Grip Force and Slip Analysis in Robotic Grasp: New Stochastic Paradigm Through Sensor Data Fusion

Algorithmic data fusion is instrumental in evaluating the quantitative output of a multisensory system and the same becomes extremely challenging, especially when the elemental sensory units do vary in type, size and characteristics. Truly, fusion of such heterogeneous sensory data remains an open-research paradigm till date, especially in the field of robotics, owing to its inherent characteristics in quantifying the output response of the system. The problem gets even critical when we need to contour with a limited number of elemental sensor-cells (taxels), in contrast to traditional theories dealing with large agglomeration of (identical) sensor units. In fact, fusion models used hitherto have been found to be largely inappropriate for the distinct object-groups, e.g. from point-mass to small-sized ones. Besides, paradigms of grasp synthesis (grip force & slippage) were largely unattended. Although traditional theories on sensory data fusion fit quite satisfactorily in searching a pre-defined object with a tentative dimension and depth perception, they fail to do justice in cases where profile of the object do vary from micro-scale to a finite spatial dimension. In answering these lacunas, the present article dwells on modeling, algorithm and experimental analysis of three novel fusion rule-bases, which are implemented in smallsized tactile array sensor to be used in robot gripper. A new proposition has been developed for assessing the decision threshold, signaling the presence of object inside the grasp-zone of the gripper. Besides, the developed model evaluates the approximate planar area of the grasped object alongwith its shape in real-time. The model also provides estimate for the gripping force required to sustain a stable grasp of the object vis-a-vis slippage characteristics, if any. Signal detection with multiple sensors, either all similar or dissimilar or any arbitrary combination, can be performed in two manners. In the traditional method, the local sensors communicate all observations (raw data) directly to a centralized detector (e.g. system controller board) where decision processing is performed. This method, although incorporates parallel channels for data communication, often requires a large bandwidth for the communication channels in order to obtain real-time results. In contrary, the second method deals with each sensor individually, by associating a detector module to each of the sensor-cells, which decides locally whether a signal is detected or not. These local decisions get transmitted to the main controller unit (traditionally called “Data Fusion Center” in the literature), where those get unified for global decision. Although this method suffers from O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg