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Vision-based and real-time calibration of industrial robot by using deep learning and dimension-reduced models

Precision Engineering 2025年

作者： Zhouxiang Jiang Ruoheng Ding Yuxuan Liu Zhongjie Long Bao Song College of Mechanical and Electrical Engineering Beijing Information Science & Technology University 12 Xiaoying East Road Qinghe Haidian District Beijing 100192 China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University 12 Xiaoying East Road Qinghe Haidian District Beijing 100192 China National NC System Engineering Research Center Huazhong University of Science & Technology 1037 Luoyu Road Wuhan 430074 China

A real-time calibration method for six-DoF industrial robot is presented. It aims to address the issue that calibration always suspends the work of robot and is quite costly due to repeated utilization of expensive measurement instrument. For this, a pose measurement strategy with cost-effective cameras is proposed where the work trajectory of robot is considered as a strict constraint on configurations to meet the requirement of real time. However, such efficiency improvement is accompanied by the accuracy decrease because the camera brings large measurement error and the trajectory constraint results in the reduced parameter identifiability. Firstly, to pull up the camera performance to the same level of precise instrument like the laser tracker, a set of neural networks are designed to map the inaccurately measured poses by camera to the accurate poses of concerned joints. Secondly, a dimension-reducing method is proposed to truncate the constrained kinematic-error model into two, which commonly achieve high parameter identifiability. Simulation shows that the prediction accuracy of joint pose with the well-trained networks is much higher than the measurement accuracy of laser tracker. Besides, the calibration accuracy with the dimension-reduced models is validated to be higher than that with the single high-dimension model of typical calibration. Finally, a generation strategy of training data with deeper regularity is proposed to meet the accuracy requirement in real scene, and the comparative results in the experiment validate a good potential of the real-time calibration method in manufacturing that requires high accuracy of robot and no suspension of work.

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A Constructive Semi-global Differential Game Design Method for Multi-agent systems under Input Constraint and Collision Avoidance*

A Constructive Semi-global Differential Game Design Method f...

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Chinese Automation Congress (CAC)

作者： Xiongjun Wu Jialing Zhou Yuanlong Li Chunfang Chen Kai Li National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering The 802 Institute of Shanghai Academy of Space Flight Technology The Eighth Academy of China Aerospace Science and Technology Corporation Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China School of Automation Nanjing University Science and Technology Nanjing The 802 Institute of Shanghai Academy of Space Flight Shanghai School of Automation Science and Electrical Engineering Beihang University Beijing

ISBN: (纸本)9781665426480

The constructive semi-global differential game concept is introduced into the multi-agent’s cooperative controller designing of typical autonomous unmanned systems subject to controller input constraint as well as the collision avoidance requirements simultaneously. The problem of driving each agent which is assume to subject to input constraint nonlinearity from the initial position to a desired one and at the same time avoiding collisions (referring to both static obstacles and other agents) is modeled and transformed into multi-agent’s collision avoidance problem. In view of the requirement that different agents working together might possibly have individual but conflicting purpose (such as in the multi-missile cooperative detection requiring slowly and continuing actions vs fast formation control scenario, and multi-agents’ fast arriving vs avoiding different kinds of obstacles). The differential game theory provided a mechanism or framework for investigating different kinds of problems in which several players seek to achieve individual purposes or goals, which might be competing rather than cooperation. In view of the input constraints, the problem is reduced as a semi-global differential game. Conditions are established to guarantee input constraints are satisfied, and mainly by using the Hamilton-Jacobi-Isaacs (HJI for short) PDIs and as well as ε-Nash equilibria. The semi-global property of the states are also guaranteed. It provides a description in terms of the invariant set for the overall differential game system under constraint controller input. Illustrative examples are carried out to verify the effectiveness of the method, and also the feasibility of the suggested control protocols.

关键词： Protocols Automation Design methodology Differential games Games control systems Collision avoidance

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Study on Subjective Perception of S3D Image with Regular-texture

Study on Subjective Perception of S3D Image with Regular-tex...

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IEEE Joint International information technology and Artificial Intelligence Conference (ITAIC)

作者： Zhibin Su Bing Liu Weiwei Li Hui Ren Rui Huang School of Information and Communication Engineering Communication University of China Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism Beijing China CITVC KEHUA CO. LTD. Technology Service Branch Beijing China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

With the rapid development of binocular stereo vision technology, S3D images and videos have been widely used in many aspects. However, there are few researches on the detail perception of comfortable experience and stereo sense based on image content with certain disparity. Considering that texture is an important visual information contained in an image, in this paper, we designed the computer-generated S3D images containing two kinds of parallax based on three types of texture distribution and six regular textures for subjective evaluation. The influence of texture features on visual comfort, depth perception and the region of interest were studied through the related data analysis. For comparison with the content of disparities, the 2D images with same texture were designed and rated for assessment. Experimental results have shown that the texture was an important factor affecting the comfort perception. However, only when the subject and the background had texture at the same time, the texture was an important factor affecting the depth perception. The different texture distribution between the main subject and background would cause the change of area of interest and evaluation result. Overall, the larger the area of the texture, the lower the comfort evaluation results, and the higher the depth perception. Our studies will not only confirm the law of perception on S3D images with certain features, but also provide the guide for multidimensional content production and display, which have practical value for the development of S3D image and video technology.

关键词： Wavelet transforms Visualization Data analysis Three-dimensional displays Two dimensional displays Feature extraction Videos

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A MAC Protocol Identification Approach Based on Convolutional Neural Network

A MAC Protocol Identification Approach Based on Convolutiona...

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International Conference on Wireless communications and Signal Processing (WCSP)

作者： Xutong Zhang Weiguo Shen Jianliang Xu Zitong Liu Guoru Ding Colledge of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Science and Technology on Communication Information Security Control Laboratory No.36 Research Institute of CETC Jiaxing China College of Communication Engineering Army Engineering University Nanjing China

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

The medium access control (MAC) protocol identification is of great application value in cognitive radio. In order to realize the MAC protocol identification with high accuracy and avoid manual feature extraction, we convert the sampled data into the form of spectrogram. Then, a graphical scheme which combines the convolutional neural network (CNN) and the spectrogram is proposed. The simulation includes CNN method and support vector machine (SVM) method. It is suggested that the CNN method has better identification performance.

关键词： Media Access Protocol Spectrogram Protocols Support vector machines Time division multiple access Feature extraction Cognitive radio

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Content Distribution based on Joint V2I and V2V Scheduling in mmWave Vehicular Networks

arXiv

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arXiv 2022年

作者： Su, Lan Niu, Yong Han, Zhu Ai, Bo He, Ruisi Wang, Yibing Wang, Ning Su, Xiang The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China The Norwegian University of Science and Technology Norway and University of Oulu Finland

With the explosive growth of vehicle applications, vehicular networks based on millimeter wave (mmWave) bands have attracted interests from both academia and industry. mmWave communications are able to utilize the huge available bandwidth to provide multiple Gbps transmission rates among vehicles. In this paper, we address the content distribution scheduling problem in mmWave vehicular networks. It has been challenging for all vehicles in the same network to complete content downloading due to the limited communication resources of roadside units (RSUs) and the high mobility of vehicles. We propose a joint vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) scheduling scheme to minimize the total number of content distribution time slots from a global optimization perspective. In the V2I phase, the RSU serially transmits integrity content to vehicles, which are selected according to the vehicular network topology and transmission scheduling scheme. In the V2V phase, full-duplex communications and concurrent transmissions are exploited to achieve content sharing between vehicles and improve transmission efficiency. Performance evaluations demonstrate that our proposed scheme reduces the number of time slots and significantly improves system throughput when compared with other schemes, especially under large-size file transfers and a large number of vehicles. Copyright © 2022, The Authors. All rights reserved.

关键词： Millimeter waves

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Model based reinforcement learning for robot grasping trajectory generation 4

Model based reinforcement learning for robot grasping trajec...

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4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019

作者： Xue, Hongxiang Wen, Shuhuan Yang, Chao Liu, Huaping Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China Department of Computer Science and Technology State Key Laboratory of Intelligent Technology and System Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9781728100647

Trajectory generation is a fundamental problem for successful robotic grasping. However, most of the existing work dealt with this problem using supervised learning with a prescribed model. It prevents the developed grasp strategies from being used for new unknown scenarios. In this paper, inspired by the success of the reinforcement learning method, we developed a new method which optimizes grasp trajectories using a policy search method. An important advantage of the proposed method is that the complicated 3D model could be used to generate the multi-dimensional grasp configurations. The developed algorithm has been validated on some typical robotic hand. The experimental results show that the proposed method achieves excellent performance. © 2019 IEEE.

关键词： Trajectories

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Camouflaged Object Detection via Context-aware Cross-level Fusion

arXiv

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arXiv 2022年

作者： Chen, Geng Liu, Si-Jie Sun, Yu-Jia Ji, Ge-Peng Wu, Ya-Feng Zhou, Tao Northwestern Polytechnical University Xi’an710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering China Data Processing Center School of Power and Energy China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Science Inner Mongolia University Hohhot010021 China Artificial Intelligence Institute School of Computer Science Wuhan University Wuhan430072 China

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. Accurate COD suffers from a number of challenges associated with low boundary contrast and the large variation of object appearances, e.g., object size and shape. To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects. Specifically, we compute informative attention coefficients from multi-level features with our Attention-induced Cross-level Fusion Module (ACFM), which further integrates the features under the guidance of attention coefficients. We then propose a Dual-branch Global Context Module (DGCM) to refine the fused features for informative feature representations by exploiting rich global context information. Multiple ACFMs and DGCMs are integrated in a cascaded manner for generating a coarse prediction from high-level features. The coarse prediction acts as an attention map to refine the low-level features before passing them to our Camouflage Inference Module (CIM) to generate the final prediction. We perform extensive experiments on three widely used benchmark datasets and compare C2F-Net with state-of-the-art (SOTA) models. The results show that C2F-Net is an effective COD model and outperforms SOTA models remarkably. Further, an evaluation on polyp segmentation datasets demonstrates the promising potentials of our C2F-Net in COD downstream applications. Our code is publicly available at: https://***/Ben57882/C2FNet-TSCVT. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Quantum proof and high-speed privacy amplification for quantum key distribution

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Physical Review Applied 2025年第5期23卷 054004-054004页

作者： Bing-Qi Yan Jun-Bin Wu Fan Fan Bing-Jie Xu Xiao-Ling Zhang Min Gu Key Laboratory of Atomic and Subatomic Structure and Quantum Control (Ministry of Education) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter School of Physics South China Normal University Guangzhou 510006 China Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials Guangdong-Hong Kong Joint Laboratory of Quantum Matter South China Normal University Guangzhou 510006 China Quantum Science Center of Guangdong-HongKong-Macao Greater Bay Area (Guangdong) Shenzhen 518000 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China National Key Laboratory of Security Communication Institute of Southwestern Communication Chengdu 610041 China

Privacy amplification (PA) is a vital step in the postprocessing stage of quantum key distribution, which can extract a secure key from a source partially leaked to the environment. We propose a PA scheme that uses the square hash (SQH)–modular arithmetic hash (MH) function. In consideration of a potential quantum attacker, we use the trace distance as a metric to measure the closeness between two classical-quantum states. We transform a raw key state to a processed state using this scheme and derive the trace distance between the processed state and the ideal target state. It is proven that the trace distance can be reached as small as desired. Additionally, our approach accelerates the SQH-MH function through the number-theoretic transform, reducing the computational complexity to O(nlog n). On the basis of a CPU, a maximum throughput rate of 1.06 Gbit/s is achieved, and the results show that our algorithm demonstrates good stability of the throughput rate when handling tasks with different compression ratios.

关键词： Number theory

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Energy Management and Trajectory Optimization for UAV-Enabled Legitimate Monitoring systems

arXiv

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arXiv 2020年

作者： Hu, Shuyan Wu, Qingqing Wang, Xin State Key Laboratory of ASIC and System School of Information Science and Technology Fudan University Shanghai200433 China Department of Electrical and Computer Engineering National University of Singapore Singapore119077 Singapore State Key Laboratory of ASIC and System Shanghai Institute for Advanced Communication and Data Science Department of Communication Science and Engineering Fudan University Shanghai200433 China

Thanks to their quick placement and high flexibility, unmanned aerial vehicles (UAVs) can be very useful in the current and future wireless communication systems. With a growing number of smart devices and infrastructure-free communication networks, it is necessary to legitimately monitor these networks to prevent crimes. In this paper, a novel framework is proposed to exploit the flexibility of the UAV for legitimate monitoring via joint trajectory design and energy management. The system includes a suspicious transmission link with a terrestrial transmitter and a terrestrial receiver, and a UAV to monitor the suspicious link. The UAV can adjust its positions and send jamming signal to the suspicious receiver to ensure successful eavesdropping. Based on this model, we first develop an approach to minimize the overall jamming energy consumption of the UAV. Building on a judicious (re)formulation, an alternating optimization approach is developed to compute a locally optimal solution in polynomial time. Furthermore, we model and include the propulsion power to minimize the overall energy consumption of the UAV. Leveraging the successive convex approximation method, an effective iterative approach is developed to find a feasible solution fulfilling the Karush-Kuhn-Tucker (KKT) conditions. Extensive numerical results are provided to verify the merits of the proposed schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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LSTM-based Frequency Hopping Sequence Prediction

LSTM-based Frequency Hopping Sequence Prediction

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International Conference on Wireless communications and Signal Processing (WCSP)

作者： Gao Li Jianliang Xu Weiguo Shen Wei Wang Zitong Liu Guoru Ding Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Communication Information Security Control Laboratory No.36 Research Institute of CETC Jiaxing China College of Communications Engineering Army Engineering University China

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

The continuous change of communication frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication. The core of this communication process is the frequency-hopping (FH) sequence with pseudo-random characteristics, which controls carrier frequency hopping. However, FH sequence is always generated by a certain model and is a kind of time sequence with certain regularity. Long Short-Term Memory (LSTM) neural network in deep learning has been proved to have strong ability to solve time series problems. Therefore, in this paper, we establish LSTM model to implement FH sequence prediction. The simulation results show that LSTM-based scheme can effectively predict frequency point by point based on historical HF frequency data. Further, we achieve frequency interval prediction based on frequency point prediction.

关键词： Predictive models Time-frequency analysis Frequency control Training Time series analysis Logic gates Spread spectrum communication

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