It is necessary for legged robots to walk stably and smoothly on rough terrain.In this paper,a desired landing points(DLP) walking method based on preview control was proposed in which an off-line foot motion trace an...It is necessary for legged robots to walk stably and smoothly on rough terrain.In this paper,a desired landing points(DLP) walking method based on preview control was proposed in which an off-line foot motion trace and an on-line modification of the trace were used to enable the robot to walk on rough terrain.The on-line modification was composed of speed modification,foot lifting-off height modification,step length modification,and identification and avoidance of unsuitable landing terrain.A planner quadruped robot simulator was used to apply the DLP walking method.The correctness of the method was proven by a series of simulations using the Adams and Simulink.展开更多
Accurate rock elastic property determination is vital for effective hydraulic fracturing,particularly Young’s modulus due to its link to rock brittleness.This study integrates interdisciplinary data for better predic...Accurate rock elastic property determination is vital for effective hydraulic fracturing,particularly Young’s modulus due to its link to rock brittleness.This study integrates interdisciplinary data for better predictions of elastic modulus,combining data mining,experiments,and calibrated synthetics.We used the microstructural insights extracted from rock images for geomechanical facies analysis.Additionally,the petrophysical data and well logs were correlated with shear wave velocity(Vs)and Young’s modulus.We developed a machine-learning workflow to predict Young’s modulus and assess rock fracturability,considering mineral composition,geomechanics,and microstructure.Our findings indicate that artificial neural networks effectively predict Young’s modulus,while K-Means clustering and hierarchical support vector machines excel in identifying rock and geomechanical facies.Utilizing Microscale thin section analysis in conjunction with fracture modeling enhances our understanding of fracture geometries and facilitates fracturability assessment.Notably,fracturability is controlled by specific geomechanical facies during initiation and propagation and influenced by continuity of geomechanical facies in small depth intervals.In conclusion,this study demonstrates data mining and machine learning potential for predicting rock properties and assessing fracturability,aiding hydraulic fracturing design optimization through diverse data and advanced methods.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 60875067the Natural Science Foundation of Heilongjiang Province under Grant F200602the Technical Innovation Talent Foundation of Harbin under Grant 2010RFQXG010
文摘It is necessary for legged robots to walk stably and smoothly on rough terrain.In this paper,a desired landing points(DLP) walking method based on preview control was proposed in which an off-line foot motion trace and an on-line modification of the trace were used to enable the robot to walk on rough terrain.The on-line modification was composed of speed modification,foot lifting-off height modification,step length modification,and identification and avoidance of unsuitable landing terrain.A planner quadruped robot simulator was used to apply the DLP walking method.The correctness of the method was proven by a series of simulations using the Adams and Simulink.
文摘Accurate rock elastic property determination is vital for effective hydraulic fracturing,particularly Young’s modulus due to its link to rock brittleness.This study integrates interdisciplinary data for better predictions of elastic modulus,combining data mining,experiments,and calibrated synthetics.We used the microstructural insights extracted from rock images for geomechanical facies analysis.Additionally,the petrophysical data and well logs were correlated with shear wave velocity(Vs)and Young’s modulus.We developed a machine-learning workflow to predict Young’s modulus and assess rock fracturability,considering mineral composition,geomechanics,and microstructure.Our findings indicate that artificial neural networks effectively predict Young’s modulus,while K-Means clustering and hierarchical support vector machines excel in identifying rock and geomechanical facies.Utilizing Microscale thin section analysis in conjunction with fracture modeling enhances our understanding of fracture geometries and facilitates fracturability assessment.Notably,fracturability is controlled by specific geomechanical facies during initiation and propagation and influenced by continuity of geomechanical facies in small depth intervals.In conclusion,this study demonstrates data mining and machine learning potential for predicting rock properties and assessing fracturability,aiding hydraulic fracturing design optimization through diverse data and advanced methods.
