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arXiv 2021年

作者： Barisic, Antonella Petric, Frano Bogdan, Stjepan The University of Zagreb Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems Unska 3 Zagreb10000 Croatia

In this paper we propose a novel approach to generate a synthetic aerial dataset for application in UAV monitoring. We propose to accentuate shape-based object representation by applying texture randomization. A diverse dataset with photo-realism in all parameters such as shape, pose, lighting, scale, viewpoint, etc. except for atypical textures is created in a 3D modelling software Blender. Our approach specifically targets two conditions in aerial images where texture of objects is difficult to detect, namely challenging illumination and objects occupying only a small portion of the image. Experimental evaluation of YOLO and Faster R-CNN detectors trained on synthetic data with randomized textures confirmed our approach by increasing the mAP value (17 and 3.7 percentage points for YOLO;20 and 1.1 percentage points for Faster R-CNN) on two test datasets of real images, both containing UAV-to-UAV images with motion blur. Testing on different domains, we conclude that the more the generalisation ability is put to the test, the more apparent are the advantages of the shape-based representation. © 2021, CC BY-NC-SA.

关键词： robotics

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Bipartite Flocking control for Multi-Agent Systems with Switching Topologies and Time Delays Under Coopetition Interactions

Bipartite Flocking Control for Multi-Agent Systems with Swit...

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IEEE Conference on Decision and control

作者： Zhuangzhuang Ma Bowen Li Jinliang Shao Yuhua Cheng Wei Xing Zheng School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China School of Computer Data and Mathematical Sciences Western Sydney University Sydney NSW Australia

This paper investigates the bipartite flocking behavior of multi-agent systems with coopetition interactions, where communications between agents are described by signed digraphs. The scenario with switching topologies due to the movement of agents, and time delays caused by the limited data transmission capability, is considered comprehensively. Nonlinear weight functions are designed to describe the relationship between the communication distance of agents and the coopetition degree in real biological networks. A distributed update rule based on the neighbors' information and the designed weight functions is proposed. By the aid of the graph theory and sub-stochastic matrix properties, the effectiveness of the proposed update rule is proved theoretically, and the algebraic conditions for achieving the bipartite flocking behavior are obtained. Finally, the theoretical results are verified by numerical simulations.

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A Study on Fake and Real Lane Change Behavior Prediction Based on Discrete Hidden Markov Model

A Study on Fake and Real Lane Change Behavior Prediction Bas...

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第40届中国控制会议

作者： Xianjian Jin Qikang Wang Zeyuan Yan Hang Yang Zhiwei Li Zhaoran Wang School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University State Key Laboratory of Automotive Simulation and Control Jilin University

In advanced assisted driving, an effective understanding of the driver's behavior can effectively improve the safety of the driving process. In this paper, we use discrete hidden Markov models to predict lane change and real lane change behavior by collecting natural driving data from a driving simulator. First, we build a lane change road on the Prescan software, collect data from 12 groups of drivers and filter the data, select whether the four characteristic parameters of steering wheel angle,steering wheel angular velocity, yaw rate and yaw acceleration are true for lane change perform analysis. Then, on the effective data set training, the viterbi algorithm is used to predict the fake lane change and the real lane change behavior of the discrete hidden Markov model. The prediction results show that the model can use the above four characteristic parameters to predict fake lane change and real lane change behavior. The accuracy of the prediction respectively is 89.83%,91.20%,88.52%,80.99%,and it has good accuracy.

关键词： DHMM fake lane change real lane change behavior prediction

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Scratch Detection of the PET Bottle Preform Based on Deep Learning 2

Scratch Detection of the PET Bottle Preform Based on Deep Le...

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2nd International Conference on Information Technology and Computer Application, ITCA 2020

作者： Zhou, Lei Liang, Jianan Cao, Yongjun Institute of Intelligent Manufacturing Gdas Guangdong Key Laboratory of Modern Control Technology Guangzhou China South China Robotics Innovation Research Institute Motion Control and Equipment Center Foshan China

ISBN: (纸本)9780738111414

Scratches are a common phenomenon in the production of the PET bottle preform, and traditional inspection by human eyes bring troubles to the automatic production process. In this paper, deep learning algorithm was used to detect PET bottle preform scratches. The detection algorithm based on YOLOv4 neural network was proposed. The detection effect was tested under different scratch degrees, which provide accurate information for automatic production picking of the PET bottle preform. The average detection time of a single image was 0.154s, and the shortest detection time was 0.133s. © 2020 IEEE.

关键词： Learning algorithms

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Corrigendum to “Fabrication of SiCN(Fe)/Al2O3 wave-absorbing ceramics with enhanced electromagnetic performance” [Ceram. Int. 49 (2023) 23851–23863]

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Ceramics International 2023年第23期49卷 39737-39738页

作者： Xiao Lin Hongyu Gong Zhangwei Chen Additive Manufacturing Institute Shenzhen University Shenzhen 518060 China College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen 518060 China Key Laboratory for Liquid-Solid Structural Evolution & Processing of Materials of Ministry of Education Shandong University Jinan 250061 China Key Laboratory of Special Functional Aggregated Materials Ministry of Education Shandong University Jinan 250061 China Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robotics Shenzhen University Shenzhen 518060 China

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Incorporating Steering Constraints in AGV Path Planning Through Graph Augmentation

Incorporating Steering Constraints in AGV Path Planning Thro...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Liming Zhang Zhimin Han Bo Wang Zhiyun Lin Xiangdong Xu School of Automation Hangzhou Dianzi University Hangzhou P. R. China Zhejiang CoTEK Robotics Co. Ltd Hangzhou P. R. China Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen P. R. China Peng Cheng Laboratory Shenzhen P. R. China Wenzhou Institute of Industry & Science Wenzhou P. R. China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

In this paper, we introduce an innovative method for the orientation-specific path planning of an Automated Guided Vehicle (AGV) with steering constraints, such as unmanned forklifts. Traditional shortest-path algorithms, which solely consider distance or travel time, are unsuitable for tasks where the AGV’s orientation is crucial. To address this, we have created an augmented graph that inherently incorporates the AGV’s steering constraints and orientation. By utilizing this enhanced graph structure, conventional shortest-path algorithms can be employed to ascertain a path that takes the AGV’s orientation into account. This novel method facilitates more adaptable and secure path planning, while preserving the immediacy required for real-time path planning execution.

关键词： Remotely guided vehicles Path planning Real-time systems Task analysis Standards Robots

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Accelerated identification method of industrial instrument based on yolov5

Accelerated identification method of industrial instrument b...

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Shimao Yu Haiyang Zheng Chunhe Song Pengpei Gao Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665464697

There are many problems in industrial instrument detection, such as complex background and visual Angle, etc. Traditional machine learning methods are difficult to achieve high-precision object detection and real-time monitoring applications. This paper proposes an accelerated identification method of industrial instruments based on YOLOv5. First, sample data are collected on site for image annotation, and YOLOv5 network structure is designed for transfer learning training, so as to realize image recognition of four types of industrial instruments. After that, the model is accelerated based on TensorRT on Jetson TX2 platform. The speed of instrument online detection reached 30 fps and mAP0.5 is 0.989.

关键词： Training Visualization Computer vision Image recognition Instruments Computational modeling Transfer learning

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Development of Robust Constrained control Strategy for Active Suspension System of Electric Vehicle with In-Wheel-Motor

Development of Robust Constrained Control Strategy for Activ...

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第40届中国控制会议

作者： Xianjian Jin Jiadong Wang Zeyuan Yan Zhiwei Li Zhaoran Wang School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University State Key Laboratory of Automotive Simulation and Control Jilin University

In order to suppress the vibration of the active suspension system for electric vehicle with in-wheel-motor, the robust constrained H control strategy is developed, the influences of the sprung mass variation and control constraint of the in-wheel active suspension system are considered in the control strategy. The oriented uncertain active suspension dynamic model is established through the linear fraction transformation method, in which in-wheel motor is suspended as dynamic vibration absorber. Under the unified framework of the generalized H synthesis system, the robust constrained H controller about the active suspension augmentation system is designed according to the theory of reachable sets and ellipsoids with considerations of the dynamic tire displacements and the suspension working spaces constraints. The simulation results show that the designed robust constrained H controller can effectively improve the ride comfort and road-holding ability even if external interference and parameter perturbation exist.

关键词： Electric vehicle vibration suppression active suspension robust control

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Design of Cascaded Model Predictive controller for Modular Robot Joints

Design of Cascaded Model Predictive Controller for Modular R...

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第34届中国控制与决策会议

作者： Wang Bolun Zhang Tao Shi Zhengjin Zhang Hualiang School of Automation and Electrical Engineering Shenyang University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences

As the core driving force providers in the robot system of next generation,modular robot joint's motion and power performance directly affects the overall motion control effect of robot *** design starts from the servo control of the modular robot joint and follows the principle of model predictive control to devise a cascaded model predictive controller based on the mathematical model of a permanent magnet synchronous motor for the joint to substitute for the traditional dual-loop PI controller with current and velocity loops in the closed-loop vector control *** design also employs the MATLAB/Simulink platform to simulate the control *** simulation experiments illuminate that the design of the cascaded model predictive controller has better dynamic response performance than the traditional PI controller and effectively enhances system robustness.

关键词： Modular Robot Joints Motor servo control Model predictive control

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intelligent electricity consumption forecasting and electricity-theft analysis method based on deep learning

Intelligent electricity consumption forecasting and electric...

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Electronic Information Engineering and Computer Technology (EIECT), International Conference on

作者： Shimao Yu Chunhe Song Pengpei Gao Haiyang Zheng Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

Residential electricity consumption data have great mining value for electricity-theft analysis and electricity consumption forecasting. This paper designs an intelligent electricity consumption forecasting and electricity-theft analysis method based on deep learning. Firstly, the power consumption data of a single user is converted into multi-period data through preprocessing, and the data forecasting is realized by designing a deep neural network model. The electricity-theft data are first amplified through user’s power data and electricity-theft labels. And then the time series are converted into two-dimensional tensors for classification and recognition by deep CNN. The classification accuracy of electricity-theft reaches 92.2% compared with RNN method.

关键词： Deep learning Training Analytical models Power demand Tensors Computational modeling Time series analysis

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