Stochastic model-based grasp synthesis: New logistics for data fusion with dissimilar sensor-cells

We attribute the data fusion logistics with dissimilar sensor-cells (taxels) an open-research paradigm, because the in-built characteristics in quantifying the system (output) response is still fuzzy. The problem gets even critical when we are bound to deal with a finite number of elemental taxels, as against traditional theories catering to somewhat denser agglomeration of (identical) sensor units. In fact, fusion models used hitherto have been found to be largely inappropriate for some of the distinct object-groups, e.g. from point-mass to small-sized ones. Besides, metrics of grasp synthesis (grip force & slippage) were largely unattended. In answering those lacunas, the present paper dwells on the modeling, algorithm and theoretical analysis of a novel fusion rule-base, centering on the relative dependency of the finite numbered taxels. A new proposition has also been developed for assessing the decision threshold-band, signaling the presence of object inside the grasp-zone of the gripper.