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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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人形机器人技术与产业发展研究

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中国工程科学 2025年第1期27卷 150-167页

作者：徐程浩王耀南莫洋朱青湖南大学电气与信息工程学院长沙410082 机器人视觉感知与控制技术国家工程研究中心长沙410082

随着人工智能、高端制造、新材料等技术的不断融合与突破,我国人形机器人产业迎来爆发式增长,技术创新和政策支持推动形成了多元化竞争格局,但人形机器人产业发展仍面临核心技术差距大、规模化量产难、商业化落地难等问题。本文在介绍... 详细信息

随着人工智能、高端制造、新材料等技术的不断融合与突破,我国人形机器人产业迎来爆发式增长,技术创新和政策支持推动形成了多元化竞争格局,但人形机器人产业发展仍面临核心技术差距大、规模化量产难、商业化落地难等问题。本文在介绍人形机器人主要细分领域与技术前沿的基础上,深入分析了全球人形机器人在政策、技术、产业布局方面的发展现状与趋势,梳理了我国人形机器人技术与产业发展态势,总结了我国人形机器人发展面临的机遇与挑战,从技术创新、定点示范、法律法规完善、政策引导等方面提出了具体发展路径。研究建议,鼓励核心技术突破、强化产业布局、建设人形机器人基础设施、实施示范性工程,推动我国人形机器人产业克服技术难题、完善产业生态、实现大规模量产和商业化落地,提升我国人形机器人产业领域的全球竞争力。

关键词：人形机器人核心零部件大模型具身智能

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Parameter selection of support vector machine for function approximation based on chaos optimization

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Journal of Systems Engineering and Electronics 2008年第1期19卷 191-197页

作者： Yuan Xiaofang wang yaonan Coll. of Electrical & Information Engineering Hunan Univ. Changsha 410082 P. R. China

The support vector machine （SVM） is a novel machine learning method, which has the ability to approximate nonlinear functions with arbitrary accuracy. Setting parameters well is very crucial for SVM learning results and generalization ability, and now there is no systematic, general method for parameter selection. In this article, the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal paraxneter values. The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy. Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.

关键词： learning systems support vector machines (SVM) approximation theory parameter selection optimization.

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New mixed broadcast scheduling approach using neural networks and graph coloring in wireless sensor network

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Journal of Systems Engineering and Electronics 2009年第1期20卷 185-191页

作者： Zhang Xizheng wang yaonan Dept. of Computer Science Hunan Inst. of Engineering Xiangtan 411104 P. R. China School of Electrical and Information Engineering Hunan Univ. Changsha 410082 P. R. China

Due to the mutual interference and sharing of wireless links in TDMA wireless sensor networks, conflicts will occur when data messages are transmitting between nodes. The broadcast scheduling problem （BSP） is aimed to schedule each node in different slot of fixed length frame at least once, and the objective of BSP is to seek for the optimal feasible solution, which has the shortest length of frame slots, as well as the maximum node transmission. A two-stage mixed algorithm based on a fuzzy Hopfield neural network is proposed to solve this BSP in wireless sensor network. In the first stage, a modified sequential vertex coloring algorithm is adopted to obtain a minimal TDMA frame length. In the second stage, the fuzzy Hopfleld network is utilized to maximize the channel utilization ratio. Experimental results, obtained from the running on three benchmark graphs, show that the algorithm can achieve better performance with shorter frame length and higher channel utilizing ratio than other exiting BSP solutions.

关键词： wireless sensor network broadcast scheduling fuzzy Hopfield network graph coloring.

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High-Order Control Barrier Function-Based Safety Control of Constrained Robotic Systems:An Augmented Dynamics Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2487-2496页

作者： Haijing wang Jinzhu Peng Fangfang Zhang yaonan wang the School of Electrical and Information Engineering Zhengzhou University the National Engineering Laboratory for Robot Visual Perception and Control Hunan University the College of Electrical and Information Engineering Hunan University

Although constraint satisfaction approaches have achieved fruitful results, system states may lose their smoothness and there may be undesired chattering of control inputs due to switching characteristics. Furthermore, it remains a challenge when there are additional constraints on control torques of robotic systems. In this article, we propose a novel high-order control barrier function(HoCBF)-based safety control method for robotic systems subject to input-output constraints, which can maintain the desired smoothness of system states and reduce undesired chattering vibration in the control torque. In our design, augmented dynamics are introduced into the HoCBF by constructing its output as the control input of the robotic system,so that the constraint satisfaction is facilitated by HoCBFs and the smoothness of system states is maintained by the augmented dynamics. This proposed scheme leads to the quadratic program(QP), which is more user-friendly in implementation since the constraint satisfaction control design is implemented as an add-on to an existing tracking control law. The proposed closed-loop control system not only achieves the requirements of real-time capability, stability, safety and compliance, but also reduces undesired chattering of control inputs. Finally, the effectiveness of the proposed control scheme is verified by simulations and experiments on robotic manipulators.

关键词： Augmented dynamics high-order control barrier function (HoCBF) input-output constraints quadratic program (QP) robotic systems

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Effective method for tracking multiple objects in real-time visual surveillance systems

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Journal of Systems Engineering and Electronics 2009年第6期20卷 1167-1178页

作者： wang yaonan Wan Qin Yu Hongshan Coll. of Electrical and Information Engineering Hunan Univ. Changsha 410082 P. R. China

An object model-based tracking method is useful for tracking multiple objects, but the main difficulties are modeling objects reliably and tracking objects via models in successive frames. An effective tracking method using the object models is proposed to track multiple objects in a real-time visual surveillance system. Firstly, for detecting objects, an adaptive kernel density estimation method is utilized, which uses an adaptive bandwidth and features combining colour and gradient. Secondly, some models of objects are built for describing motion, shape and colour features. Then, a matching matrix is formed to analyze tracking situations. If objects are tracked under occlusions, the optimal ＂visual＂ object is found to represent the occluded object, and the posterior probability of pixel is used to determine which pixel is utilized for updating object models. Extensive experiments show that this method improves the accuracy and validity of tracking objects even under occlusions and is used in real-time visual surveillance systems.

关键词： visual surveillance multiple object tracking object model matching matrix.

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Robust dynamic surface control of flexible joint robots using recurrent neural networks

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控制理论与应用（英文版） 2013年第2期11卷 222-229页

作者： Zhiqiang MIAO yaonan wang College of Electrical and Information Engineering Hunan University

A robust neuro-adaptive controller for uncertain flexible joint robots is presented. This control scheme integrates H^infinity disturbance attenuation design and recurrent neural network adaptive control technique into the dy- namic surface control framework. Two recurrent neural networks are used to adaptively learn the uncertain functions in a flexible joint robot. Then, the effects of approximation error and filter error on the tracking performance are attenuated to a prescribed level by the embedded H-infinity controller, so that the desired H-infinity tracking performance can be achieved. Finally. simulation results verifv the effectiveness of the nronosed control scheme.

关键词： Dynamic surface control Flexible joint robots Robust H-infinity control Recurrent neural network

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Boundary Gap Based Reactive Navigation in Unknown Environments

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 468-477页

作者： Zhao Gao Jiahu Qin Shuai wang yaonan wang the Department of Automation University of Science and Technology of ChinaHefei 230027China the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha 410082China

Due to the requirements for mobile robots to search or rescue in unknown environments,reactive navigation which plays an essential role in these applications has attracted increasing ***,most existing reactive methods are vulnerable to local minima in the absence of prior knowledge about the *** paper aims to address the local minimum problem by employing the proposed boundary gap(BG)based reactive navigation ***,the narrowest gap extraction algorithm(NGEA)is proposed to eliminate the improper ***,we present a new concept called boundary gap which enables the robot to follow the obstacle boundary and then get rid of local ***,in order to enhance the smoothness of generated trajectories,we take the robot dynamics into consideration by using the modified dynamic window approach(DWA).Simulation and experimental results show the superiority of our method in avoiding local minima and improving the smoothness.

关键词： Boundary gap local minima reactive navigation unknown environments

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Robust fuzzy control of Takagi-Sugeno fuzzy neural networks with discontinuous activation functions and time delays

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Journal of Systems Engineering and Electronics 2011年第3期22卷 473-481页

作者： yaonan wang Xiru Wu Yi Zuo College of Electrical and Information Engineering Hunan UniversityChangsha 410082P.R.China School of Energy and Power Engineering Changsha University of Science and TechnologyChangsha 410004P.R.China

The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks（TSFNN） with discontinuous activation functions and time delays is investigated by using Lyapunov stability *** on linear matrix inequalities（LMIs）,we originally propose robust fuzzy control to guarantee the global robust asymptotical stability of *** with the existing literature,this paper removes the assumptions on the neuron activations such as Lipschitz conditions,bounded,monotonic increasing property or the right-limit value is bigger than the left one at the discontinuous ***,the results are more general and ***,two numerical examples are given to show the effectiveness of the proposed stability results.

关键词： delayed neural network global robust asymptotical stability discontinuous neuron activation linear matrix inequality(LMI) Takagi-sugeno(T-S) fuzzy model.

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Adaptive-backstepping force/motion control for mobile-manipulator robot based on fuzzy CMAC neural networks

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Control Theory and Technology 2014年第4期12卷 368-382页

作者： Thang-Long MAI yaonan wang College of Electrical and Information Engineering Hunan University Faculty of Electronics Engineering Industrial University of Hochiminh City

In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control （ABFCNC） system for motion/force control of the mobile-manipulator robot （MMR） is proposed. By applying the ABFCNC in the tracking-position controller, the unknown dynamics and parameter variation problems of the MMR control system are relaxed. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, uncertain disturbances. Based on the tracking position-ABFCNC design, an adaptive robust control strategy is also developed for the nonholonomicconstraint force of the MMR. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. Therefore, the proposed method proves that it not only can guarantee the stability and robustness but also the tracking performances of the MMR control system. The effectiveness and robustness of the proposed control system are verified by comparative simulation results.

关键词： Backstepping control Fuzzy CMAC (cerebellar model articulation controller) neural networks Adaptive robustcontrol Mobile-manipulator robot

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