In recent decades,capacitive pressure sensors(CPSs)with high sensitivity have demonstrated significant potential in applications such as medical monitoring,artificial intelligence,and soft robotics.Efforts to enhance ...In recent decades,capacitive pressure sensors(CPSs)with high sensitivity have demonstrated significant potential in applications such as medical monitoring,artificial intelligence,and soft robotics.Efforts to enhance this sensitivity have predominantly focused on material design and structural optimization,with surface microstructures such as wrinkles,pyramids,and micro-pillars proving effective.Although finite element modeling(FEM)has guided enhancements in CPS sensitivity across various surface designs,a theoretical understanding of sensitivity improvements remains underexplored.This paper employs sinusoidal wavy surfaces as a representative model to analytically elucidate the underlying mechanisms of sensitivity enhancement through contact mechanics.These theoretical insights are corroborated by FEM and experimental validations.Our findings underscore that optimizing material properties,such as Young’s modulus and relative permittivity,alongside adjustments in surface roughness and substrate thickness,can significantly elevate the sensitivity.The optimal performance is achieved when the amplitude-to-wavelength ratio(H/)is about 0.2.These results offer critical insights for designing ultrasensitive CPS devices,paving the way for advancements in sensor technology.展开更多
All-inorganic perovskites based on cesium-lead-bromine(Cs-Pb-Br)have been a prominent research focus in optoelectronics in recent years.The optimisation and tunability of their macroscopic properties exploit the confo...All-inorganic perovskites based on cesium-lead-bromine(Cs-Pb-Br)have been a prominent research focus in optoelectronics in recent years.The optimisation and tunability of their macroscopic properties exploit the conformational flexibility,resulting in various crystal structures.Varying synthesis parameters can yield distinct crystal structures from Cs,Pb,and Br precursors,and manually exploring the relationship between these synthesis parameters and the resulting crystal structure is both labour-intensive and time-consuming.Machine learning(ML)can rapidly uncover insights and drive discoveries in chemical synthesis with the support of data,significantly reducing both the cost and development cycle of materials.Here,we gathered synthesis parameters from published literature(220 synthesis runs)and implemented eight distinct ML models,including eXtreme Gradient Boosting(XGB),Decision Tree(DT),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),Logistic Regression(LR),Gradient Boosting(GB),and K-Nearest(KN)to classify and predict Cs-Pb-Br crystal structures from given synthesis parameters.Validation accuracy,precision,F1 score,recall,and average area under the curve(AUC)are employed to evaluate these ML models.The XGB model exhibited the best performance,achieving a validation accuracy of 0.841.The trained XGB model was subsequently utilised to predict the structure from 10 experimental runs using a randomised set of parameters,achieving a testing accuracy of 0.8.The results indicate that the Cs/Pb molar ratio,reaction time,and the concentration of organic compounds(ligands)play crucial roles in synthesising various crystal structures of Cs-Pb-Br.This study demonstrates a significant decrease in effort required for experimental procedures and builds a foundational basis for predicting crystal structures from synthesis parameters.展开更多
The use of orbital angular momentum(OAM)as an independent dimension for information encryption has garnered considerable attention.However,the multiplexing capacity of OAM is limited,and there is a need for additional...The use of orbital angular momentum(OAM)as an independent dimension for information encryption has garnered considerable attention.However,the multiplexing capacity of OAM is limited,and there is a need for additional dimensions to enhance storage capabilities.We propose and implement orbital angular momentum lattice(OAML)multiplexed holography.The vortex lattice(VL)beam comprises three adjustable parameters:the rotation angle of the VL,the angle between the wave normal and the z axis,which determines the VL’s dimensions,and the topological charge.Both the rotation angle and the VL’s dimensions serve as supplementary encrypted dimensions,contributing azimuthally and radially,respectively.We investigate the mode selectivity of OAML and focus on the aforementioned parameters.Through experimental validation,we demonstrate the practical feasibility of OAML multiplexed holography across multiple dimensions.This groundbreaking development reveals new possibilities for the advancement of practical information encryption systems.展开更多
为解决基于关节力矩的双足机器人参数辨识方法辨识精度不高,基于完整的足底力信息和运动捕捉数据的辨识方法对实验条件要求较高的问题,提出基于ZMP(zero moment point)数据的双足机器人惯性参数辨识方法。将理论ZMP与实际ZMP的位置偏差...为解决基于关节力矩的双足机器人参数辨识方法辨识精度不高,基于完整的足底力信息和运动捕捉数据的辨识方法对实验条件要求较高的问题,提出基于ZMP(zero moment point)数据的双足机器人惯性参数辨识方法。将理论ZMP与实际ZMP的位置偏差作为目标函数,考虑参数范围和机器人总质量两类约束条件,建立只使用双足机器人自身传感器采样数据的惯性参数辨识优化模型。针对所建模型无法拆分成线性形式的问题,推导目标函数关于参数矢量的梯度矢量和海塞矩阵,并给出了基于最速下降法和牛顿法的优化求解算法。使用GoRoBoT-II机器人的双足部分,进行腿部杆件的惯性参数辨识实验,将所提出方法得到的辨识结果与传统基于关节力矩的辨识结果进行对比,发现基于ZMP的辨识方法的结果更接近于三维几何建模得到的参数标称值,且理论ZMP与实际ZMP的偏差均值为4.6 mm,小于传统基于力矩辨识方法的12.4 mm,说明所提出的基于ZMP的惯性参数辨识方法能够得到比传统方法更好的结果。展开更多
Highly deformable bodies are essential for numerous types of applications in all sorts of environments. Joint-like structures comprising a ball and socket joint have many degrees of freedom that allow mobility of many...Highly deformable bodies are essential for numerous types of applications in all sorts of environments. Joint-like structures comprising a ball and socket joint have many degrees of freedom that allow mobility of many biomimetic structures. Recently, soft robots are favored over rigid structures for their highly compliant material, high-deformation properties at low forces, and ability to operate in di fficult environments. However, it is still challenging to fabricate complex designs that satisfy application constraints due to the combined e ffects of material properties, actuation method, and structural geometry on the performance of the soft robot. Therefore, a combination of a rigid joint and a soft body can help achieve modular robots with fully functional body morphology. Yet, the fabrication of soft parts requires extensive molding for complex shapes, which comprises several processes and can be time-consuming. In addition, molded connections between extremely soft materials and hard materials can be critical failing points. In this paper, we present a functionally graded 3D-printed joint-like structure actuated by novel contractile actuators. Functionally graded materials (FGMs) via 3D printing allow for extensive material property enhancement and control which warrant tunable functionalities of the system. The 3D-printed structure is made of 3 rigid ball and socket joints connected in series and actuated by integrating twisted and coiled polymer fishing line ( TCPFL) actuators, which are con fined in the FGM accordion-shaped channels. The implementation of the untethered T CPFL actuation system can be highly bene ficial for deployment in environments that require low vibrations and silent actuation. The fishing line TCP actuators produce an actuation strain up to 40% and bend the joint up to 40° in any direction. The T CPFL can be actuated individually or as a group to control the bending trajectory of the modular joint, which is bene ficial when deployed in areas that contain small crevices. Obtaining complex modes of bending, the FGM multidirectional joint demonstrated a great potential to achieve di fferent functionalities such as crawling, rolling, swimming, or underwater exploration.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.12272369)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB0620101).
文摘In recent decades,capacitive pressure sensors(CPSs)with high sensitivity have demonstrated significant potential in applications such as medical monitoring,artificial intelligence,and soft robotics.Efforts to enhance this sensitivity have predominantly focused on material design and structural optimization,with surface microstructures such as wrinkles,pyramids,and micro-pillars proving effective.Although finite element modeling(FEM)has guided enhancements in CPS sensitivity across various surface designs,a theoretical understanding of sensitivity improvements remains underexplored.This paper employs sinusoidal wavy surfaces as a representative model to analytically elucidate the underlying mechanisms of sensitivity enhancement through contact mechanics.These theoretical insights are corroborated by FEM and experimental validations.Our findings underscore that optimizing material properties,such as Young’s modulus and relative permittivity,alongside adjustments in surface roughness and substrate thickness,can significantly elevate the sensitivity.The optimal performance is achieved when the amplitude-to-wavelength ratio(H/)is about 0.2.These results offer critical insights for designing ultrasensitive CPS devices,paving the way for advancements in sensor technology.
文摘针对水下自主航行器(autonomous underwater vehicle,AUV)在复杂三维洋流环境中目标跟踪的高维、动态干扰和稀疏回报挑战,提出了一种基于分布式强化学习的水下自主航行器水下三维洋流目标跟踪控制算法。首先,引入真实三维洋流数据,设计动态目标跟踪场景,以准确描述AUV的运动过程;其次,结合对抗深度强化学习网络(dueling deep Q-network,Dueling DQN)结构与分位数回归方法,针对三维洋流环境可能导致Q值过高估计的问题,构建分布强化学习框架,以量化Q值的不确定性,提升策略对动态干扰的适应能力;最后,引入优先经验回放机制,设计约束条件下的奖励函数,优化数据采样策略,加速模型收敛。实验结果表明,相较于深度Q网络(deep Q-network,DQN)、双深度Q网络(double deep Q-network,DDQN)和Dueling DQN,所提算法在复杂洋流环境中表现更优,在收敛速度、目标跟踪精度和鲁棒性方面均取得了显著的进展。
基金the Italian Space Agency(Agenzia Spaziale Italiana,ASI)in the framework of the Research Day“Giornate della Ricerca Spaziale”initiative through the contract ASI N.2023-4-U.0.
文摘All-inorganic perovskites based on cesium-lead-bromine(Cs-Pb-Br)have been a prominent research focus in optoelectronics in recent years.The optimisation and tunability of their macroscopic properties exploit the conformational flexibility,resulting in various crystal structures.Varying synthesis parameters can yield distinct crystal structures from Cs,Pb,and Br precursors,and manually exploring the relationship between these synthesis parameters and the resulting crystal structure is both labour-intensive and time-consuming.Machine learning(ML)can rapidly uncover insights and drive discoveries in chemical synthesis with the support of data,significantly reducing both the cost and development cycle of materials.Here,we gathered synthesis parameters from published literature(220 synthesis runs)and implemented eight distinct ML models,including eXtreme Gradient Boosting(XGB),Decision Tree(DT),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),Logistic Regression(LR),Gradient Boosting(GB),and K-Nearest(KN)to classify and predict Cs-Pb-Br crystal structures from given synthesis parameters.Validation accuracy,precision,F1 score,recall,and average area under the curve(AUC)are employed to evaluate these ML models.The XGB model exhibited the best performance,achieving a validation accuracy of 0.841.The trained XGB model was subsequently utilised to predict the structure from 10 experimental runs using a randomised set of parameters,achieving a testing accuracy of 0.8.The results indicate that the Cs/Pb molar ratio,reaction time,and the concentration of organic compounds(ligands)play crucial roles in synthesising various crystal structures of Cs-Pb-Br.This study demonstrates a significant decrease in effort required for experimental procedures and builds a foundational basis for predicting crystal structures from synthesis parameters.
基金supported by the Guangdong Major Project of Basic and Applied Basic Research (Grant No.2020B0301030009)the National Natural Science Foundation of China (Grant Nos.61935013,62375181,and 61975133)+1 种基金the Shenzhen Science and Technology Program (Grant No.JCYJ20200109114018750)the Shenzhen Peacock Plan (Grant No.KQTD20170330110444030).
文摘The use of orbital angular momentum(OAM)as an independent dimension for information encryption has garnered considerable attention.However,the multiplexing capacity of OAM is limited,and there is a need for additional dimensions to enhance storage capabilities.We propose and implement orbital angular momentum lattice(OAML)multiplexed holography.The vortex lattice(VL)beam comprises three adjustable parameters:the rotation angle of the VL,the angle between the wave normal and the z axis,which determines the VL’s dimensions,and the topological charge.Both the rotation angle and the VL’s dimensions serve as supplementary encrypted dimensions,contributing azimuthally and radially,respectively.We investigate the mode selectivity of OAML and focus on the aforementioned parameters.Through experimental validation,we demonstrate the practical feasibility of OAML multiplexed holography across multiple dimensions.This groundbreaking development reveals new possibilities for the advancement of practical information encryption systems.
文摘为解决基于关节力矩的双足机器人参数辨识方法辨识精度不高,基于完整的足底力信息和运动捕捉数据的辨识方法对实验条件要求较高的问题,提出基于ZMP(zero moment point)数据的双足机器人惯性参数辨识方法。将理论ZMP与实际ZMP的位置偏差作为目标函数,考虑参数范围和机器人总质量两类约束条件,建立只使用双足机器人自身传感器采样数据的惯性参数辨识优化模型。针对所建模型无法拆分成线性形式的问题,推导目标函数关于参数矢量的梯度矢量和海塞矩阵,并给出了基于最速下降法和牛顿法的优化求解算法。使用GoRoBoT-II机器人的双足部分,进行腿部杆件的惯性参数辨识实验,将所提出方法得到的辨识结果与传统基于关节力矩的辨识结果进行对比,发现基于ZMP的辨识方法的结果更接近于三维几何建模得到的参数标称值,且理论ZMP与实际ZMP的偏差均值为4.6 mm,小于传统基于力矩辨识方法的12.4 mm,说明所提出的基于ZMP的惯性参数辨识方法能够得到比传统方法更好的结果。
文摘Highly deformable bodies are essential for numerous types of applications in all sorts of environments. Joint-like structures comprising a ball and socket joint have many degrees of freedom that allow mobility of many biomimetic structures. Recently, soft robots are favored over rigid structures for their highly compliant material, high-deformation properties at low forces, and ability to operate in di fficult environments. However, it is still challenging to fabricate complex designs that satisfy application constraints due to the combined e ffects of material properties, actuation method, and structural geometry on the performance of the soft robot. Therefore, a combination of a rigid joint and a soft body can help achieve modular robots with fully functional body morphology. Yet, the fabrication of soft parts requires extensive molding for complex shapes, which comprises several processes and can be time-consuming. In addition, molded connections between extremely soft materials and hard materials can be critical failing points. In this paper, we present a functionally graded 3D-printed joint-like structure actuated by novel contractile actuators. Functionally graded materials (FGMs) via 3D printing allow for extensive material property enhancement and control which warrant tunable functionalities of the system. The 3D-printed structure is made of 3 rigid ball and socket joints connected in series and actuated by integrating twisted and coiled polymer fishing line ( TCPFL) actuators, which are con fined in the FGM accordion-shaped channels. The implementation of the untethered T CPFL actuation system can be highly bene ficial for deployment in environments that require low vibrations and silent actuation. The fishing line TCP actuators produce an actuation strain up to 40% and bend the joint up to 40° in any direction. The T CPFL can be actuated individually or as a group to control the bending trajectory of the modular joint, which is bene ficial when deployed in areas that contain small crevices. Obtaining complex modes of bending, the FGM multidirectional joint demonstrated a great potential to achieve di fferent functionalities such as crawling, rolling, swimming, or underwater exploration.
