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arXiv 2023年

作者： Changoluisa Caiza, Iván D. Milas, Ana Montes Grova, Marco A. Pérez-Grau, Francisco Javier Petrovic, Tamara The University of Zagreb Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems Unska 3 Zagreb10000 Croatia Aerospace Technology Park of Andalusia Seville Spain

Autonomous exploration using unmanned aerial vehicles (UAVs) is essential for various tasks such as building inspections, rescue operations, deliveries, and warehousing. However, there are two main limitations to previous approaches: they may not be able to provide a complete map of the environment and assume that the map built during exploration is accurate enough for safe navigation, which is usually not the case. To address these limitations, a novel exploration method is proposed that combines frontier-based exploration with a collector strategy that achieves global exploration and complete map creation. In each iteration, the collector strategy stores and validates frontiers detected during exploration and selects the next best frontier to navigate to. The collector strategy ensures global exploration by balancing the exploitation of a known map with the exploration of unknown areas. In addition, the online path replanning ensures safe navigation through the map created during motion. The performance of the proposed method is verified by exploring 3D simulation environments in comparison with the state-of-the-art methods. Finally, the proposed approach is validated in a real-world experiment. © 2023, CC BY-NC-SA.

关键词： Iterative methods

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Intelligent Leakage Inspection Method of Magnetic Mill Workshop based on Lightweight Yolov5

Intelligent Leakage Inspection Method of Magnetic Mill Works...

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

作者： Shimao Yu Chunhe Song Yong Sun Pengpei Gao 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

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In the process of mining company beneficiation, large rotating equipment, pipelines and electrical equipment have many complex inspection areas and diverse inspection contents. The lack of intelligent solutions to the problem of pipeline leakage will cause serious consequences such as equipment shutdown if it is not found in time. This paper proposes a lightweight yolov5 intelligent leakage inspection method for the magnetic mill workshop. The inspection robot is used to continuously collect images of possible leakage points and transmit them to the background server. The improved yolov5 algorithm simplifies the model structure and realizes rapid and accurate identification of leakage targets with limited computing resources. The computational efficiency is increased by 44.98% with almost no loss of model accuracy. The mAP_0.5 of identifying large targets at leakage points reaches 97.96%.

关键词： YOLO Training Accuracy Conferences Magnetic resonance imaging Computational modeling Pipelines Inspection Servers Monitoring

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Design and Implementation of Industrial Real-Time Ethernet Redundant Network Based on Parallel Redundancy Protocol

Design and Implementation of Industrial Real-Time Ethernet R...

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Computer Science, Electronic Information Engineering and Intelligent control Technology (CEI), IEEE International Conference on

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

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

Industrial real-time Ethernet technology is widely used in modern industrial control systems due to its high communication rate and low application cost. However, in complex network environments, industrial real-time Ethernet may encounter data collisions, data loss, and other issues, which reduce the overall reliability of industrial control systems. To address these issues, a method for designing and implementing industrial real-time Ethernet redundancy networks based on parallel redundancy protocols is proposed. The method first analyzes the redundant data frame structure, dual-connected node (DANP) structure, and redundancy implementation principle of parallel redundancy protocols (PRP). Then, aiming at the standard redundancy discard algorithm principle and its shortcomings, an improved sliding window redundancy discard algorithm is proposed to adapt to complex network environments. Based on the discard algorithm, dual-connected redundancy nodes are designed and implemented. Finally, the dual-connected redundancy nodes are used as the core to construct industrial real-time Ethernet redundancy networks. The test results show that in a PRP-based industrial real-time Ethernet redundant network, when a single redundant network fails, the system can switch without delay, greatly improving the reliability of the industrial control system communication network

关键词： Protocols Industrial control Computer network reliability Redundancy Ethernet Complex networks Reliability engineering

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LiDAR-based drone navigation with reinforcement learning

TechRxiv

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TechRxiv 2023年

作者： Miera, Pawel Szolc, Hubert Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

Reinforcement learning is of increasing importance in the field of robot control and simulation plays a key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number of published scientific papers involving this approach. In this work, an autonomous drone control system was prepared to fly forward (according to its coordinates system) and pass the trees encountered in the forest based on the data from a rotating LiDAR sensor. The Proximal Policy Optimization (PPO) algorithm, an example of reinforcement learning (RL), was used to prepare it. A custom simulator in the Python language was developed for this purpose. The Gazebo environment, integrated with the Robot Operating System (ROS), was also used to test the resulting control algorithm. Finally, the prepared solution was implemented in the Nvidia Jetson Nano eGPU and verified in the real tests scenarios. During them, the drone successfully completed the set task and was able to repeatably avoid trees and fly through the forest. © 2023, CC BY.

关键词： Drones

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Robust Robot control Policy Learning Based on Gaussian Mixture Variational Autoencoders

Robust Robot Control Policy Learning Based on Gaussian Mixtu...

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Chinese Automation Congress (CAC)

作者： Qingwei Dong Peng Zeng Chuanzhi Zang Guangxi Wan Xiaoting Dong Shijie Cui State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Shenyang University of Technology Shenyang China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Reinforcement learning methods have demonstrated advanced capabilities in training neural network controllers for specific tasks. In the industrial domain, where model discrepancies and state disturbances occur, robot control strategies must exhibit adaptability. Existing studies, such as Generative Motor Reflexes (GMR), employ Variational Autoencoders (VAE) to approximate the latent variable space distribution to a standard normal distribution. However, this method, while straightforward, offers limited capability in representing data. The Gaussian Mixture Variational Autoencoder (GMVAE) algorithm leverages a Gaussian Mixture Model (GMM) as the prior distribution for data generation, enhancing the representation of the original state distribution. To increase the robustness of the trained neural network controllers against unknown states, we utilize GMVAE to represent the original state space, subsequently learning the mapping from latent variables to actions. We further accelerate the policy search process by integrating state representation learning with Guided Policy Search (GPS) methods. Our method was evaluated on robotic reaching tasks. Experimental results validate the robustness of our method against state perturbations.

关键词： Representation learning Training Visualization Autoencoders Robot control Neural networks Aerospace electronics Robustness Standards Global Positioning System

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Event-Triggered Finite-Time Course control for Unmanned Surface Vehicles with Quantization

Event-Triggered Finite-Time Course Control for Unmanned Surf...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Yiming Sun Yuqi Liu Xi Jin Changqing Xia Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Liaoning China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

This paper concerns the event-triggered finite-time course control problem of an unmanned surface vehicle (USV) under the network environment. To facilitate investigation, the nonlinear dynamic of the course control system is firstly modeled by a class of state-dependent uncertain systems. Then a output feedback framework of course control is established for the USV subject to wave-induced disturbance, where an event generator with dynamic triggering scheme and a logarithmic quantizer are introduced to save communication resources. By means of slack variable technique, a novel co-design criterion of output feedback controller and event-triggered scheme is developed to guarantee the course control system finite-time bounded with a desired ${\mathcal{H}}_{\infty}$ performance. Finally, the effectiveness of the proposed method is verified through a practical simulation case.

关键词： Industrial electronics Uncertain systems Quantization (signal) Simulation control systems Nonlinear dynamical systems Vehicle dynamics

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LiDAR-based drone navigation with reinforcement learning

arXiv

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arXiv 2023年

关键词： Robot Operating System

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A new SPICE modeling approach of annular MOSFET in 350 nm commercial CMOS process

A new SPICE modeling approach of annular MOSFET in 350 nm co...

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

作者： He Cheng Zhijia Yang Chao Zhang Zhipeng Zhang Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

Annular gate MOSFETs have emerged as potential solutions for radiation-hardened electronics, demonstrating exceptional total ionizing dose tolerance. However, accurate modeling of annular MOSFETs is crucial when designing analog integrated circuits for use in radiation environments, especially when implementing commercial CMOS processes. To enable circuit simulation using tools like SPECTRE/SPICE, foundry-provided PDKs must incorporate a rigorously verified compact model of annular MOSFET behavior. TCAD simulation can then be used to numerically evaluate the current characteristics of the annular MOSFET against the refined compact model constructed from the foundry-provided PDK. In this paper, by aligning the current characteristics of a standard NMOS between TCAD and SPICE simulations, we can verify the material process conditions. Then, based on the material process parameters, the model of the annular gate MOSFET is established using the TCAD simulator. Finally, reasonable agreements are obtained between the TCAD and SPICE simulation results for the I DS -V GS and I DS -V DS characteristics.

关键词：

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Research on Multiple Automated Guided Vehicles Path Planning to Reduce Default Time 2

Research on Multiple Automated Guided Vehicles Path Planning...

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2nd IEEE International Conference on Data Science and Computer Application, ICDSCA 2022

作者： Liu, Yiyang Yan, Shuaihua Hou, Zheng Zhao, Yang Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China Kunshan Intelligent Equipment Research Institute Key Laboratory of Networked Control Systems Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China University of Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China College of Information Science and Engineering Northeastern University Shenyang China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

This paper studies the path planning problem of multiple automated guided vehicles (multi-AGV) in a matrix manufacturing workshop. We proposed the multi-AGV path planning problem with default time, which is a variant of the classic path planning problem. In order to ensure the timely completion of each task, the default time of the task is explicitly considered. The goal is to obtain a conflict-free path with the minimum total default time, total time, and total path length. For these three goals, this problem is NP-hard. In order to solve this problem better, we established a multi-objective optimization model of total default time, total time and total path length of multi-AGV in flexible manufacturing environment. This paper proposes a multi-AGV path planning method that integrates game theory and A∗ algorithm. This method regards each AGV as a participant in the game model, through the default time obtained from each game dynamically adjusts the probability of each AGV planning the next path, and uses game theory to help the system dynamically select the most suitable AGV to plan the next path to improve the on-time delivery rate of tasks. Finally, the simulation results show that the multi-AGV path planning method integrating game theory and A∗ algorithm further reduces the total default time, total time and total path length of all AGVs. © 2022 IEEE.

关键词： Motion planning

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A New Recursive Composite Adaptive controller for Robot Manipulators

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Space(Science & Technology) 2021年第1期2021卷 77-83页

作者： Jianfei Li Yaobing Wang Zhiyong Liu Xin Jing Chengwei Hu The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Intelligent Space Robotic System Technology and Applications Beijing Institute of Spacecraft System EngineeringBeijing 100094China State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China

In this paper,a new recursive implementation of composite adaptive control for robot manipulators is *** investigate the recursive composite adaptive algorithm and prove the stability directly based on the Newton-Euler equations in matrix form,which,to our knowledge,is the first result on this point in the *** proposed algorithm has an amount of computation O(n),which is less than any existing similar algorithms and can satisfy the computation need of the complicated multidegree *** manipulator of the Chinese Space Station is employed as a simulation example,and the results verify the effectiveness of this proposed recursive algorithm.

关键词： Robot manipulators robot

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