The kinematic redundancy in a robot leads to an infinite number of solutions for inverse kinematics, which implies the possibility to select a 'best' solution according to an optimization criterion. In this pa...The kinematic redundancy in a robot leads to an infinite number of solutions for inverse kinematics, which implies the possibility to select a 'best' solution according to an optimization criterion. In this paper, two optimization objective functions are proposed, aiming at either minimizing extra degrees of freedom (DOFs) or minimizing the total potential energy of a multilink redundant robot. Physical constraints of either equality or inequality types are taken into consideration in the objective functions. Since the closed-form solutions do not exist in general for highly nonlinear and constrained optimization problems, we adopt and develop two numerical methods, which are verified to be effective and precise in solving the two optimization problems associated with the redundant inverse kinematics. We first verify that the well established trajectory following method can precisely solve the two optimization problems, but is computation intensive. To reduce the computation time, a sequential approach that combines the sequential quadratic programming and iterative Newton-Raphson algorithm is developed. A 4-DOF Fujitsu Hoap-1 humanoid robot arm is used as a prototype to validate the effectiveness of the proposed optimization solutions.展开更多
Optical metasurfaces(OMs)offer unprecedented control over electromagnetic waves,enabling advanced optical multiplexing.The emergence of deep learning has opened new avenues for designing OMs.However,existing deep lear...Optical metasurfaces(OMs)offer unprecedented control over electromagnetic waves,enabling advanced optical multiplexing.The emergence of deep learning has opened new avenues for designing OMs.However,existing deep learning methods for OMs primarily focus on forward design,which limits their design capabilities,lacks global optimization,and relies on prior knowledge.Additionally,most OMs are static,with fixed functionalities once processed.To overcome these limitations,we propose an inverse design deep learning method for dynamic OMs.Our approach comprises a forward prediction network and an inverse retrieval network.The forward prediction network establishes a mapping between meta-unit structure parameters and reflectance spectra.The inverse retrieval network generates a library of meta-unit structure parameters based on target requirements,enabling end-to-end design of OMs.By incorporating the dynamic tunability of the phase change material Sb2Te3with inverse design deep learning,we achieve the design and verification of dynamic multifunctional OMs.Our results demonstrate OMs with multiple information channels and encryption capabilities that can realize multiple physical field optical modulation functions.When Sb2Te3is in the amorphous state,near-field nano-printing based on meta-unit amplitude modulation is achieved for X-polarized incident light,while holographic imaging based on meta-unit phase modulation is realized for circularly polarized light.In the crystalline state,the encrypted information remains secure even with the correct polarization input,achieving double encryption.This research points towards ultra-compact,high-capacity,and highly secure information storage approaches.展开更多
The radial basis function （RBF） interpolation approach proposed by Freedman is used to solve inverse problems encountered in well-logging and other petrophysical issues. The approach is to predict petrophysical prop...The radial basis function （RBF） interpolation approach proposed by Freedman is used to solve inverse problems encountered in well-logging and other petrophysical issues. The approach is to predict petrophysical properties in the laboratory on the basis of physical rock datasets, which include the formation factor, viscosity, permeability, and molecular composition. However, this approach does not consider the effect of spatial distribution of the calibration data on the interpolation result. This study proposes a new RBF interpolation approach based on the Freedman＇s RBF interpolation approach, by which the unit basis functions are uniformly populated in the space domain. The inverse results of the two approaches are comparatively analyzed by using our datasets. We determine that although the interpolation effects of the two approaches are equivalent, the new approach is more flexible and beneficial for reducing the number of basis functions when the database is large, resulting in simplification of the interpolation function expression. However, the predicted results of the central data are not sufficiently satisfied when the data clusters are far apart.展开更多
文摘The kinematic redundancy in a robot leads to an infinite number of solutions for inverse kinematics, which implies the possibility to select a 'best' solution according to an optimization criterion. In this paper, two optimization objective functions are proposed, aiming at either minimizing extra degrees of freedom (DOFs) or minimizing the total potential energy of a multilink redundant robot. Physical constraints of either equality or inequality types are taken into consideration in the objective functions. Since the closed-form solutions do not exist in general for highly nonlinear and constrained optimization problems, we adopt and develop two numerical methods, which are verified to be effective and precise in solving the two optimization problems associated with the redundant inverse kinematics. We first verify that the well established trajectory following method can precisely solve the two optimization problems, but is computation intensive. To reduce the computation time, a sequential approach that combines the sequential quadratic programming and iterative Newton-Raphson algorithm is developed. A 4-DOF Fujitsu Hoap-1 humanoid robot arm is used as a prototype to validate the effectiveness of the proposed optimization solutions.
基金National Key Research and Development Program of China(2023YFB4603803)National Natural Science Foundation of China(62075200,12374295,22273069)+1 种基金Key R&D Program of Hubei(2021BAA173)Fundamental Research Funds for the Central Universities(2042023kf0113,2042022gf0004)。
文摘Optical metasurfaces(OMs)offer unprecedented control over electromagnetic waves,enabling advanced optical multiplexing.The emergence of deep learning has opened new avenues for designing OMs.However,existing deep learning methods for OMs primarily focus on forward design,which limits their design capabilities,lacks global optimization,and relies on prior knowledge.Additionally,most OMs are static,with fixed functionalities once processed.To overcome these limitations,we propose an inverse design deep learning method for dynamic OMs.Our approach comprises a forward prediction network and an inverse retrieval network.The forward prediction network establishes a mapping between meta-unit structure parameters and reflectance spectra.The inverse retrieval network generates a library of meta-unit structure parameters based on target requirements,enabling end-to-end design of OMs.By incorporating the dynamic tunability of the phase change material Sb2Te3with inverse design deep learning,we achieve the design and verification of dynamic multifunctional OMs.Our results demonstrate OMs with multiple information channels and encryption capabilities that can realize multiple physical field optical modulation functions.When Sb2Te3is in the amorphous state,near-field nano-printing based on meta-unit amplitude modulation is achieved for X-polarized incident light,while holographic imaging based on meta-unit phase modulation is realized for circularly polarized light.In the crystalline state,the encrypted information remains secure even with the correct polarization input,achieving double encryption.This research points towards ultra-compact,high-capacity,and highly secure information storage approaches.
基金supported by the National Science and Technology Major Projects(No.2011ZX05020-008)Well Logging Advanced Technique and Application Basis Research Project of Petrochina Company(No.2011A-3901)
文摘The radial basis function （RBF） interpolation approach proposed by Freedman is used to solve inverse problems encountered in well-logging and other petrophysical issues. The approach is to predict petrophysical properties in the laboratory on the basis of physical rock datasets, which include the formation factor, viscosity, permeability, and molecular composition. However, this approach does not consider the effect of spatial distribution of the calibration data on the interpolation result. This study proposes a new RBF interpolation approach based on the Freedman＇s RBF interpolation approach, by which the unit basis functions are uniformly populated in the space domain. The inverse results of the two approaches are comparatively analyzed by using our datasets. We determine that although the interpolation effects of the two approaches are equivalent, the new approach is more flexible and beneficial for reducing the number of basis functions when the database is large, resulting in simplification of the interpolation function expression. However, the predicted results of the central data are not sufficiently satisfied when the data clusters are far apart.
