A new fusion rule with dynamic decision threshold for heterogeneous field gripper sensory system: Part II

Rule-based data fusion for heterogeneous field sensory system is a challenging research paradigm till date, primarily due to the inherent characteristics in quantifying the output response of the system. The problem gets even critical when we need to deal with a limited number of elemental sensor-units in field, in contrast to traditional theories dealing with denser agglomeration of (identical) sensor units. In fact, field fusion models used hitherto have been found to be somewhat inappropriate for online detection of target-object(s), unless those are in a pre-assigned layout. Besides, the effect due to zonal distribution of the field-sensors was largely unattended. In answering those lacunas, the present paper dwells on the modeling, algorithm and theoretical analysis of a novel fusion rule-base, in its refined form, centering on the zone-based relative dependency of the finite numbered field sensor-units. Besides, a new proposition is developed for assessing the decision threshold-band, signaling the activation of the field robotic gripper, using a stochastic model.