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Key Technologies for Machine Vision for Picking Robots:Review and Benchmarking

Machine intelligence research 2025年第1期22卷 2-16页

作者： Xu Xiao Yiming Jiang Yaonan Wang College of Electrical and Information engineering Hunan UniversityChangsha 410082China National engineering research center for Robot Vision perception and control Technology Hunan UniversityChangsha 410082China

The increase in precision agriculture has promoted the development of picking robot technology,and the visual recognition system at its core is crucial for improving the level of agricultural *** paper reviews the progress of visual recognition tech-nology for picking robots,including image capture technology,target detection algorithms,spatial positioning strategies and scene *** article begins with a description of the basic structure and function of the vision system of the picking robot and em-phasizes the importance of achieving high-efficiency and high-accuracy recognition in the natural agricultural ***-sequently,various image processing techniques and vision algorithms,including color image analysis,three-dimensional depth percep-tion,and automatic object recognition technology that integrates machine learning and deep learning algorithms,were *** the same time,the paper also highlights the challenges of existing technologies in dynamic lighting,occlusion problems,fruit maturity di-versity,and real-time processing *** paper further discusses multisensor information fusion technology and discusses methods for combining visual recognition with a robot control system to improve the accuracy and working rate of *** the same time,this paper also introduces innovative research,such as the application of convolutional neural networks(CNNs)for accurate fruit detection and the development of event-based vision systems to improve the response speed of the *** the end of this paper,the future development of visual recognition technology for picking robots is predicted,and new research trends are proposed,including the refinement of algorithms,hardware innovation,and the adaptability of technology to different agricultural *** purpose of this paper is to provide a comprehensive analysis of visual recognition technology for researchers and practitioners in the field of agricul-tural rob

关键词： Picking robot visual system perception technology image processing machine learning deep learning.

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Adaptive control of a Flexible Manipulator With Unknown Hysteresis and Intermittent Actuator Faults

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 148-158页

作者： Shouyan Chen Weitian He Zhijia Zhao Yun Feng Zhijie Liu Keum-Shik Hong School of Mechanical and Electrical engineering Guangzhou University the School of control Science and engineering Shandong University IEEE the College of Electrical and Information engineering Hunan University the National engineering research center for Robot Visual perception and control Technology Hunan University the School of intelligence Science and Technology University of Science and Technology Beijing (USTB) the Institute for Future the School of AutomationQingdao University the School of Mechanical engineering Pusan National University

In this study, we consider a single-link flexible manipulator in the presence of an unknown Bouc-Wen type of hysteresis and intermittent actuator faults. First, an inverse hysteresis dynamics model is introduced, and then the control input is divided into an expected input and an error compensator. Second,a novel adaptive neural network-based control scheme is proposed to cancel the unknown input hysteresis. Subsequently,by modifying the adaptive laws and local control laws, a fault-tolerant control strategy is applied to address uncertain intermittent actuator faults in a flexible manipulator system. Through the direct Lyapunov theory, the proposed scheme allows the state errors to asymptotically converge to a specified interval. Finally,the effectiveness of the proposed scheme is verified through numerical simulations and experiments.

关键词： Adaptive control flexible manipulator intermittent actuator faults inverse hysteresis dynamics vibration control

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Penalty Function-Based Distributed Primal-Dual Algorithm for Nonconvex Optimization Problem

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 394-402页

作者： Xiasheng Shi Changyin Sun IEEE the engineering research center of autonomous Unmanned System Technology Ministry of Education Anhui University the School of Artificial intelligence Anhui University Anhui Provincial engineering research center for Unmanned System and Intelligent Technology Anhui University the Anhui Key Laboratory Industrial Energy-Saving and Safety Anhui University

This paper addresses the distributed nonconvex optimization problem, where both the global cost function and local inequality constraint function are nonconvex. To tackle this issue, the p-power transformation and penalty function techniques are introduced to reframe the nonconvex optimization problem. This ensures that the Hessian matrix of the augmented Lagrangian function becomes local positive definite by choosing appropriate control parameters. A multi-timescale primal-dual method is then devised based on the Karush-Kuhn-Tucker(KKT) point of the reformulated nonconvex problem to attain convergence. The Lyapunov theory guarantees the model's stability in the presence of an undirected and connected communication network. Finally, two nonconvex optimization problems are presented to demonstrate the efficacy of the previously developed method.

关键词： Constrained optimization Karush-Kuhn-Tucker (KKT) point nonconvex p-power transformation

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Semi-autonomous Navigation Based on Local Semantic Map for Mobile Robot

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 27-33页

作者： ZHAO Yanfei XIAO Peng WANG Jingchuan GUO Rui Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai200240China State Key Laboratory of System control and Information Processing Ministry of Education of ChinaShanghai200240China Shanghai engineering research center of Intelligent control and Management Shanghai200240China State Grid intelligence Technology Co. Ltd.Jinan250013China

Mobile robots represented by smart wheelchairs can assist elderly people with mobility *** paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior *** order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic *** last,comparative experiments were carried out in the real *** show that our method is superior in terms of safety,comfort and docking accuracy.

关键词： semi-autonomous navigation mobile robot semantic map voice interaction

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Dissolved oxygen concentration control in wastewater treatment process based on reinforcement learning

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Science China(Technological Sciences) 2023年第9期66卷 2549-2560页

作者： DU ShengLi CHEN PeiXi HAN HongGui QIAO JunFei School of Artificial intelligence and Automation Beijing University of TechnologyBeijing 100124China engineering research center of Intelligent perception and autonomous control Ministry of EducationBeijing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China

In this article, the dissolved oxygen(DO) concentration control problem in wastewater treatment process(WWTP) is *** existing control strategies that control DO concentration at a fixed value, here we develop a different control *** the proposed control framework, an intelligent control method of DO concentration based on reinforcement learning(RL)algorithm is presented to resolve the DO concentration control problem. By using the deep deterministic policy gradient(DDPG)algorithm, the DO concentration of the fifth tank in the activated sludge reactor can be adjusted dynamically. In addition, by designing two different reward functions and by analysing the relationships among effluent quality, energy consumption, and DO concentration, the target of energy-saving and emission-reducing is achieved. The simulation results indicate that the designed control method can reduce energy consumption while ensuring that the effluent quality meet the specified standards.

关键词： dissolved oxygen concentration wastewater treatment process intelligent control reinforcement learning energy-saving and emission-reducing

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Prediction of NO_(x)concentration using modular long short-term memory neural network for municipal solid waste incineration

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Chinese Journal of Chemical engineering 2023年第4期56卷 46-57页

作者： Haoshan Duan Xi Meng Jian Tang Junfei Qiao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China Beijing Key Laboratory of Computational intelligence and Intelligent System Beijing 100124China engineering research center of intelligence perception and autonomous control Ministry of Education Beijing 100124China

Air pollution control poses a major problem in the implementation of municipal solid waste incineration(MSWI).Accurate prediction of nitrogen oxides(NO_(x))concentration plays an important role in efficient NO_(x)emission *** this study,a modular long short-term memory(M-LSTM)network is developed to design an efficient prediction model for NO_(x)***,the fuzzy C means(FCM)algorithm is utilized to divide the task into several sub-tasks,aiming to realize the divide-and-conquer ability for complex ***,long short-term memory(LSTM)neural networks are applied to tackle corresponding sub-tasks,which can improve the prediction accuracy of the ***,a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application ***,after being evaluated by a benchmark simulation,the proposed method is applied to a real MSWI *** the experimental results demonstrate the considerable prediction ability of the M-LSTM network.

关键词： Municipal solid waste incineration NO_(x)concentration prediction Modular neural network Model

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A Perspective-Aware Cyclist Image Generation Method for perception Development of autonomous Vehicles

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Computers, Materials & Continua 2025年第2期82卷 2687-2702页

作者： Beike Yu Dafang Wang Xing Cui Bowen Yang School of Mechatronics engineering Harbin Institute of TechnologyWeihai264200China China North Artificial intelligence&Innovation research Institute Beijing100000China

Realistic urban scene generation has been extensively studied for the sake of the development of autonomous vehicles. However, the research has primarily focused on the synthesis of vehicles and pedestrians, while the generation of cyclists is rarely presented due to its complexity. This paper proposes a perspective-aware and realistic cyclist generation method via object retrieval. Images, semantic maps, and depth labels of objects are first collected from existing datasets, categorized by class and perspective, and calculated by an algorithm newly designed according to imaging principles. During scene generation, objects with the desired class and perspective are retrieved from the collection and inserted into the background, which is then sent to the modified 2D synthesis model to generate images. This pipeline introduces a perspective computing method, utilizes object retrieval to control the perspective accurately, and modifies a diffusion model to achieve high fidelity. Experiments show that our proposal gets a 2.36 Fréchet Inception Distance, which is lower than the competitive methods, indicating a superior realistic expression ability. When these images are used for augmentation in the semantic segmentation task, the performance of ResNet-50 on the target class can be improved by 4.47%. These results demonstrate that the proposed method can be used to generate cyclists in corner cases to augment model training data, further enhancing the perception capability of autonomous vehicles and improving the safety performance of autonomous driving technology.

关键词： Realistic cyclist generation perspective-aware image synthesis autonomous vehicle artificial intelligence

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Event-triggered-based self-organizing fuzzy neural network control for the municipal solid waste incineration process

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Science China(Technological Sciences) 2023年第4期66卷 1096-1109页

作者： HE HaiJun MENG Xi TANG Jian QIAO JunFei Faculty of Information Technology Beijing University of TechnologyBejing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China Beijing Key Laboratory of Computational intelligence and Intelligent System Bejing 100124China engineering research center of intelligence perception and autonomous control Ministry of Education Bejing 100124China Xi'an Institute of Electromechanical Information Technology Xi'an 710065China

Due to the large uncertainty in the municipal solid waste incineration(MSWI)process,the furnace temperature of the MSWI process is difficult to control and the controller is updated *** improve the accuracy and reduce the number of controller updates,a novel event-triggered control method based correntropy self-organizing TS fuzzy neural network(ETCSTSFNN)is ***,the neurons of the rule layer are grown or pruned adaptively based on activation intensity and control error to meet the dynamic change of the actual operating ***,the performance index is designed based on the correntropy of tracking errors,and the parameters of the controller are adjusted by gradient descent ***,a fixed threshold event-triggered condition is designed to determine whether the current controller is updated or *** stability of the control system is proved based on the Lyapunov stability ***,the furnace temperature control experiments are conducted based on the actual data of a municipal solid waste incineration plant in *** experimental results show that the proposed ET-CSTSFNN controller shows a better control performance,which can reduce the number of the controller update significantly while achieving accurate furnace temperature control compared with other traditional control methods.

关键词： municipal solid waste incineration furnace temperature fuzzy control event-triggered

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Brain-inspired multimodal approach for effluent quality prediction using wastewater surface images and water quality data

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Frontiers of Environmental Science & engineering 2024年第3期18卷 69-82页

作者： Junchen Li Sijie Lin Liang Zhang Yuheng Liu Yongzhen Peng Qing Hu School of Environment Harbin Institute of TechnologyHarbin 150090China School of Environmental Science and engineering Southern University of Science and TechnologyShenzhen 518055China engineering Innovation center of SUSTech(Beijing) Southern University of Science and TechnologyBeijing 100083China Faculty of Environment and Life Beijing University of TechnologyBeijing 100124China engineering research center of intelligence perception and autonomous control Ministry of EducationBeijing 100124China

Efficiently predicting effluent quality through data-driven analysis presents a significant advancement for consistent wastewater treatment *** this study,we aimed to develop an integrated method for predicting effluent COD and NH3 *** employed a 200 L pilot-scale sequencing batch reactor(SBR)to gather multimodal data from urban sewage over 40 *** we collected data on critical parameters like COD,DO,pH,NH_(3),EC,ORP,SS,and water temperature,alongside wastewater surface images,resulting in a data set of approximately 40246 *** we proposed a brain-inspired image and temporal fusion model integrated with a CNN-LSTM network(BITF-CL)using this *** innovative model synergized sewage imagery with water quality data,enhancing prediction *** a result,the BITF-CL model reduced prediction error by over 23%compared to traditional methods and still performed comparably to conventional techniques even without using DO and SS sensor ***,this research presents a cost-effective and precise prediction system for sewage treatment,demonstrating the potential of brain-inspired models.

关键词： Wastewater treatment system Water quality prediction Data driven analysis Brain-inspired model Multimodal data Attention mechanism

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Privacy-Preserving Average Consensus Algorithm Under Round-Robin Scheduling Protocol

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1705-1707页

作者： Yingjiang Guo Wenying Xu Haodong Wang Jianquan Lu Shengli Du the School of Mathematics Southeast UniversityNanjing 21189China the School of Electronic Information and Electrical engineering Chengdu UniversityChengdu 610106Chengdu 610106China the School of Artificial intelligence and Automation Beijing University of TechnologyBeijing 100124 the engineering research center of Intelligent perception and autonomous control Ministry of EducationBeijing 100124China

Dear Editor,Over the past decades,cooperative control and distributed optimization have gained significant research attention due to their broad applications such as signal processing,robotics,and social networks[1],[2].As a fundamental component of distributed control and optimization,the issue of average consensus has become a recurring topic of interest[3],[4].

关键词： optimization Round Robin

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