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Self-Preloading Flexible Attachment Actuator with Multi-Mechanism Hierarchical Structure

Journal of Bionic Engineering 2024年第4期21卷 1830-1846页

作者： Zhouyi Wang Qingsong Yuan Zhiyuan Weng Junsheng Yao Xuan Wu Lei Li Weipeng Li Yiping Feng Zhendong Dai College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics210016NanjingChina State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and Astronautics210016NanjingChina Robotics Laboratory China Nanhu Academy of Electronics and Information Technology314000JiaxingZhejiangChina

Flexible attachment actuators are popular in a wide range of applications,owing to their flexibility and highly reliable ***,their reversible adhesion performance depends on the actual effective contact area and peel angle during ***,a good actuator must ensure a uniform and reliable pre-pressure load on an adhesive surface,to increase the effective contact area of the attached surface,thereby maximizing *** study was inspired by fusion bionics for designing a hierarchical attachment structure with vacuum-adsorption and dry-adhesion *** designed structure used the normal force under the negative pressure of a suction cup as a stable source of a pre-pressure *** optimizing the rigid and flexible structural layers of the attachment structure,a load was applied uniformly to the adhesion area;thus,reliable attachment was achieved by *** structure achieved detachment by exploiting the large deformation of a pneumatic structure under a positive *** hierarchical attachment structure achieved up to 85%of the optimal performance of the adhesive *** to its self-preloading and reliable attachment characteristics,the designed structure can be used as an attachment unit in various complex scenarios,such as small,lightweight climbing platforms and the transport of objects in long,narrow pipelines.

关键词： Multi-mechanism attachment Self-preloading Hierarchical structure Adhesion Vacuum adsorption

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ES-MSCNN: An Echo state Multi-Scale Convolutional Neural Network for Multi-Step Prediction of Wastewater Quality Variables 7

ES-MSCNN: An Echo State Multi-Scale Convolutional Neural Net...

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7th International Conference on robotics, control and Automation Engineering, RCAE 2024

作者： Sun, Yue Liu, Yiqi South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science & Engineering Guangzhou510640 China

ISBN: (纸本)9798350355642

Given the limitations of hardware sensors together with strong nonlinearities and nexus between variables in wastewater treatment processes (WWTPs), the measurement of key quality variables is usually inaccurate or even impossible. Moreover, due to the short data sampling period, the single-output single-step prediction model cannot meet the actual demand. Therefore, this paper proposes a new soft sensor, termed as the Echo state Multi-Scale Convolutional Neural Network (ES-MSCNN), designed for accurate multivariate multi-step prediction. ES-MSCNN first generates an echo state representation (ESR) containing temporal information through an echo state network (ESN). Subsequently, a convolutional neural network (CNN) is employed to avoid the shallowness of ESN and further extract high-dimensional feature information from the data. Besides, a multi-scale learning strategy is used in the convolutional module to capture multi-timescale features and improve the multivariate prediction accuracy. Validation on the benchmark simulation model 2 platform and comparison with several models indicate that the proposed ES-MSCNN has higher accuracy and stronger robustness in multi-step prediction tasks with multi-input and multi-output. © 2024 IEEE.

关键词： Convolutional neural networks

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An Online Strategy for Nonlinear Vector Autoregression 7

An Online Strategy for Nonlinear Vector Autoregression

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7th International Conference on robotics, control and Automation Engineering, RCAE 2024

作者： Fang, Gang Liu, Yiqi South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science & Engineering Guangzhou510640 China

ISBN: (纸本)9798350355642

Nonlinear vector autoregression (NVAR), as an effective alternative to traditional reservoir computing (RC), has garnered significant attention in the field of time series prediction in recent years. Unlike conventional RC, NVAR does not require the use of random sampling matrices to define the underlying recurrent neural network, and it features fewer hyperparameters. However, NVAR is an offline model, making it challenging to meet the real-time monitoring requirements of industrial processes. Inspired by the Woodbury matrix identity, this paper proposes an online NVAR that can update the model using data arriving in blocks. Subsequently, this study constructs a soft sensing model based on the online NVAR to perform real-time monitoring of industrial processes. Finally, the effectiveness of the proposed method is validated using a real-world dataset from a wastewater treatment plant. © 2024 IEEE.

关键词： Wastewater treatment

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Research and Implementation of Industrial Real-Time Ethernet Scheduling Method Based on Time Slot Reuse 5

Research and Implementation of Industrial Real-Time Ethernet...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Gang, Mingxu Yan, Bingjun Hu, Bo Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes For Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350385557

To improve the operation efficiency and real-time scheduling capability of existing industrial control network systems, based on the existing industrial real-time Ethernet communication protocol technology, an industrial real-time Ethernet scheduling method based on time slot reuse is proposed. This method adopts the time-division multiplexing strategy, decomposes the continuous time into different time slots, configures the data of different types of nodes to be transmitted in their respective time slots, uses the peak shift clock synchronization method and timer trigger task mechanism to improve the real-time communication, and uses the time slot multiplexing mechanism to improve the data transmission efficiency;Finally, a test platform is built to verify the proposed method. The experimental results show that the proposed method has high clock synchronization accuracy and real-time scheduling capability. © 2024 IEEE.

关键词： Synchronization

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Design and Implementation of High Precision Clock Synchronization System Based on RT1064 6

Design and Implementation of High Precision Clock Synchroniz...

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6th International Conference on Communications, Information System and Computer Engineering, CISCE 2024

作者： Gang, Mingxu Yan, Bingjun Hu, Bo Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350353174

To realize the high-precision clock synchronization of various types of control equipment in the industrial process control network, a high-precision clock synchronizing system is designed and implemented. The system uses RT1064 microprocessor as the core, combined with ieee1588v2 high-accuracy clock synchronization protocol, and uses the MAC kernel integrated in rt1064 to support hardware timestamp function to achieve high-precision time synchronization between devices;Finally, the test verification platform is built to test and verify the accuracy. The test results show that the clock synchronization accuracy is within ± 400ns after the system runs stably, which meets the design requirements. © 2024 IEEE.

关键词： Mechanical clocks

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Unsupervised Anomaly Detection Approach Based on Adversarial Memory Autoencoders for Multivariate Time Series

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Computers, Materials & Continua 2023年第7期76卷 329-346页

作者： Tianzi Zhao Liang Jin Xiaofeng Zhou Shuai Li Shurui Liu Jiang Zhu Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang110000China Shenyang Institute of Automation Chinese Academy of SciencesShenyang110000China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang110000China University of Chinese Academy of Sciences Beijing100000China

The widespread usage of Cyber Physical systems(CPSs)generates a vast volume of time series data,and precisely determining anomalies in the data is critical for practical *** is the mainstream method for time series anomaly detection,and the anomaly is judged by reconstruction ***,due to the strong generalization ability of neural networks,some abnormal samples close to normal samples may be judged as normal,which fails to detect the *** addition,the dataset rarely provides sufficient anomaly *** research proposes an unsupervised anomaly detection approach based on adversarial memory autoencoders for multivariate time series to solve the above ***,an encoder encodes the input data into low-dimensional space to acquire a feature ***,a memory module is used to learn the feature vector’s prototype patterns and update the feature *** updating process allows partial forgetting of information to prevent model *** that,two decoders reconstruct the input ***,this research uses the Peak Over Threshold(POT)method to calculate the threshold to determine anomalous samples from normal *** research uses a two-stage adversarial training strategy during model training to enlarge the gap between the reconstruction error of normal and abnormal *** proposed method achieves significant anomaly detection results on synthetic and real datasets from power systems,water treatment plants,and computer *** F1 score reached an average of 0.9196 on the five datasets,which is 0.0769 higher than the best baseline method.

关键词： Anomaly detection autoencoder memory module adversarial training

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Robot-CT Scanning Oriented Synchronous Trajectory Planning of a Dual-Manipulator System 42

Robot-CT Scanning Oriented Synchronous Trajectory Planning o...

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42nd Chinese control Conference, CCC 2023

作者： Zhang, Shuaikang Yuan, Mingxing Zhang, Xuebo Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing400065 China

ISBN: (纸本)9789887581543

At present, most CT scanning systems adopt the traditional horizontal O-frame, suspension or C-arm structures, which can not balance the flexibility and functionality. With the development of robot technology, CT scanning systems based on manipulators have gradually emerged. However, limited by the motion accuracy of the manipulator, most researchers sacrifice the flexibility or increase the mass to improve the accuracy. To overcome this limitation, a new master-slave teaching-based scanning method is proposed to improve the scanning accuracy of the dual-manipulator Robot-CT system with the auxiliary function of rigid links. First, a rigid link is employed to connect the end effectors of the two manipulators. Next, the sampled trajectory of each joint of the slave-manipulator is recorded while the master-manipulator pulls the slave-manipulator to perform a circular motion. Finally, when carrying out the scanning, the dual-manilupator system reproduces the recorded joint trajectory without the rigid element, thereby improving the accuracy of the dual-manipulator CT scanning system to achieve a high-accuracy spatiotemporal synchronization and alignment of pose. To this end, a fifth-order spline interpolation is used to interpolate the sampled trajectory of each joint of the slave-manipulator, which allows it to perfectly reproduce the joint trajectory recorded during the teaching process and ensures the smoothness of the angular velocity and acceleration curves. Simulations are investigated and the corresponding results confirm the effectiveness of the trajectory planning method of the slave-manipulator. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Manipulators

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state and parameter identification of linearized water wave equation via adjoint method

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Science China(Information Sciences) 2024年第10期67卷 283-297页

作者： Yang YU Cheng-Zhong XU Hai-Long PEI Jinpeng YU School of Automation Qingdao University LAGEP Batiment CPE University Claude Bernard-Lyon 1 School of Automation Science and Engineering Key Laboratory of Autonomous Systems and Networked ControlMinistry of Education Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle SystemSouth China University of Technology

In this paper, we focus on the state and parameter identification problem of a hydrodynamical system. This system is modeled as a linearized water wave equation(LWWE), a hyperbolic state-space model coupled with a Laplace equation. We assume that the wave elevation at two distinct points is the only measurement of water waves. We show that the state and water depth can be reconstructed from this point measurement records. The identification problem is recast as an optimization problem over an infinite-dimensional space. We propose the adjoint method-based identification algorithm to generate an estimated state and water depth. We then performed a numerical simulation to show the effectiveness of our designed algorithm by comparing it with existing studies.

关键词： linearized water wave equation (LWWE) point observation state estimation parameter identification adjoint method numerical simulation

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ERASOR++: Height Coding Plus Egocentric Ratio Based Dynamic Object Removal for Static Point Cloud Mapping

ERASOR++: Height Coding Plus Egocentric Ratio Based Dynamic ...

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2024 IEEE International Conference on robotics and Automation, ICRA 2024

作者： Zhang, Jiabao Zhang, Yu Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

ISBN: (纸本)9798350384574

Mapping plays a crucial role in location and navigation within automatic systems. However, the presence of dynamic objects in 3D point cloud maps generated from scan sensors can introduce map distortion and long traces, thereby posing challenges for accurate mapping and navigation. To address this issue, we propose ERASOR++, an enhanced approach based on the Egocentric Ratio of Pseudo Occupancy for effective dynamic object removal. To begin, we introduce the Height Coding Descriptor, which combines height difference and height layer information to encode the point cloud. Subsequently, we propose the Height Stack Test, Ground Layer Test, and Surrounding Point Test methods to precisely and efficiently identify the dynamic bins within point cloud bins, thus overcoming the limitations of prior approaches. Through extensive evaluation on open-source datasets, our approach demonstrates superior performance in terms of precision and efficiency compared to existing methods. Furthermore, the techniques described in our work hold promise for addressing various challenging tasks or aspects through subsequent migration. © 2024 IEEE.

关键词： Mapping

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A Framework for Automatic Identification of Neural Network Structural Redundancy Based on Reinforcement Learning 5

A Framework for Automatic Identification of Neural Network S...

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5th International Conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Wu, Tingting Song, Chunhe Zeng, Peng 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 University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510662520

The increasing structure of neural networks makes it difficult to deploy on edge devices with limited computing resources. Network pruning has become one of the most successful model compression methods in recent years. Existing works usually compress models by removing unimportant filters based on the importance. However, the importance-based algorithms tend to ignore the parameters that extract edge features with small criterion values. And recent studies have shown that the existing criteria rely on norm and lead to similar model compression structures. Aiming at the problems of ignoring edge features and manually specifying the pruning rate in current importance-based model pruning algorithms, this paper proposes an automatic recognition framework for neural network structure redundancy based on reinforcement learning. First, we perform cluster analysis on the filters of each layer, and map the filters into a multidimensional space to generate similar sets with different functions. We then propose a criterion for identifying redundant filters within similar sets. Finally, we use reinforcement learning to automatically optimize the cluster dimension, and then determine the optimal pruning rate for each layer to reduce the performance loss caused by pruning. Extensive experiments on various benchmark network architectures and datasets demonstrate the effectiveness of our proposed framework. © 2023 SPIE.

关键词： Reinforcement learning

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