With the diversity of manipulator grasping methods and the complexity of the unstructured environment, the grasping planning of the target object is very complicated. However, the external factor for the manipulator g...
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With the diversity of manipulator grasping methods and the complexity of the unstructured environment, the grasping planning of the target object is very complicated. However, the external factor for the manipulator grasping is primarily the external shape of the target object. Therefore, it is of great significance to establish a grasping system based on target shape analysis. This paper proposes a method for determining the grasping posture of manipulator based on shape analysis and force closure. The irregular or complex objects are reduced to a combination of some basic shapes. The 3D data points of the object are split into blocks and each part is fitted to sphere, cylinder or rectangle by a best-fit algorithm. This allows the grasping posture of the manipulator to be determined quickly. The grasping characteristics of the object are analyzed and the grasping surface is described qualitatively by means of a superellipse. The grasping of objects is simplified according to the stable grasping condition of force closure. The force spiral space for grasping defines the grasping quality of the manipulator. The best grasping posture is obtained by evaluating the indicators. This method eliminates complex training processes, reduces the complexity of robotic grasping and highly universal. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.
This article reevaluates the impulse response functions (IRFs) to a monetary policy shock of the structural vector autoregression (SVAR). Identifying restrictions are specified and justified based on empirical evidenc...
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This article reevaluates the impulse response functions (IRFs) to a monetary policy shock of the structural vector autoregression (SVAR). Identifying restrictions are specified and justified based on empirical evidence,i.e., conditional independence relations of variables, which is an important dimension that a good model must be able to mimic. The empirical-based approach is able to significant narrow down the set of admissible causal orders to identify the IRFs to a monetary policy shock (from 2,482 to 8). I find that most of the qualitative "stylized" features reported in the literature remain intact. However, the quantitative predictions are much less certain than what is commonly perceived.
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