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Mole-inspired Forepaw Design and Optimization Based on Resistive Force Theory

Journal of Bionic engineering 2025年第1期22卷 171-180页

作者： Tao Zhang Zhaofeng Liang Hongmin Zheng Zibiao Chen Kunquan Zheng Ran Xu Jiabin Liu Haifei Zhu Yisheng Guan Kun Xu Xilun Ding School of Mechanical Engineering and Automation Beihang UniversityBeijing100191China School of Electro-mechanical Engineering Guangdong University of TechnologyGuangzhou510006China

Moles exhibit highly effective capabilities due to their unique body structures and digging techniques,making them ideal models for biomimetic ***,a major challenge for mole-inspired robots lies in overcoming resistance in granular media when burrowing with *** the absence of effective forepaw design strategies,most robotic designs rely on increased power to enhance *** address this issue,this paper employs Resistive Force Theory to optimize mole-inspired forepaws,aiming to enhance burrowing *** analyzing the relationship between geometric parameters and burrowing forces,we propose several forepaw design *** granular resistance assessments,an effective forepaw configuration is identified and further refined using parameters such as longitudinal and transverse ***,the Particle Swarm Optimization algorithm is applied to determine the optimal forepaw *** force-loading tests,the optimized forepaw demonstrated a 79.44%reduction in granular lift force and a 22.55%increase in propulsive force compared with the control *** robotic burrowing experiments,the optimized forepaw achieved the longest burrow displacement(179.528 mm)and the lowest burrowing lift force(0.9355 mm/s),verifying its effectiveness in reducing the lift force and enhancing the propulsive force.

关键词： Resistive force theory Mole-inspired forepaw design Structural optimization Bioinspired robot

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Evaluating Effect of Magnetic Field on Nusselt Number and Friction Factor of Fe_(3)O_(4)-TiO_(2)/Water Nanofluids in Heat-Sink Using Artificial Intelligence Techniques

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Frontiers in Heat and Mass Transfer 2025年第1期23卷 131-162页

作者： L.S.Sundar Sérgio M.O.Tavares António M.B.Pereira Antonio C.M.Sousa Centre for Mechanical Technology and Automation Department of Mechanical EngineeringUniversity of AveiroAveiro3810-131Portugal

The experimental analysis takes too much time-consuming process and requires considerable effort,while,the Artificial Neural Network(ANN)algorithms are simple,affordable,and fast,and they allow us to make a relevant analysis in establishing an appropriate relationship between the input and output *** paper deals with the use of back-propagation ANN algorithms for the experimental data of heat transfer coefficient,Nusselt number,and friction factor of water-based Fe_(3)O_(4)-TiO_(2) magnetic hybrid nanofluids in a mini heat sink under magnetic *** data considered for the ANN network is at different Reynolds numbers(239 to 1874),different volume concentrations(0%to 2.0%),and different magnetic fields(250 to 1000 G),*** types of ANN back-propagation algorithms Levenberg-Marquardt(LM),Broyden-Fletcher-Goldfarb-Shanno Quasi Newton(BFGS),and Variable Learning Rate Gradient Descent(VLGD)were used to train the heat transfer coefficient,Nusselt number,and friction factor data,*** ANOVA t-test analysis was also performed to determine the relative accuracy of the three ANN *** Nusselt number of 2.0%*** Fe_(3)O_(4)-TiO_(2) hybrid nanofluid is enhanced by 38.16%without a magnetic field,and it is further enhanced by 88.93%with the magnetic field of 1000 Gauss at a Reynolds number of 1874,with respect to the base fluid.A total of 126 datasets of heat transfer coefficient,Nusselt number,and friction factor were used as input and output *** three ANN algorithms of LM,BFGS,and VLGD,have shown good acceptance with the experimental data with root-mean-square errors of 0.34883,0.25341,and 1.0202 with correlation coefficients(R2)of 0.99954,0.9967,and 0.94501,respectively,for the Nusselt number ***,the three ANN algorithms predict root-mean-square errors of 0.001488,0.005041,and 0.006924 with correlation coefficients(R2)of 0.99982,0.99976,and 0.99486,respectively,for the friction factor *** to BFGS and V

关键词： Artificial neural network nusselt number friction factor heat sink correlations

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Study on friction behavior of HMX crystals based on peridynamic theory

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Applied Physics A: Materials Science and Processing 2025年第6期131卷 1-16页

作者： Wang, Mingchen Zhu, Pengzhe Rong, Jiacheng Xu, Yimeng Yun, Xiao School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China School of Mechanical Engineering & Automation Beihang University Beijing100081 China

The friction behavior of HMX crystals has attracted significant attention since friction is proven to be a critical factor in causing local heating and even accidental ignition. But the friction mechanisms of HMX single crystals remain elusive. Classical continuum mechanics faces challenges in modeling discontinuities. In contrast, peridynamic (PD) theory has emerged as an effective approach for investigating discontinuous phenomena such as damage evolution. In this study, a frictional contact model for HMX crystals is developed based on the state-based PD theory. The effects of indenter shape, size, scratching depth and scratching velocity on the tribological response of HMX crystals, particularly regarding damage and crack propagation, are investigated. The results indicate that the tribological response is insensitive to changes in scratching velocity. As the scratching depth increases, longer lateral cracks develop for both the spherical and conical indenters, but the median crack propagation depth increases for only the spherical indenter. And the combined action of radial and lateral cracks plays a crucial role in debris peeling. It is also found that increasing the diameter of the spherical indenter results in more shear cracks and rougher scratch surfaces, without affecting the depth of tensile crack propagation. In addition, the normalized material removal volume and its depth sensitivity are greater for the spherical indenter than for the conical indenter. These findings provide valuable insights into the friction mechanisms of the energetic crystal HMX. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Continuum damage mechanics

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Cooperative output regulation of heterogeneous directed multi-agent systems:a fully distributed model-free reinforcement learning framework

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Science China(Information Sciences) 2025年第2期68卷 170-185页

作者： Xiongtao SHI Yanjie LI Chenglong DU Huiping LI Chaoyang CHEN Weihua GUI Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics School of Mechanical Engineering and AutomationHarbin Institute of Technology (Shenzhen) School of Automation Central South University School of Marine Science and Technology Northwestern Polytechnical University School of Information and Electrical Engineering Hunan University of Science and Technology

In this paper, the cooperative output regulation(COR) problem of a class of unknown heterogeneous multi-agent systems(MASs) with directed graphs is studied via a model-free reinforcement learning(RL) based fully distributed eventtriggered control(ETC) strategy. First, we consider the scenario that the exosystem is accessible globally to all agents, an internal model-based augmented algebraic Riccati equation(AARE) is constructed, and its solution is learned by the proposed model-free RL algorithm via online input-output data. Further, for the scenario that the exosystem is accessible only to its adjacent followers, the distributed observers are designed for each agent to get the state of the exosystem, and an internal modelbased fully distributed adaptive ETC protocol is then synthesized to construct the corresponding AARE, and the feedback gain matrix is learned in a model-free fashion. The model-free RL-based control protocol proposed in this paper can not only remove the prior knowledge of agents' dynamics, but also release the dependence on global information by the adaptive event-triggered mechanism(ETM) and the new graph-based Lyapunov function. Finally, simulation results are illustrated to show the feasibility and effectiveness of the proposed control scheme.

关键词： model-free reinforcement learning unknown heterogeneous multi-agent systems fully distributed event-triggered control directed graph

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FDNet:Fourier transform guided dual-channel underwater image enhancement diffusion network

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Science China(Technological Sciences) 2025年第1期68卷 58-74页

作者： Zhen ZHU Xiaobo LI Qianwen MA Jingsheng ZHAI Haofeng HU School of Marine Science and Technology Tianjin UniversityTianjin 300072China College of Mechanical Engineering and Automation Fuzhou UniversityFuzhou 350108China

Due to the scattering and absorption of light,underwater imaging yields suboptimal results characterized by low brightness,diminished image contrast,and the loss of *** learning-based underwater image enhancement(UIE)methods mainly focus on addressing these issues in the spatial domain,with limited attention to the Fourier frequency *** propose the Fourier transform guided dual-channel underwater image enhancement diffusion network(FDNet),aiming to fully leverage frequency domain information and the characteristics of a diffusion-based generation *** amplitude-phase dualchannel not only effectively provides feedback on the energy distribution of the image but also incorporates crucial image structure and texture *** on these advantages,we first introduce a Fourier transform-based amplitude-phase dualchannel enhancement front-end network for *** network significantly improves the quality of the network's image input,including brightness and contrast,by incorporating phase congruency edge enhancement and prior-driven amplitude mapping *** preceding network mitigates the overall training challenges caused by poor input image ***,the application of denoising networks based on lightweight transformers effectively improves the computational time required for each iteration during model *** results indicate that FDNet outperforms many current learning-based UIE algorithms in restoring images across multiple real underwater image datasets,demonstrating more robust generalization capabilities in degraded underwater *** competitive performance advantage achieved in enhancing visual quality underscores the effectiveness of our method.

关键词： underwater image enhancement Fourier transform deep neural network phase congruency diffusion model

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Scaled non-passive environmental interaction force tracking for telerobotic manufacturing system with variable time delay

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Science China(Technological Sciences) 2025年第3期68卷 167-178页

作者： Yukui ZHANG Fangyu PENG Yiming WAN Chen CHEN Zhitao GAO Haoyan LIU Yu WANG Rong YAN Xiaowei TANG School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan430074China School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan430074China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and TechnologyWuhan430081China

Robot teleoperation plays an important role in industrial manufacturing in unknown and dangerous environments beyond human *** telerobotic manufacturing tasks,environmental interaction forces may vary significantly from task to ***,it is crucial to provide operators with the specific proportional feedback of environmental interaction forces to enhance their environmental awareness and manipulation ***,variable time delays and various scales of environmental interaction force feedback seriously affect the system stability,which should be rigorously addressed when designing control *** cope with these difficulties,a position and scaled force tracking control framework is proposed and the LyapunovKrasovskii theory is used to obtain a simple algebraic stability criterion with the scaling factor of the environmental interaction force *** addition,a low-pass filter-based radial basis function neural network is designed to avoid the effect of the measurement noise and the sudden change of the non-passive environmental interaction force on the system *** with different controllers in various telerobotic manufacturing tasks such as heavy lifting,cutting,and polishing,our proposed method achieves better position and scaled force tracking performance.

关键词： non-passive interaction force position tracking scaled force tracking stability analysis telerobotic manufacturing system variable time delay

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A Walk Primitive with Double Support for Biped Robots

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Journal of Bionic engineering 2025年第1期22卷 133-143页

作者： Guoshuai Liu Zhiguo Lu Zhongqing Li Jin Xuan Aizun Liu School of Mechanical Engineering and Automation Northeastern UniversityShenyang110819China Aerospace Times FeiHong Technology Company Limited OrganizationBeijing100094China

Walking is the basic locomotion pattern for bipedal *** walking pattern is widely generated using the linear inverted pendulum *** linear inverted pendulum motion of each support period can be designed as a walk primitive to be connected to form a walking trajectory.A novel method of integrating double support phase into the walk primitive was proposed in this *** method describes the generation of walking patterns using walk primitives with double support,specifically for lateral plane including walking in place,walking for lateral,and walking initiation,and for sagittal plane including fixed step length walking,variable step length walking,and walking *** to walk primitives without double support phase,those with double support phase reduce the maximum speed required by the robot and eliminate the need to adjust foothold for achieving continuous *** performance of the proposed method is validated by simulations and experiments on Neubot,a position-controlled biped robot.

关键词： Biped robot Linear inverted pendulum Walk primitive Double support

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Semantic Segmentation of Assembly Images Combining Deep Learning and Ontological Reasoning

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IEEE Access 2025年 13卷 70116-70127页

作者： Zhang, Han Shi, Xiaolin Xu, Haisong Li, Yi Ma, Liping Liaoning University of Technology College of Mechanical Engineering and Automation Jinzhou121001 China Institute of Intelligent Manufacturing School of Mechanical Engineering Northwestern Polytechnical University Xi’an710072 China

With the advancement of industrial intelligence, semantic segmentation of assembly images is increasingly widely used in automated production and other fields. Aiming at the problem that traditional image segmentation methods often neglect the semantic association and structural information between objects in the image, this paper proposes a semantic segmentation method for assembly body images that combines deep learning and ontology reasoning. DeepLabV3+ is used as the base model, and MobileNetV2 backbone network and Convolutional Block Attention Module (CBAM) are introduced to accelerate the computational speed of the network and at the same time enhance the fusion ability of multi-scale features of the network to further improve the semantic segmentation accuracy. However, due to the poor interpretability and reliability of the data-driven deep learning method, an ontology module with inference capability is introduced and semantic rules are written based on the relevant knowledge of the domain to correct the misclassification of deep learning. Finally, we make the reducer model through Solidworks as the dataset for the experiment. The experimental results show that the improved network model improves 11.92% and 6.53% over the original network model in mIoU and mPA, respectively. The constructed ontology model of assembly information is then referenced and the misclassified parts are corrected by reasoning. The effectiveness of the semantic segmentation method proposed in this paper is verified. © 2013 IEEE.

关键词： Semantic Segmentation

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Event-based non-fragile state estimation for time-varying systems under deception attacks

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Science China(Information Sciences) 2025年第4期68卷 349-362页

作者： Xiao-Meng LI Tao ZOU Renquan LU Zhijia ZHAO School of Mechanical and Electrical Engineering Guangzhou University School of Automation Guangdong Hong Kong Joint Laboratory for Intelligent Decision and Cooperative ControlGuangdong University of Technology

This paper addresses the challenge of dynamic event-based non-fragile state estimation for discrete time-varying systems under deception attacks. These attacks involve injecting deceptive signals into the communication channel, which can consume limited network resources and disrupt system estimation tasks. To tackle this issue, the study introduces a comprehensive and realistic deception attack model that accounts for the various hard physical constraints. To mitigate the influence of these attacks and reduce communication demands, a dynamic event-triggered scheme that uses dynamic threshold parameters is developed. The main purpose of the addressed problem is to design an event-based non-fragile estimator that ensures the estimation error systems meet the H∞performance constraint over a finite horizon. The paper provides two key criteria to ensure the existence of the proposed estimator, leveraging stochastic analysis techniques. The desired estimator gains are determined using a recursive process of solving matrix inequalities. Finally, a numerical example illustrates the effectiveness of the developed event-based non-fragile estimator design method.

关键词： discrete time-varying systems deception attacks dynamic event-triggered mechanism finite horizon non-fragile estimation

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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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