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2022 2nd International Conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2022

作者： Song, Libo Liu, Chang Shi, Haibo Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation 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

For the flexible job shop scheduling problem with parallel batch processing machines (FJSP-PBPM), the mathematical model of the problem is defined, and an improved enhanced discrete particle swarm algorithm is proposed to solve this problem. In terms of global search, an improved discrete particle swarm algorithm is adopted, and effective particle velocity and position update formulas are designed;in the local search, in order to speed up the convergence speed and avoid local convergence, it is integrated into the q-learning method in reinforcement learning. The state space, action space, reward function, and Q-table update methods that meet the scheduling problem are designed, and the proposed hybrid algorithm achieves an effective balance between global search and local search capabilities. At the same time, according to the characteristics of FJSP-PBPM, effective process sequence scheduling rules and batch machine selection rules are designed respectively to improve the search ability of the algorithm. Through the standard FJSP benchmark problem and the production data of a transformer manufacturer that meets the characteristics of the FJSP-PBPM problem, the validity and feasibility of the proposed algorithm are verified. © Published under licence by IOP Publishing Ltd.

关键词： Reinforcement learning

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Blockchain-based Dependable Task Offloading and Resource Allocation for IIoT via Multi-Agent Deep Reinforcement Learning

Blockchain-based Dependable Task Offloading and Resource All...

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IEEE Conference on Vehicular Technology (VTC)

作者： Peifeng Zhang Chi Xu Changqing Xia Xi Jin 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 Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

Task offloading and resource allocation are fundamental and crucial for the edge computing-enhanced industrial Internet of things, where the security and credibility among massive heterogeneous devices are being challenged. This paper first proposes a novel blockchain consensus scheme named replicated and Byzantine fault tolerant, which can enhance the trust among nodes with the low communication cost. Then, with the objective of minimizing the task completion time, which includes credible verification, task offloading and transaction record, a joint task offloading and resource allocation problem with respect to blockchain verification ratio, offloading decision, communication and computing resources is formulated. Due to its non-convexity and the decentralized characteristic of blockchain, a multi-agent deep reinforcement learning algorithm with deterministic policy gradient is proposed to appropriate the optimal solution. Experiment results confirm the effectiveness of the proposed scheme in guaranteeing the timeliness and security.

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Description and fault diagnosis in autonomous underwater vehicles based on ontologies

Description and fault diagnosis in autonomous underwater veh...

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Ocean Studies (ICOS), International Conference on

作者： Timoshenko Alexander Zuev Alexander Mursalimov Eduard Gribova Valeria Inzartsev Alexander Department of Automation and Robotics Laboratory of intelligent systems for marine robots Far Eastern Federal UniversityInstitute of Marine Technology Problems FEB RAS Vladivostok Russia Laboratory of Intelligent Systems Institute of Automation and Control FEB RAS Vladivostok Russia Laboratory of Control Systems Institute of Marine Technology Problems FEB RAS Vladivostok Russia

ISBN: (纸本)9781665473736

The paper proposes new ontologies for describing and diagnosing malfunctions of subsystems of autonomous underwater vehicles (AUV) as a part of the development of a previously created approach to the intelligent diagnosis of these vehicles using knowledge bases. To implement this approach, it is proposed to use two knowledge bases, one of which describes malfunctions, features for their identification and actions necessary to adapt to the negative consequences caused by the appearance of these malfunctions. The other database contains information about the main subsystems of the AUV, devices and replaceable modules, as well as the states they may be in. Knowledge bases are used in the work of the intelligent control-emergency system (ICES) on board the AUV, which allows for comprehensive functional diagnostics of all subsystems of the device, including payload modules, as well as adaptation to the consequences of emerging malfunctions, taking into account the actual cause-and-effect relationships between them. To test the system’s operability, a prototype was developed. The results of the prototype simulation showed that ICES performs the tasks and can be implemented on the onboard computers of existing AUV using well-known programming technologies.

关键词： Performance evaluation Autonomous underwater vehicles Simulation Knowledge based systems Prototypes Ontologies Programming

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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel State Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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The Development of a Marine Internet of Things System Architecture for the control of Heterogeneous Robotic Agents

The Development of a Marine Internet of Things System Archit...

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Ocean Studies (ICOS), International Conference on

作者： Aleksey A. Kabanov Vadim A. Kramar Denis A. Tokarev Kirill V. Dementiev Department of Informatics and Control in Technical Systems Sevastopol State University Sevastopol Russian Federation Robotics and Intelligent Control System Laboratory Sevastopol State University Sevastopol Russian Federation

ISBN: (纸本)9781665473736

To create a platform for the Marine IoT for ensuring the interaction and digital navigation of heterogeneous marine robotic agents, it is necessary to combine into one network heterogeneous robotic agents, active and passive means of communication and illumination of the underwater and surface environment (marine communication buoys, underwater sonar stations, coastal connection posts). For their joint work, it is necessary to create a communication system unifying all elements of the platform, an integrated navigation and information management system for solving complex problems of navigation and motion control of robotic agents included in the platform to implement a joint mission. To do this, it is necessary to define the architecture of the platform to use a unified approach to data exchange.

关键词： Underwater communication systems architecture Sonar Sea measurements Switches Network architecture User interfaces

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A Novel Hybrid Method for Fault Diagnosis of Industrial Equipment Based on Vibration Signals 14

A Novel Hybrid Method for Fault Diagnosis of Industrial Equi...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Tao, Sen Wang, Kai Zeng, Peng Yu, Tianda Wu, Hao Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110169 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Nuclear Power Institute of China Chengdu610213 China State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing400044 China

ISBN: (纸本)9798350301359

Industrial equipment reliability is a critical issue that smart manufacturing systems must address. Implementation of online fault diagnosis for industrial equipment is a promising technology that can improve equipment reliability. Vibration analysis is a popular technique for equipment condition monitoring. For the non-linear and non-stationary features of industrial vibration signals, a hybrid model based on wavelet transform (WT), support vector machine (SVM), and grey wolf optimization algorithm (GWO) is proposed. The time-frequency domain features of fault signals are extracted by wavelet transform to solve the non-linearity of signals, and the obtained feature vectors are input into SVM for classification. Aiming at the problem of setting penalty coefficient and kernel function parameters, GWO is introduced to seek hyperparameters by using the hunting principle of wolves. In the experimental part, firstly, the comparison of the GWO-WT-SVM, GWO-SVM and SVM models was carried out. Secondly, Comparison with classical classification algorithms is made. Apparently, the GWO-WT-SVM model achieves higher accuracy than other models in bearing fault diagnosis. © 2023 IEEE.

关键词： component Fault diagnosis GWO PHM SVM

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Design and implementation of FPGA-based hardware acceleration system for target detection

Design and implementation of FPGA-based hardware acceleratio...

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2022 Chinese Automation Congress, CAC 2022

作者： Zhan, Canyu Zhao, Bingjie Zhang, Huangliang Yan, Zhongpeng Wu, Jie Yuan, Da Shenyang University of Chemical Technology Shenyang China 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 Shenyang University of Technology Shenyang China

ISBN: (纸本)9781665465335

Since the application of target detection in convolutional neural networks is becoming more and more widespread, and the computational volume is increasing, the complexity of the network hierarchy and the memory requirements are gradually increasing, so according to the Field Programmable Gate Array (FPGA) parallel computing characteristics, using FPGA to accelerate the convolutional neural network hardware, can design a CPU+FPGA target detection hardware acceleration system. The system adopts the YOLOv2 target detection algorithm and divides the tasks according to the network structure of the algorithm, and at the same time, it quantifies the 32-bit floating-point numbers of the algorithm with 16-bit fixed points, optimizes the convolutional, pooling, and reordering layers of the algorithm, and finally designs the algorithm by using the HLS high-level synthesis. The system uses PYNQ-Z2 platform to implement the convolutional neural network YOLOv2, and the final system performance reaches 27.06GOPs (Giga Operations Per Second, 1 billion operations/s), and the power consumption is only 2.609W, compared to CPU (E5-2620V4), its performance is 6.58 times that of CPU, and the power consumption is only 3% of the CPU, which has certain advantages compared with current target detection systems. © 2022 IEEE.

关键词： Convolution

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Research on Target Detection Method Based on Improved YOLOv5 19th

Research on Target Detection Method Based on Improved YOLOv5

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19th International Conference on Web Information systems and Applications, WISA 2022

作者： Wang, Rongzhuang Liu, Jun Zhang, Hualiang School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang110142 China Liaoning Key Laboratory of Intelligent Technology for Chemical Process Industry Shenyang110142 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9783031203084

At present, various target detection algorithms are used in detection and classification. It is a problem to improve the accuracy and speed of target detection by using deep learning and neural network model to train the parameters of the data set. Aiming at the problems of insufficient detection, inaccurate detection of complex background and loss of small target feature information in yolov5 target detection, an improved method combining hole convolution, moving exponential average and retraining of positive samples is proposed in this paper. This method effectively solves the problems of complex background, occlusion and incomplete feature extraction, reduces the loss rate of target features, and improves the detection accuracy of each category. The improved yolov5 target detection algorithm in this paper can not only use multi feature fusion and accelerate positive sample matching, but also use cavity convolution to increase receptive field, improve the detection effect of small targets and reduce the occurrence of missed detection and false detection. Experiments show that the improved yolov5 algorithm has better detection rate and lower loss rate than many existing target detection algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Nowhere to Go: Benchmarking Multi-robot Collaboration in Target Trapping Environment

arXiv

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arXiv 2023年

作者： Zhang, Hao Chen, Jiaming Cheng, Jiyu Li, Yibin Yang, Simon X. Zhang, Wei The School of Control Science and Engineering Shandong University Jinan250061 China The Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph GuelphONN1G2W1 Canada

Collaboration is one of the most important factors in multi-robot systems. Considering certain real-world applications and to further promote its development, we propose a new benchmark to evaluate multi-robot collaboration in Target Trapping Environment (T2E). In T2E, two kinds of robots (called captor robot and target robot) share the same space. The captors aim to catch the target collaboratively, while the target will try to escape from the trap. Both the trapping and escaping process can use the environment layout to help achieve the corresponding objective, which requires high collaboration between robots and the utilization of the environment. For the benchmark, we present and evaluate multiple learning-based baselines in T2E, and provide insights into regimes of multi-robot collaboration. We also make our benchmark publicly available and encourage researchers from related robotics disciplines to propose, evaluate, and compare their solutions in this benchmark. Our project is released at https://***/Dr-Xiaogaren/T2E. Copyright © 2023, The Authors. All rights reserved.

关键词： Multipurpose robots

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Lidar-Inertial and Downward Vision Based Mobile Robot SLAM in Digital Workshops

Lidar-Inertial and Downward Vision Based Mobile Robot SLAM i...

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Data Driven control and Learning systems (DDCLS)

作者： Yanchao Zhang Hao Geng Bin Liu Yinlong Zhang Xudong Yuan Sichao Zhang School of Information Science and Engineering Shenyang University of Technology Shenyang China Guangzhou Institute of Industrial Intelligence Guangzhou China 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 Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In order to solve the problems of long-term cumulative error and the absence of absolute pose in the existing Lidar-Inertial Measurement Unit (IMU) fusion methods for Automated Guided Vehicle (AGV) Simultaneous Localization and Mapping (SLAM), we propose a new SLAM method for mobile robot, integrating Lidar, IMU, and a downward vision camera. Firstly, we tightly couple Lidar-IMU and downward vision camera using a multimodal fusion framework. Downward visual measurements are integrated as QR factors into the factor graph, providing absolute pose information to correct cumulative error in the Lidar-IMU system and enable the joint pose estimation of the AGV. Secondly, an enhanced Iterative Closest Point (ICP) point cloud matching algorithm has been developed to correct the incorrect pose. This approach achieves Scan-to-Map alignment by employing a coarse-to-fine matching strategy, thereby enhancing the accuracy of point cloud alignment. Finally, the extended Kalman filter is used to optimize the Lidar pose drift, as well as the Lidar mapping effectively by using the Lidar-IMU as the measurements and the downward visual measurements as the updates. The algorithm has been extensively evaluated on the developed AGV experimental platform. The experimental results show that the position error is less than 0.02 meters and the orientation error is less than 1°, which solves the problems of long-term error accumulation and the lack of absolute pose information for AGV navigation.

关键词： Point cloud compression Visualization Laser radar Simultaneous localization and mapping Conferences Robot vision systems Measurement uncertainty

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