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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Shi, Xianyu Shao, Shiliang Wang, Ting Zhang, Qi Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of the Chinese Academy of Sciences Beijing China School of Software Shenyang University of Technology Shenyang China

ISBN: (纸本)9798350316308

Vision-based terrain recognition technology can help the robot understand the surrounding environment and then adjust the control and motion strategy in time. However, most of the existing work focuses on urban environments, and little attention is paid to complex off-road environments. Moreover, the class imbalance in the off-road environment is highly dependent on the specific environment so that the class imbalance problem is more serious, which makes the semantic segmentation of rare classes more challenging. In this study, we propose a visual terrain recognition method based on different weight loss functions for ground mobile robots in the off-road environment with class imbalance. The visual information is input into the improved network for terrain recognition. In order to better identify the rare classes, different weights are assigned to different classes in the loss function of the improved network. After a series of experiments, the results show that our improvement achieves better results than the original model in the off-road environment. © 2023 IEEE.

关键词： Mobile robots

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Squeezing More Past Knowledge for Online Class-Incremental Continual Learning

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 722-736页

作者： Da Yu Mingyi Zhang Mantian Li Fusheng Zha Junge Zhang Lining Sun Kaiqi Huang the State Key Laboratory of Robotics and System Harbin Institute of Technology(HIT)Harbin 150080China Center for Research on Intelligent System and Engineering Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Center for Research on Intelligent System and Engineering Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190the School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijing 100049 the CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031China IEEE

Continual learning(CL)studies the problem of learning to accumulate knowledge over time from a stream of data.A crucial challenge is that neural networks suffer from performance degradation on previously seen data,known as catastrophic forgetting,due to allowing parameter *** this work,we consider a more practical online class-incremental CL setting,where the model learns new samples in an online manner and may continuously experience new ***,prior knowledge is unavailable during training and *** works usually explore sample usages from a single dimension,which ignores a lot of valuable supervisory *** better tackle the setting,we propose a novel replay-based CL method,which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous ***,besides the previous raw samples,we store the corresponding logits and features in the ***,to imitate the prediction of the past model,we construct extra constraints by leveraging multi-level information stored in the *** the same number of samples for replay,our method can use more past knowledge to prevent *** conduct extensive evaluations on several popular CL datasets,and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic *** further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.

关键词： Catastrophic forgetting class-incremental learning continual learning(CL) experience replay

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Effectiveness of artificial neural network for forecasting of fracture toughness of concrete specimens

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Asian Journal of Civil Engineering 2024年第6期25卷 4697-4712页

作者： Pathak, Sudhanshu S. Vesmawala, Gaurang R. Mane, Sachin J. Sarnobat, Sandeep S. Department of Civil Engineering D Y Patil College of Engineering Akurdi Pune India Department of Civil Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat India Department of Robotics and Automation D Y Patil College of Engineering Akurdi Pune India

Since fracture energy and fracture toughness are directly linked to failure behavior, crack initiation and propagation assumes a significant importance in the study of fracture mechanics. The current study attempts to develop a prediction model for fracture toughness by employing artificial neural network (ANN). Data sets of previous research work obtained from 210 fracture tests have been used to train, test and validate the ANN Model. Experimental results are further used check the feasibility of the model. The test specimen were concrete beams made using Nano TiO₂ (1, 2, 3 and 4%) and ground granulated blast furnace slag (30%). Compressive strength, maximum aggregate size and water to cement ratio are used as input parameters while fracture toughness, tensile strength, Compressive strength and flexure strength are the measured outputs for experimentation. The experimental results indicate the fracture toughness, tensile strength, compressive strength increase with the increase in the NT percentage by 46.15, 10.22, 16.07 and 14.83% respectively. The ANN predicted values of fracture toughness are in good agreement with the experimental results. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Artificial neural network Fracture toughness Ground granulated blast furnace slag Nano TiO2

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Insulators Defect Detection for Power Grid Based on Improved Yolov7 Algorithm 13

Insulators Defect Detection for Power Grid Based on Improved...

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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Zhang, Zhiwei Zhuang, Yan Liang, Zhida Kong, Shuai Zhu, Shengyi School of Control Science and Engineering Dalian University of Technology Dalian China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Special Equipment Safety Testing Technology of Zhejiang Province Zhejiang Academy of Special Equipment Science Hangzhou China

ISBN: (纸本)9798331516147

High-current out-of-phase closed bus is an essential link in the transmission of electric energy and is widely used in thermal power stations, hydropower stations and nuclear power plants. Although these busbars have the advantages of excellent dustproof effect and strong magnetic shielding, they are susceptible to insulator aging and damage in long-term operation, posing a potential safety risk to the power grid. Due to the enclosed space inside the bus and the complexity of external maintenance tasks, manual inspection has problems such as inefficiency and low safety. In order to solve these problems, this paper proposes an improved detection algorithm based on Yolov7 for insulator defect detection in hydropower station scenarios. Because of the particularity of Yolov7 network structure and application scenarios, this paper proposes a Channel Feature Enhancement module to enhance the channel representation ability of the network. In addition, an Edge Feature Enhancement module is introduced to improve the boundary detection of insulators by enhancing the contrast between the target and the background. Considering the limitations of existing studies in specific scenarios and the scarcity of dataset, this paper constructs and accurately annotates an insulator defect detection dataset applied to hydropower station scenarios. Experimental results show that the improved-Yolov7-tiny algorithm in this paper achieves a balance between accuracy and inference speed. © 2025 IEEE.

关键词： Magnetic shielding

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Energy Management for Industrial Robots based on automationML

Energy Management for Industrial Robots based on AutomationM...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Valentyn Petrichenko Gregor Thiele Department for Process Automation and Robotics Fraunhofer Institute for Production Systems and Design Technology IPK Berlin Germany

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Motivated by political regulations and rising energy prices, companies strive for implementing energy management and for reducing their energy consumption. It is an expenditure of work for the energy manager to gather all relevant information about the facilities and machines (e.g. meaning of measurement and control signals, limits of these quantities, installed nominal capacities). In many cases, there is digital planning data from the planning, construction and commissioning phases. Using automationML, this information can be used collaboratively not only for engineering and maintenance but for energy monitoring and optimization. In this study, an industrial robot and two conveyors are integrated in the wide-spread energy management software suite EnEffCo. We document the method and results. Implications for deriving optimization formulations are discussed.

关键词： Service robots Industrial robots Software Regulation Planning Maintenance Energy management Information technology Optimization Monitoring

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Editorial:special issue on frontiers of control and automation,dedicated to *** *** 60th birthday

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Control Theory and technology 2023年第3期21卷 263-264页

作者： Jie Chen Jie Huang Lihua Xie Department of Control Science and Engineering Tongji UniversityShanghaiChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Center for Advanced Robotics Technology Innovation School of Electrical and Electronic EngineeringNanyang Technological UniversitySingaporeSingapore

It is our great pleasure and honor to organize this special issue"Frontiers of Control and automation"in honor of the 60th birthday of our long-time colleague and friend Professor Ben *** Chen obtained his *** in mathemat-ics and computer science in 1983 from Xiamen University,China;*** in electrical engineering in 1988 from Gonzaga University,Spokane,Washington,USA;and *** in electrical and computer engineering in 1991 from Washington State University,Pullman,Washington,USA.

关键词： computer Xiamen frontier

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Real-Time Plant Recognition and Crop Row Navigation for Autonomous Precision Agricultural Sprayer Robot

Real-Time Plant Recognition and Crop Row Navigation for Auto...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Fazal Nasir Muhammad Haris Bilawal Khan Muhammad Tufail Muhammad Tahir Khan Zhang Dong Advanced Robotics and Automation Lab National Center of Robotics and Automation Peshawar Pakistan Department of Mechatronics Engineering University of Engineering and Technology Peshawar Pakistan Institute of Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan China

This paper presents a vision-based selective spraying technique for an autonomous agricultural sprayer robot. In traditional methods, excessive chemical spraying cause deleterious effects on human health, environment and becomes uneconomical. In order to reduce the agrochemical wastage encounters in a broadcast spraying, a selective plant spraying method is used for crop chemical treatment. The sensor-based approach assisted with YOLOv7 model is deployed on a custom designed robot for recognizing and localizing the lettuce plants in field. The PID-based pressure controller is designed that minimizes the undesirable fluctuations cause by the opening/closing of solenoid-valve-nozzles (SVNs) during spraying. Thus the nozzle's spraying quality is maintained by keeping the pressure constant to a desired value. A visual servoing scheme for row tracking is presented that uses the detected plant's spatial features. The robustness of the visual-based navigation is validated in the real field experiments.

关键词： Service robots Plants (biology) Spraying Crops Wheels Robot sensing systems Visual servoing

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Healthcare Monitoring Companion: The Social Robot

Healthcare Monitoring Companion: The Social Robot

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Rahul Goraksha Arunkumar Bongale Sameer Sayyad Satish Kumar Department of Robotics and Automation Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Department of Robotics and Automation Symbiosis Centre for Applied Artificial Intelligence Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

There is a rising scarcity of healthcare professionals to meet the demand of taking care of aged people. Social robots are becoming more and more popular because of the benefits they provide; during the pandemic, in particular, several of their primary applications are advantageous. The purpose of this work is to build the prototype of social robots and carry out the integration of those robots into healthcare monitoring using Thing Speak and Raspberry Pi. This prototype of social robots with Thing Speak and Raspberry Pi hosts an array of sensors (for measuring a multitude of data from the body of humans) like heartbeat sensor, Dallas temperature sensor, Radio-Frequency Identification (RFID), ultrasonic sensor, and Infrared (IR) sensor along with other components like motor controlling module and DC motor. The data from these sensors are transmitted to the cloud platform Thing Speak, offering remote monitoring and data analysis capabilities and the Raspberry Pi acts as the central processing element in our design, which additionally helps in triggering corresponding alerts regarding the patient status. Finally, we experimentally validate our design using 2 cases and then we present the assessment of the patient alerting feature.

关键词： Temperature sensors Temperature measurement Ultrasonic variables measurement Social robots Prototypes Medical services Sensors

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Learning to schedule dynamic distributed reconfigurable workshops using expected deep Q-network

Learning to schedule dynamic distributed reconfigurable work...

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作者： Yang, Shengluo Wang, Junyi Xu, Zhigang School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai200093 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

Distributed manufacturing has become common in the globalized production environment. Facing the tendency of mass customization, distributed workshops are required to have the reconfiguration capability to produce multiple products. Based on the scheduling demands of distributed workshops, this paper studied the dynamic distributed reconfigurable flowshop scheduling problem (DRFSP) with new job arrivals using deep reinforcement learning (DRL) for the first time. The DRL-based intelligent scheduling and reconfiguration systems are modeled by designing efficient rewards, actions, and state features for both scheduling and reconfiguration agents. A new state feature normalization approach is proposed to address the variability in production configurations and dynamics. A novel expected deep Q-network (EDQN) algorithm is proposed to provide more precise state-action values rather than using estimations. Moreover, the DRL-based training and execution procedures for the dynamic DRFSP are provided by combining decision-making, agent learning, and reconfigurable production. Training curves validate the reasonableness of system modeling and the good learning efficiency of the EDQN algorithm. Extensive comparison experiments show that our EDQN algorithm outperforms three popular DRL algorithms and four well-known priority dispatching rules (PDRs) by around 20 % and 89 % respectively, with good generalization ability and time efficiency. The average decision time under dynamics is only 0.65 ms, which enables real-time scheduling. © 2023 Elsevier Ltd

关键词： Reinforcement learning

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An improved memetic algorithm for multi-objective resource-constrained flexible job shop inverse scheduling problem: An application for machining workshop

An improved memetic algorithm for multi-objective resource-c...

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作者： Wei, Shupeng Tang, Hongtao Li, Xixing Lei, Deming Wang, Xi Vincent School of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan China Hubei Provincial Engineering Research Center of Robotics and Intelligent Manufacturing Wuhan China School of Mechanical Engineering Hubei University of Technology Wuhan China School of Automation Wuhan University of Technology Wuhan China Department of Production Engineering KTH Royal Institute of Technology Sweden

Resource-constrained flexible job shop scheduling problems are commonly encountered in some manufacturing industries, and have been widely studied in recent years. However, traditional resource constrained flexible job shop scheduling problem rarely consider the uncertainties in actual manufacturing systems, which may make the original schedule become suboptimal or even unfeasible. Therefore, a resource constrained flexible job shop inverse scheduling problem (RCFJISP) is proposed in this paper, which aims to cope with uncertain events by simultaneously adjusting the machine, worker and process parameters of the original schedule. A multi-objective optimization model is constructed to minimize the makespan, worker cost, machine energy consumption and deviation index. Furthermore, an improved memetic algorithm (IMA) is developed for solving the proposed problem. In IMA, a novel double-layer encoding mechanism is designed to enhance the capacity in exploring new solution's domains. Three initialization strategies utilizing original scheduling information are designed to improve the quality of initial solutions. An adaptive mutation strategy and a local search mechanism are designed to enhance exploration and exploitation ability of the algorithm. And a crowding operator is proposed to reflect the diversity of the population effectively. In computational experiments, 28 extended benchmarks are constructed, and the effectiveness of the proposed strategy and algorithm is verified by comparing IMA with its 4 variants and other 4 widely used algorithms. Finally, two inverse scheduling problems of a real-world hydraulic cylinder machining workshop under two uncertain situations are studied. The results demonstrate that IMA can effectively solve the actual inverse scheduling problem. With a slight adjustment to the original scheduling, it can reduce the makespan by 11.5%, the worker cost by 8.1% and the machine energy consumption by 27.9% on average. © 2024 The Society of Ma

关键词： Multiobjective optimization

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