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Parallel Factories for Smart Industrial Operations:From Big AI Models to Field Foundational Models and Scenarios Engineering

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2079-2086页

作者： Jingwei Lu Xingxia Wang Xiang Cheng Jing Yang Oliver Kwan Xiao Wang Qingdao Academy of Intelligent Industries Qingdao 266114 The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 Motion G Inc.Singapore 068898Singapore IEEE School of Artificial Intelligence Anhui UniversityHefei 230031

The rapid advancement of fundamental theories and computing capacity has brought artificial intelligence,internet of things, extended reality, and many other new intelligent technologies into our daily lives. Due to the lack of interpretability and reliability guarantees, it is extremely challenging to apply these technologies directly to real-world industrial systems. Here we present a new paradigm for establishing parallel factories in metaverses to accelerate the deployment of intelligent technologies in real-world industrial systems: QAII-1.0. Based on cyber-physical-social systems,QAII-1.0 incorporates complex social and human factors into the design and analysis of industrial operations and is capable of handling industrial operations involving complex social and human behaviors. In QAII-1.0, a field foundational model called Eu Artisan combined with scenarios engineering is developed to improve the intelligence of industrial systems while ensuring industrial interpretability and reliability. Finally, parallel oil fields in metaverses are established to demonstrate the operating procedure of QAII-1.0.

关键词： Cyber-physical-social system(CPSS) Industry5.0 Metaverses Parallel factories Parallel intelligence

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Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

Deep Domain Adaptation Regression for Force Calibration of O...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Zhuo Chen Ni Ou Jiaqi Jiang Shan Luo Department of Engineering Robot Perception Lab Centre for Robotics Research King’s College London London United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors.

关键词： Accuracy Force Tactile sensors Lighting Elastomers Data collection Optical imaging Adaptive optics Calibration Optical sensors

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Differential Steering control for 6 × 6 Wheel-drive Mobile Robot 26

Differential Steering Control for 6 × 6 Wheel-drive Mobile ...

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26th International Conference on Automation and Computing, ICAC 2021

作者： Zhao, Hongqiang Luo, Chao Xu, Yongkang Li, Jiehao Central South University State Key Laboratory of High Performance Complex Manufacturing School of Mechanical and Electrical Engineering ChangSha China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing China

ISBN: (纸本)9781860435577

The 6 × 6 Wheel-drive mobile robot is widely used in the field of special operations, and there is the phenomenon of slipping in the process of steering. To study the steering accuracy and stability of mobile robot, the dynamic model of 6 × 6 Wheel-drive Mobile Robot is established, and the sliding mode control strategy of differential steering based on mobile robot is proposed. Firstly, taking the torque of six wheels and the operator's direction control as input, the three-dimensional attitude and the relative position of the steering center of the mobile robot are obtained by using the position and attitude sensor, to determine the reference steering angle of the mobile robot. Secondly, considering the relationship between the position error and the velocity error, a recursive sliding surface is designed, and a neural network is introduced to approach the uncertain part of the mobile robot model. Then, a nonlinear gain function is applied to construct a dynamic surface control law. Finally, through the Trucksim/Simulink co-simulation model, the nonlinear gain recursive sliding mode dynamic surface adaptive control method for mobile robot trajectory tracking can improve the tracking speed and control precision, and the control force and torque are smooth and reasonable. The results show that the method is effective in improving the steering accuracy and stability of mobile robot. © 2021 Chinese Automation and Computing Society in the UK-CACSUK.

关键词： Mobile robots

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Data-Driven Model for Detection,Isolation and Accommodation of Faulty Angle of Attack Sensor Measurements in Fixed Winged Aircraft

Data-Driven Model for Detection,Isolation and Accommodation ...

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第34届中国控制与决策会议

作者： Bemnet Wondimagegnehu Mersha Hongbin Ma School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

Ethiopian Airlines' Boeing 737-8 MAX nosedived and crashed shortly after takeoff on March 10,2019,at Ejere Town,south of Addis Ababa.A faulty angle of attack(AOA) sensor was the cause of the *** airplane accidents have been linked to faulty AOA sensors in the *** majority of the AOA sensor fault detection,isolation,and accommodation(SFDIA) literature relied on linear model-driven techniques,which are not suitable when the system's model is uncertain,complex,or *** multilayer perceptron(MLP) models have been employed in datadriven models in the literature and the effectiveness of deep learning-based data-driven models has not been *** this work,a data collection and processing method that ensures the collected data is not monotonous and a data-driven model for AOA SFDIA is *** proposed model uses a deep learning-based recurrent neural network(RNN) to accommodate for faulty AOA measurement under flight conditions with faulty AOA measurement,faulty total velocity measurement,and faulty pitch rate *** residual analysis with a fixed threshold is used to detect and isolate faulty AOA *** proposed and benchmark models are trained with the adaptive momentum estimation(Adam) *** show that the proposed model effectively detects,isolates,and accommodates faulty AOA measurements when compared to other data-driven benchmark *** method is able to detect and isolate faulty AOA sensors with a detection delay of 0.5 seconds for ramp failure and 0.1 seconds for step failure.

关键词： Data-driven modeling intelligent transportation systems Faulty angle of attack measurement Sensor fault Detection Isolation and Accommodation(SFDIA) and Recurrent neural network

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Relative Roughness Measurement Based Real-Time Speed Planning for Autonomous Vehicles on Rugged Road

Relative Roughness Measurement Based Real-Time Speed Plannin...

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Liang Wang Tianwei Niu Shoukun Wang Shuai Wang Junzheng Wang Key Laboratory of Drive and Control of Servo Motion System School of Automation Beijing Institute of Technology Beijing China The State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology School of Automation Beijing Institute of Technology Beijing China

In order to guarantee autonomous vehicles' autonomy, mobility, and ride quality in rugged environments, a real-time speed planning method based on the time-frequency transformation of terrain characteristics is designed to achieve adaptive speed planning of autonomous vehicles in rough ground. On the one hand, the vertical profile of the lidar's point cloud data is converted from the time domain to the frequency domain in real time, and the integrated area of the sub-frequency range in the frequency domain is chosen as the relative roughness quantification value to realize the roughness quantification under various terrains. On the other hand, to model the relationship between vehicle speed and relative roughness, iterative search is utilized to create a speed and roughness model, and sliding windows are employed to update the roughness to achieve continuous mapping between speed and roughness. Ultimately, a number of tests were conducted on various rough roads using the oil exploration vehicle EV-56 as the study object. The experimental results show that the proposed method can identify the terrain roughness changes under complex terrain and change their speed within 0.2 m accuracy.

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Distributed Conditional Gradient Algorithm for Two-Network Saddle-point Problem

Distributed Conditional Gradient Algorithm for Two-Network S...

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Chinese control and decision Conference, CCDC

作者： Jie Hou Xianlin Zeng Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665478977

We consider a two-network saddle-point problem with constraints, whose projections are expensive. We propose a projection-free algorithm, which is referred to as Distributed Frank-Wolfe Saddle-Point algorithm (DFWSP), which combines the gradient tracking technique and Frank-Wolfe technique. We prove that the algorithm achieves O(1/k 2 ) convergence rate for strongly-convex-strongly-concave saddle-point problems. We empirically shows that the proposed algorithm has better numerical performance than the distributed projected saddle-point algorithm.

关键词： Distributed computing Optimization Convergence

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Lane-Change Intention Prediction of Surrounding Vehicles Using BiLSTM-CRF Models with Rule Embedding

Lane-Change Intention Prediction of Surrounding Vehicles Usi...

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Chinese Automation Congress (CAC)

作者： Kai Wang Jie Hou Xianlin Zeng Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

Predicting lane-change intentions of surrounding vehicles can effectively help autonomous vehicles reduce collisions caused by lane changes and ensure driving safety. Because prediction methods based on black-box models will lead to passengers’ distrust of machine prediction, the intention prediction methods used to autonomous driving need to be interpretable and trustworthy. This paper presents a method for intention prediction of surrounding vehicles by using a bidirectional long short term memory network (BiLSTM) with a conditional random field (CRF) layer above it. Compared with intention prediction methods using deep network, the proposed method can find the features that contribute most to the prediction, thereby improving the interpretability and ensuring the prediction performance. In addition, by employing the transfer characteristic of the CRF layer, traffic rules and the experience of skilled drivers can be embedded to the prediction in the form of rules. Use rules to constrain the intention prediction, thereby improving the trustworthiness of prediction results. Test results on naturalistic driving dataset show that the proposed method can predict the lane-change intention with an accuracy of 97.22%, which is higher than that of Bi-LSTM.

关键词： Training Road transportation Knowledge engineering Predictive models Safety Autonomous vehicles Standards

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Temperature control System of Thermoelectric Cooler Based on Improved ADRC

Temperature Control System of Thermoelectric Cooler Based on...

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Chinese control and decision Conference, CCDC

作者： Xiyuan Shan Tao Cai Zhicheng Zong Hongbo Mu School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing China

ISBN: (纸本)9781665478977

Aiming at the problems containing complex working conditions, modeling difficulties and long-time delay of thermoelectric cooler temperature control system, an improved ADRC control method combining ADRC and Smith predictor is proposed in this paper. ADRC deals with the disturbances and uncertain dynamics in the system, SP compensates the time delay to improve the control performance. Compared with the traditional PID controller, the proposed control method has faster response speed and stronger anti-disturbance ability, meanwhile, overcomes the dependence of Smith predictor on object parameters to a certain extent. Through simulation and experimental verification, a good control effect is obtained, which provides a positive guidance of the related application of thermoelectric cooler.

关键词： Robust control Employee welfare Delay effects Simulation Perturbation methods Predictive models control systems

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An Improved Active Disturbance Rejection control Method for Crane System

An Improved Active Disturbance Rejection Control Method for ...

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Chinese control and decision Conference, CCDC

作者： Yubo Sun Xiyuan Shan Zhicheng Zong School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing China

ISBN: (纸本)9781665478977

In order to improve the dynamic performance of the underdriven crane system, an improved linear active disturbance rejection controller (LADRC) based on the new error was proposed. The improved LADRC takes the error value between the disturbance and its observed value multiplied by a coefficient as the basis for adjusting the linear extended state observer (LESO). The improved method has two advantages. Firstly, the new error can prevent the traditional LESO from choosing larger parameter adjustment disturbances, which will limit the performance of the observer. Secondly, the pole can be configured by adjusting the coefficient to obtain better dynamic characteristics. Finally, the effectiveness of the proposed method is verified by simulation and experiment. The proposed method can effectively restrain the swing of the payload and it is robust to system parameters perturbation as well.

关键词： Robust control Couplings Adaptation models Cranes Uncertainty Perturbation methods Bandwidth

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GAT-Net: A Network using Grid Attention and Transformer for Dynamic Facial Expression Recognition

GAT-Net: A Network using Grid Attention and Transformer for ...

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Chinese control and decision Conference, CCDC

作者： Wenxin Dai Yaping Dai Kaoru Hirota Shuai Shao School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing China

ISBN: (纸本)9781665478977

Dynamic facial expression recognition (DFER) in the wild has received widespread attention recently. There are complex factors such as face occlusion and pose variation in the wild. Facial expression recognition has a subtle competition between capturing local features of a human face and obtaining a global feature representation. This paper proposes an end-to-end DFER network GAT-Net based on the grid attention module and Transformer, which improves the robustness and accuracy of DFER in the wild. Specifically, GAT-Net is divided into two components: spatial feature extraction and temporal feature processing. The grid attention module of the spatial feature extraction component guides the network to pay attention to the local salient features of the face, which reduces the interference of field occlusion and non-frontal poses. The Transformer in the temporal feature processing component guides the network to learn the temporal relationship of high-level semantic features and the global representation of facial expression features. These two components balance the subtle competition between local features and global feature representations of facial expressions. The ablation experiment has proved the effectiveness of the grid attention module and Transformer. Experiments demonstrate that our GAT-Net outperforms state-of-the-art methods on DFEW and AFEW benchmarks with accuracies of 67.53%, and 50.14% respectively.

关键词： Face recognition Semantics Interference Benchmark testing Transformers Feature extraction Robustness

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