Probabilistic validation of a stochastic kinematic model for an eight-legged robot

Abstract

The paper suggests a new method for statistically validating, and selecting the parameters of a model for a miniature eight-legged robot. It is based on a novel adaptation of concepts and techniques originally developed in the context of robust control design using randomized algorithms. The proposed approach is data driven and offers probabilistic guarantees of model fidelity and descriptive capacity, checking models against experimental data. In principle, this method applies to a large class of physical processes, the available models of which may be in a variety of forms including sets of differential equations.