  Many path planning algorithms are based on sampling the state space. While
this approach is very simple, it can become costly when the obstacles are
unknown, since samples hitting these obstacles are wasted. The goal of this
paper is to efficiently identify obstacles in a map and remove them from the
sampling space. To this end, we propose a pre-processing algorithm for space
exploration that enables more efficient sampling. We show that it can boost the
performance of other space sampling methods and path planners.
  Our approach is based on the fact that a convex obstacle can be approximated
provably well by its minimum volume enclosing ellipsoid (MVEE), and a
non-convex obstacle may be partitioned into convex shapes. Our main
contribution is an algorithm that strategically finds a small sample, called
the \emph{active-coreset}, that adaptively samples the space via
membership-oracle such that the MVEE of the coreset approximates the MVEE of
the obstacle. Experimental results confirm the effectiveness of our approach
across multiple planners based on Rapidly-exploring random trees, showing
significant improvement in terms of time and path length.
