Fusion of Vision and Inertial Data for Motion and Structure Estimation
Journal of Robotic Systems
Decentralized Bayesian detection with feedback
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Performance and geometric interpretation for decision fusion with memory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Algorithmic data fusion for multi-sensory system becomes extremely challenging, particularly where the elemental sensory units do vary in type, size and characteristics. 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 a point-mass to a finite spatial dimension. The present paper dwells on modeling, algorithm and experimental analysis of two novel fusion rule-bases, which are implemented in a small-sized tactile array sensor to be used in robot gripper. A new proposition has also 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-à-vis slippage characteristics, if any.