu Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing College of Electrical Engineering and Automation Fuzhou University Fuzhou China Evomotion Co. Ltd. Shenzhen Guangdong China

High cost of environmental interaction and low data efficiency limit the development of reinforcement learning in robotic grasping. This paper proposes an end-to-end robotic grasping method based on offline reinforcement learning via sequence modeling. It considers the most recent n-step history to assist the agent in making decisions, where a predictive model learns to directly predict actions from raw image inputs. The experimental results show that our method can achieve higher grasping success rate with less training data than traditional reinforcement learning algorithms in offline setting.

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Modeling and Heuristic Solutions for Storage Scheduling of Steel Plates in Steel Logistics Park

Modeling and Heuristic Solutions for Storage Scheduling of S...

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Chinese automation Congress (CAC)

作者： Hao Yang Jinlong Wang Zhezhuang Xu Boyu Chen Qingdong Zhang Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing College of Electrical Engineering and Automation Fuzhou University Fuzhou China Sansteel Minguang Co. Ltd. Sanming China

Steel plates are highly customized according to different customer demands. In this case, the outbound date, the specifications of the steel plates and the distribution of stacks must be simultaneously considered when the steel plates are stored in the yard. Improper storage of steel plates significantly increases the number of shuffles during outbound operations. To overcome the challenge, we propose an efficient steel plate storage scheme based on the actual scenario of existing steel plate distribution in the yard. Firstly, a model for steel plate storage scheduling is established to minimize the number of extra blocking plates. Then, three heuristic algorithms are designed to solve the problem. Comparative experiments are carried out to verify the effectiveness of the proposed algorithm. The experimental results show that the proposed method can sufficiently improve the efficiency of steel plates scheduling.

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Enhancing Scheduling Efficiency of Steel Logistics Parks by Optimized Storage Distribution

Enhancing Scheduling Efficiency of Steel Logistics Parks by ...

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Chinese automation Congress (CAC)

作者： Zhibin Xu Song Zheng Jinlong Wang Zhezhuang Xu Boyu Chen Qingdong Zhang Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing College of Electrical Engineering and Automation Fuzhou University Fuzhou China Sansteel Minguang Co. Ltd. Sanming China

The efficient management of storage has become a critical determinant of operational efficiency due to the growing and diverse demand for steel logistics parks. However, the current storage methods consistently result in significant imbalances in the loading of steel products and operating times across multiple yards. To address this challenge, we propose optimizing the distribution of steel products based on demand levels and retrieval efficiency. Firstly, we present an optimization problem that employs demand-driven storage principles to formulate the scheduling of steel product storage. Then, we introduce a novel algorithm called the Adaptive Preferred Evolutionary Heuristic (APEH) to tackle this problem. The results from our experiments indicate that this model effectively enhances the storage structure, and the proposed algorithm consistently yields optimal solutions within a reasonable timeframe.

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Predicting the State of Crane Spreader for Steel Bar Delivery Based on Machine Learning

Predicting the State of Crane Spreader for Steel Bar Deliver...

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IEEE International Conference on Cyber technology in automation, control, and Intelligent Systems

作者： Zibin Liu Zhezhuang Xu Jian Chen Chen Cui Boyu Chen Qingdong Zhang Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing Education College of Electrical Engineering and Automation Fuzhou University Fuzhou China Sansteel Minguang Co. Ltd Sanming Fujian China

Deducing the twisting pattern of spreader is very important for crane operators in steel warehouse, since crane spreaders have to be rotated repeatedly at steel bar transport sites. To improve the efficiency of the steel bar delivery, the state prediction for crane spreaders is necessary. In order to fulfill this requirement, the HTSK-LN-ReLU model prediction method based on Optuna optimization is applied in this paper. We deploy the IMU on the spreader surface, and then obtain the state data during the operation of the crane spreader by wireless transmission. The data are first denoised through fourier filtering, and then the Optuna framework is used to optimize the HTSK-LN-ReLU model's hyperparameters. The evaluation index of mean square error is used for model evaluation, and finally the optimal hyperparameter combination of the model is selected from 200 comparison experiments. Experimental results demonstrate superior performance of the HTSK-LN-ReLU model in predicting the state of crane spreader for steel bar delivery.

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Optimization for Pickup Vehicle Scheduling in Steel Logistics Park Based on Enhanced SPEA2

Optimization for Pickup Vehicle Scheduling in Steel Logistic...

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Chinese automation Congress (CAC)

作者： Hongjie Xu Jinlong Wang Zhezhuang Xu Boyu Chen Qingdong Zhang Education Department of Fujian Province Key Laboratory of Industrial Automation Control Technology and Information Processing College of Electrical Engineering and Automation Fuzhou University Fuzhou China Sansteel Minguang Co. Ltd. Sanming China

Pickup vehicle scheduling in the steel logistics park is a critical issue in determining the outbound effciency of steel products. Steel products are distributed in the yards of the steel logistics park with mixed storage, and the optional yards for each pickup operation are not unique, which greatly increases the scheduling complexity. To overcome this challenge, in this paper, we propose a pickup vehicle scheduling problem with mixed steel storage (PVSP-MSS) to optimize makespan of pickup vehicle and makespan of steel logistics park simultaneously. The optimization problem is described as a multi-objective problem, and an enhanced Strength Pareto Evolutionary Algorithm 2 (ESPEA) is proposed to solve the problem with high efficiency. Experiments are executed based on data collected from a real steel logistics park. The results confirm that ESPEA can significantly reduce both makespan of each pickup vehicle and makespan of steel logistics park.

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Switched High-Gain Based Interval Observer Design for Harmonic Oscillator System 42

Switched High-Gain Based Interval Observer Design for Harmon...

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42nd Chinese control Conference, CCC 2023

作者： Ming, Tianhang Wang, Xiaoling Liu, Bo Su, Housheng College of Automation & Ai Nanjing University of Posts and Telecommunications Nanjing210023 China Nanjing210023 China College of Information Engineering Minzu University of China Beijing100081 China Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligence Control of Ministry of Education Wuhan430074 China

ISBN: (纸本)9789887581543

In this paper, we revisit the interval observer design problem of harmonic oscillator system with unknown but bounded external disturbance and measurement noise. With the bounding information of the external disturbance and measurement noise, a high-gain interval observer is firstly defined and constructed for the harmonic oscillator system to realize the interval estimation on the state and the robust control of the system. Then, a switched high-gain interval observer is proposed to reduce the influence of the measurement noise for a tighter interval width on the state estimation. Finally, some numerical simulations are given to verify the theoretical results. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Robust control

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