Fusion Strategies for Minimizing Sensing-Level Uncertainty in Manipulator Control

Abstract Humanoid robotic applications require robot to act and behave like human being. Following soft computing like approach human being can think, decide and control himself in unstructured dynamic surroundings, where a great degree of uncertainty exists in the information obtained through sensory organs. In the robotics domain also, one of the key issues in extracting useful knowledge from sensory data is that of coping with information as well as sensory uncertainty at various levels. In this paper a generalized fusion based hybrid classifier (ANN-FDD-FFA) has been developed and applied for validating on generated synthetic data from observation model as well as from real hardware robot. The fusion goal, selected here, is primarily to minimize uncertainties in robotic manipulation tasks that are based on internal (joint sensors) as well as external (vision camera) sensory information. The effectiveness of present methodology has been extensively studied with a specially configured experimental robot having five degrees of freedom and a simulated model of a vision guided manipulator. In the present investigation main uncertainty handling approach includes weighted parameter selection (of geometric fusion) by a trained neural network that is not available in standard manipulator robotic controller designs. These approaches in hybrid configuration has significantly reduce the uncertainty at different levels for faster and more accurate manipulator control as demonstrated here through rigorous simulations and experimentations.

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