Redundancy resolution in tasks with parametrizable uncertainty

Redundant robots motion planning and control in uncertain task is addressed by model-based approach. Instead of control and adapt the environment to the robot or apply a complex visual servoing system, we have modeled the redundancy resolution (RR) on the parameter spaces that quantify uncertainties of the task. A modeling tool was Successive Approximations (SA). It provides very advantageous properties: small computational effort and small model size, accurate output, extrapolation, and generalization across parameter set obtained by random addressing of the model. The task discussed is press loading with typical two-dimensional uncertainties in pick-up and unloading locations. The robot used is 4 DOF planar robot. The SA-based models of redundancy resolution in the 2D parameter spaces are highly efficient: for more that 30 times less computational efforts resulted in a zero end-point errors, regardless of the task uncertainty.