Iterated local search(ILS)is used to construct the optimal experimental designs for multi-dimensional constrained spaces,in which the inner loop is based on the stochastic coordinate-exchange(SCE)algorithm.Every time ...Iterated local search(ILS)is used to construct the optimal experimental designs for multi-dimensional constrained spaces,in which the inner loop is based on the stochastic coordinate-exchange(SCE)algorithm.Every time a local optimal solution is found by the SCE algorithm,the perturbation operator is applied to it,and then a new solution is explored in the areas where the exchange of coordinates may produce improvement,so as to retain the features and attributes of the current optimal solution and avoid the defects of random restart.We implement the iterated local coordinate-exchange algorithm for experimental designs in the multi-dimensional constrained spaces.In addition,sensitivity analysis was conducted to analyze the impacts of the parameters on the performance of the proposed algorithm.Also we compared the performance of the proposed algorithm to the SCE algorithm using the random restart strategy.The analysis shows that the proposed algorithm is better than the SCE algorithm in terms of efficiency and quality,especially in the experimental designs for high-dimensional constrained space.展开更多
Most existing legged robots are developed under laboratory environments and, corre- spondingly, have good performance of locomotion. The robots＇ ability of walking on rough terrain is of great importance but is seldo...Most existing legged robots are developed under laboratory environments and, corre- spondingly, have good performance of locomotion. The robots＇ ability of walking on rough terrain is of great importance but is seldom achieved. Being compliant to external unperceived impacts is cru- cial since it is unavoidable that the slip, modeling errors and imprecise information of terrain will make planned trajectories to be followed with errors and unpredictable contacts. The impedance control gives an inspiration to realize an active compliance which allows the legged robots to follow reference trajectories and overcome external disturbances. In this paper, a novel impedance force/ position control scheme is presented, which is based on Cartesian force measurement of leg＇ s end effector for our hydraulic quadruped robot The simulation verifies the efficiency of the impedance model, and the experimental results at the end demonstrate the feasibility of the proposed control scheme.展开更多
The fusion of experimental automation and machine learning has catalyzed a new era in materials research,prominently featuring Gaussian Process(GP)Bayesian Optimization(BO)driven autonomous experiments.Here we introdu...The fusion of experimental automation and machine learning has catalyzed a new era in materials research,prominently featuring Gaussian Process(GP)Bayesian Optimization(BO)driven autonomous experiments.Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on realtime assessments of the raw experimental data.This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training.We also incorporate a flexible,human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results.We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data.This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings,providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.展开更多
基金This work was supported by the National Natural Science Foundation of China(72171231).
文摘Iterated local search(ILS)is used to construct the optimal experimental designs for multi-dimensional constrained spaces,in which the inner loop is based on the stochastic coordinate-exchange(SCE)algorithm.Every time a local optimal solution is found by the SCE algorithm,the perturbation operator is applied to it,and then a new solution is explored in the areas where the exchange of coordinates may produce improvement,so as to retain the features and attributes of the current optimal solution and avoid the defects of random restart.We implement the iterated local coordinate-exchange algorithm for experimental designs in the multi-dimensional constrained spaces.In addition,sensitivity analysis was conducted to analyze the impacts of the parameters on the performance of the proposed algorithm.Also we compared the performance of the proposed algorithm to the SCE algorithm using the random restart strategy.The analysis shows that the proposed algorithm is better than the SCE algorithm in terms of efficiency and quality,especially in the experimental designs for high-dimensional constrained space.
基金Supported by the National High Technology Research and Development Program of China(863Program)(2011AA041002)
文摘Most existing legged robots are developed under laboratory environments and, corre- spondingly, have good performance of locomotion. The robots＇ ability of walking on rough terrain is of great importance but is seldom achieved. Being compliant to external unperceived impacts is cru- cial since it is unavoidable that the slip, modeling errors and imprecise information of terrain will make planned trajectories to be followed with errors and unpredictable contacts. The impedance control gives an inspiration to realize an active compliance which allows the legged robots to follow reference trajectories and overcome external disturbances. In this paper, a novel impedance force/ position control scheme is presented, which is based on Cartesian force measurement of leg＇ s end effector for our hydraulic quadruped robot The simulation verifies the efficiency of the impedance model, and the experimental results at the end demonstrate the feasibility of the proposed control scheme.
基金supported by the Center for Nanophase Materials Sciences(CNMS),which is a US Department of Energy,Office of Science User Facility at Oak Ridge National Laboratory.
文摘The fusion of experimental automation and machine learning has catalyzed a new era in materials research,prominently featuring Gaussian Process(GP)Bayesian Optimization(BO)driven autonomous experiments.Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on realtime assessments of the raw experimental data.This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training.We also incorporate a flexible,human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results.We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data.This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings,providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.
