Sublevel Set Approximation in the Hausdorff and Volume Metric With Application to Path Planning and Obstacle Avoidance

Under what circumstances does the “closeness” of two functions imply the “closeness” of their respective sublevel sets? In this article, we answer this question by showing that if a sequence of functions converges strictly from above/below to a function, $V$, in the $L^\infty$ (or $L^{1}$) norm then these functions yield a sequence sublevel sets that converge to the sublevel set of $V$ with respect to the Hausdorff metric (or volume metric). Based on these theoretical results we propose sum-of-squares numerical schemes for the optimal outer/inner polynomial sublevel set approximation of various sets, including intersections and unions of semialgebraic sets, Minkowski sums, Pontryagin differences, and discrete points. We present several numerical examples demonstrating the usefulness of our proposed algorithm including approximating sets of discrete points to solve machine learning one-class classification problems and approximating Minkowski sums to construct C-spaces for computing optimal collision-free paths for Dubin's car.