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Brain Cognition Mechanism-Inspired Hierarchical Navigation Method for Mobile Robots

Journal of Bionic Engineering 2024年第2期21卷 852-865页

作者： Qiang Zou chengdong wu Ming Cong Dong Liu Faculty of Robot Science and Engineering Northeastern UniversityShenyang110819China School of Mechanical Engineering Dalian University of TechnologyDalian116024China

Autonomous navigation is a fundamental problem in *** methods generally build point cloud map or dense feature map in perceptual space;due to lack of cognition and memory formation mechanism,traditional methods exist poor robustness and low cognitive *** a new navigation technology that draws inspiration from mammal’s navigation,bionic navigation method can map perceptual information into cognitive space,and have strong autonomy and environment *** improve the robot’s autonomous navigation ability,this paper proposes a cognitive map-based hierarchical navigation ***,the mammals’navigation-related grid cells and head direction cells are modeled to provide the robots with location *** then a global path planning strategy based on cognitive map is proposed,which can anticipate one preferred global path to the target with high efficiency and short ***,a hierarchical motion controlling method is proposed,with which the target navigation can be divided into several sub-target navigation,and the mobile robot can reach to these sub-targets with high confidence ***,some experiments are implemented,the results show that the proposed path planning method can avoid passing through obstacles and obtain one preferred global path to the target with high efficiency,and the time cost does not increase extremely with the increase of experience nodes *** motion controlling results show that the mobile robot can arrive at the target successfully only depending on its self-motion information,which is an effective attempt and reflects strong bionic properties.

关键词： Bionic navigation Spatial localization cells Global path planning Hierarchical motion controlling

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SGT-Net: A Transformer-Based Stratified Graph Convolutional Network for 3D Point Cloud Semantic Segmentation

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Computers, Materials & Continua 2024年第6期79卷 4471-4489页

作者： Suyi Liu Jianning Chi chengdong wu Fang Xu Xiaosheng Yu Faculty of Robot Science and Engineering Northeastern UniversityShenyang110167China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang110169China SIASUN Robot&Automation Co. Ltd.Shenyang110169China

In recent years,semantic segmentation on 3D point cloud data has attracted much *** 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and inherently ***,it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud *** current methods either focus on local feature aggregation or long-range context dependency,but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation *** this paper,we propose a Transformer-based stratified graph convolutional network(SGT-Net),which enlarges the effective receptive field and builds direct long-range ***,we first propose a novel dense-sparse sampling strategy that provides dense local vertices and sparse long-distance vertices for subsequent graph convolutional network(GCN).Secondly,we propose a multi-key self-attention mechanism based on the Transformer to further weight augmentation for crucial neighboring relationships and enlarge the effective receptive *** addition,to further improve the efficiency of the network,we propose a similarity measurement module to determine whether the neighborhood near the center point is *** demonstrate the validity and superiority of our method on the S3DIS and ShapeNet *** ablation experiments and segmentation visualization,we verify that the SGT model can improve the performance of the point cloud semantic segmentation.

关键词： 3D point cloud semantic segmentation long-range contexts global-local feature graph convolutional network dense-sparse sampling strategy

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面向协作机器人的零力控制与碰撞检测方法研究

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工程科学与技术 2024年第1期56卷 1-10页

作者：赵彬吴成东孙若怀姜杨吴兴茂东北大学信息科学与工程学院辽宁沈阳110819 新松机器人自动化股份有限公司辽宁沈阳110168 东北大学机器人科学与工程学院辽宁沈阳110169

在3C(计算机、通信和消费电子)行业中,对协作机器人的安全、交互、精度、柔顺等方面有严格要求。为了解决协作机器人柔顺交互控制问题,对机器人的零力控制和碰撞检测方法进行了深入研究。首先,建立了一种分析冗余协作机器人牛顿−穆尔彭... 详细信息

在3C(计算机、通信和消费电子)行业中,对协作机器人的安全、交互、精度、柔顺等方面有严格要求。为了解决协作机器人柔顺交互控制问题,对机器人的零力控制和碰撞检测方法进行了深入研究。首先,建立了一种分析冗余协作机器人牛顿−穆尔彭罗斯(Newton−MP)通用的逆运动学算法,将逆运动学问题转化为Newton−MP法的迭代求解问题。其次,针对协作机器人的零力控制问题,通过同时考虑摩擦力形成完整动力学方程。同时,建立基于加速度3次摩擦力模型的完全动力学方程,采用遗传算法对摩擦力模型进行多参数辨识。再次,提出基于One-class卷积神经网络的碰撞检测方法,构建无碰撞数据集。One-class卷积神经网络在特征空间中引入伪负高斯数据,并使用2元交叉熵损失对网络进行了训练。One-class卷积神经网络碰撞检测方法成功地补偿了模型不确定的动态影响,解决了传统碰撞检测方法建模不准确的问题。最后,通过实验证明,提出的Newton−MP优化方法具有良好的性能,绝对误差达到0.00013 mm。与理想摩擦力模型进行对比,采用基于速度的3次摩擦力模型拟合出的摩擦力能够更好适用于零力控制。将外力矩观测器与One-class卷积神经网络碰撞检测进行优缺点分析,可以证明,One-class卷积神经网络可以在不依靠模型的情况下,准确地检测机器人的异常碰撞。

关键词：动力学协作机器人 One-class卷积神经网络摩擦参数辨识

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Modeling of nano piezoelectric actuator based on block matching algorithm with optimal block size

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Science China(Technological Sciences) 2013年第11期56卷 2649-2657页

作者： WEI YangJie wu chengdong College of Information Science & Engineering Northeastern University

In order to model the hysteresis behavior of a nano piezoelectric actuator(PA)on nano scale in a real time system,a new hysteresis modeling method based on an improved sub-pixel blocking matching algorithm with an optimal block size is proposed in this ***,Preisach model is introduced to model the hysteresis behavior of a piezoelectric ***,a real time block matching algorithm is researched and its block size is optimized with a standard ***,experiments are performed with respect to a nanometer movement platform system,and the results show the feasibility and validity of the sub-pixel estimation based block matching algorithm and its application in modeling the hysteresis behavior of PA.

关键词： piezoelectric actuator hysteresis modeling optimal block size nano

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An automatic compensation method on compression effect and surface elasticity measurement based on the deflection signal of AFM

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Science China(Technological Sciences) 2011年第9期54卷 2397-2403页

作者： WEI YangJie wu chengdong DONG ZaiLi Graduate School of Chinese Academy of Sciences & the State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China School of lnformation Science & Engineering Northeast University Shenyang 110004 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China

Atomic force microscope （AFM）, as an important instrument in micro/nano operation, has been widely used to measure sampie＇s height information. However, the so called compression effect, due to force aroused from the contact of AFM tip with a sample surface, would result in imprecision of the surface＇s height measurement, i.e., the measured height is lower than expected. Up to now, there is not any effective and rapid method to attenuate this kind of measurement error. Thus, in this paper, an algorithm to obtain high accurate height measurement is proposed. Firstly, the concept of force curve is used to analyze the basic principle of the compression effect. Secondly, an automatic compensation method by fusing the height signal and the deflection signal is proposed. The proposed algorithm can also be used to obtain a surface elasticity image. Finally, in order to validate the proposed method, two experiments are conducted with respect to mufti-wall nano-carbon tubes on a silicon substrate and graphemes on a mica substrate.

关键词： atomic force microscope (AFM) compression effect information fusion surface elasticity

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Global shape reconstruction of nano grid with singly fixed camera

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Science China(Technological Sciences) 2011年第4期54卷 1044-1052页

作者： WEI YangJie wu chengdong DONG ZaiLi Graduate School of Chinese Academy of Sciences & the State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China School of Information Science & Engineering Northeast UniversityShenyang 110004China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China

Depth from defocus(DFD),as a typical shape reconstruction method,has been widely researched in most recent ***,all the existing DFD algorithms require at least two defocused images with different camera ***,in micro/nano manipulation,any change on visual sensor's parameters is absolutely ***,a novel DFD method to reconstruct the shape of a nano grid on micro/nano scale is researched in this ***,the blurring imaging model is constructed with the relative blurring and the diffusion ***,the relationship between depth and blurring is discussed from four ***,depth measurement problem is transformed into an optimization issue which is solved using the gradient flow ***,experiment results and error analysis are conducted to show the feasibility and effectiveness of the proposed method.

关键词： depth from defocus diffusion equation shape reconstruction nano grid

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Clinical Knowledge-Based Hybrid Swin Transformer for Brain Tumor Segmentation

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Computers, Materials & Continua 2023年第9期76卷 3797-3811页

作者： Xiaoliang Lei Xiaosheng Yu Hao wu chengdong wu Jingsi Zhang College of Information Science and Engineering Northeastern UniversityShenyang110819China Faculty of Robot Science and Engineer Northeastern UniversityShenyang110819China Faculty of Engineering Macquarie UniversitySydneyNSW2109Australia

Accurate tumor segmentation from brain tissues in Magnetic Resonance Imaging(MRI)imaging is crucial in the pre-surgical planning of brain tumor *** images’heterogeneous intensity and fuzzy boundaries make brain tumor segmentation ***,recent studies have yet to fully employ MRI sequences’considerable and supplementary information,which offers critical a priori *** paper proposes a clinical knowledge-based hybrid Swin Transformermultimodal brain tumor segmentation algorithmbased on how experts identify malignancies from MRI *** the encoder phase,a dual backbone network with a Swin Transformer backbone to capture long dependencies from 3D MR images and a Convolutional Neural Network(CNN)-based backbone to represent local features have been *** of directly connecting all the MRI sequences,the proposed method re-organizes them and splits them into two groups based on MRI principles and characteristics:T1 and T1ce,T2 and *** aggregated images are received by the dual-stem Swin Transformer-based encoder branch,and the multimodal sequence-interacted cross-attention module(MScAM)captures the interactive information between two sets of linked modalities in each *** the CNN-based encoder branch,a triple down-sampling module(TDsM)has been proposed to balance the performance while *** the final stage of the encoder,the feature maps acquired from two branches are concatenated as input to the decoder,which is constrained by MScAM *** proposed method has been evaluated on datasets from the MICCAI BraTS2021 *** results of the experiments demonstrate that the method algorithm can precisely segment brain tumors,especially the portions within tumors.

关键词： Brain tumor segmentation swin transformer multimodal clinical knowledge

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几种重要热液矿床成矿的岩浆作用贡献探讨

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地球学报 2024年第3期45卷 337-348页

作者：曾钦旺石少华钱滔吴承东潘军华魏元泵陈雨林湖南省地质院湖南长沙410014 湖南省地质调查所湖南长沙410014 湖南省核地质调查所湖南长沙410007 湖南省地球物理地球化学调查所湖南长沙410014 湖南省生态地质调查监测所湖南长沙410119

本文运用地球科学相关基础学科的基本理论知识,结合地质工作实践认识和矿床地质特征分析,对几种重要热液矿床成矿的岩浆作用贡献进行分析探讨,提出了新的认识。研究结果认为:(1)地下水是通过补给和排泄交替循环的,因此,通过简单的地下... 详细信息

本文运用地球科学相关基础学科的基本理论知识,结合地质工作实践认识和矿床地质特征分析,对几种重要热液矿床成矿的岩浆作用贡献进行分析探讨,提出了新的认识。研究结果认为:(1)地下水是通过补给和排泄交替循环的,因此,通过简单的地下水的循环逐步转化为含矿热液成矿是难以实现的,地下水层控热液矿床成矿流体往往是经历了深部岩浆房驱动的热水循环。(2)地下卤水不是形成内生多金属矿床的矿源体,其本身不具有形成多金属矿床的成矿物质条件,产于板缘地热异常带内的热卤水中的多金属元素,是板块活动引起的强烈的岩浆-火山活动所提供,卤水起到了活化、富集和搬运金属元素的作用。(3)MVT铅锌矿床和卡林型金矿床的矿床特征本身说明它们的成矿与岩浆热液有成因关系。(4)根据变质作用和变质岩的定义,所有的变化是在固态条件下进行的,变质作用和变质岩难以形成变质热液矿床,其成因类型可归为岩浆热液矿床。(5)韧性剪切带是岩浆热力作用和构造应力作用下的塑性变形,成矿是岩浆热液在后期脆性断裂中充填交代而成。对矿床成因的正确认识,可有效的指导找矿勘查工作。岩浆成矿作用是最广泛、最强烈的成矿作用之一。

关键词：地下水层控热液矿床地下卤水多金属矿床 MVT铅锌矿床卡林型金矿床变质热液矿床韧性剪切带型金矿床

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Neural network equivalent model for highly efficient massive data classification

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Science China(Information Sciences) 2023年第3期66卷 287-288页

作者： Siquan YU Zhi HAN Yandong TANG chengdong wu School of Information Science and Engineering Northeastern University Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Faculty of Robot Science and Engineering Northeastern University

Dear editor, Recently, neural networks(NNs) have been successfully applied to massive data classification tasks [1, 2]. However,there is an inconsistent problem in neural network structure and convergence [3]. On one hand, under the overparameterize assumption, it requires to design a complex network to guarantee the convergence [4].

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Active tension optimal control for WT wheelchair robot by using a novel control law for holonomic or nonholonomic systems

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Science China(Information Sciences) 2014年第11期57卷 208-222页

作者： WANG Jian WANG Ting YAO Chen LI XiaoFan wu chengdong State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences School of Information Science and Engineering Northeastern University

The characteristics of interaction between WT wheelchair robot and stair environments is analyzed and possible patterns of WT wheelchair robot during the stair-climbing process are summarized, with criteria to determine the pattern of the wheelchair robot proposed. Aiming at the complicated mechanism of WT wheelchair robot with holonomic constraints and combining it with the dynamic programming, namely the Hamilton-Jacobi equation, a new control law called active tension optimal control is presented for holonomic or nonholonomic robotic systems, based on which one can make the wheelchair robot with a holonomic or nonholonomic mechanism track the expected reference input of constraint forces of holonomic or nonholinomic constraints as well as track the expected reference input of the generalized coordinate of each joint. The module STATEFLOW in MATLAB is used to simulate the entire stair-climbing process of WT wheelchair robot, and comparison is made between the output curves of each joint and the tension of the track and the expected reference input curves, which verifies the effectiveness of the proposed control law.

关键词： wheelchair robot stair-climbing holonomic or nonholonomic systems active tension optimal control

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