Measuring worst-case errors in a robot workcell

Errors in model parameters, sensing, and control are inevitably present in real robot systems. These errors must be considered in order to automatically plan robust solutions to many manipulation tasks. Lozano-Perez, Mason, and Taylor proposed a formal method for synthesizing robust actions in the presence of uncertainty; this method has been extended by several subsequent researchers. All of these results presume the existence of worst-case error bounds that describe the maximum possible deviation between the robot`s model of the world and reality. This paper examines the problem of measuring these error bounds for a real robot workcell. These measurements are difficult, because of the desire to completely contain all possible deviations while avoiding bounds that are overly conservative. The authors present a detailed description of a series of experiments that characterize and quantify the possible errors in visual sensing and motion control for a robot workcell equipped with standard industrial robot hardware. In addition to providing a means for measuring these specific errors, these experiments shed light on the general problem of measuring worst-case errors