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3. Formulation of a set of fuzzy rules re ecting the relationship between the extrema of the distance norms and the model grasps 4. Computation of a vector of similarity degrees between the model grasps and the grasp combination 15.4.2.1 Distance Norms. Let, for example, grasp2 ; grasp5 ; grasp7 ; grasp10 ; grasp14 be a combination of grasps taken from the list of grasps shown in Figure 15.4. In the training phase, a time series of these grasps is generated using the existing time series of the corresponding grasp models. Then, each of the model grasps i 2,5,7,10,14 is shifted along the time sequence of the grasp combination and compared with parts of it while taking the norm2 kQci Qmi k between the difference of the nger angles Qmi qm t1 ; . . . ; qm tnc T i of a graspi and the nger angles of the grasp combination t t i Qci qci ~1 ; . . . ; qci ~nc T The vectors qm and qc include the three nger angles for each of the ve ngers. For scaling reasons, the norm of the difference is divided by the norm kQmi k of the model grasp. Then we obtain for the scaled norm ni kQci Qmi k kQmi k 15:13 rdlc pdf 417 PDF417 Barcode Creating Library for RDLC Reports | Generate ...
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