Obstacle Aware Sampling for Path Planning

TL;DR

This paper introduces an obstacle-aware pre-processing algorithm that efficiently identifies and approximates obstacles in a map, significantly improving the performance of sampling-based path planning algorithms by reducing wasted samples.

Contribution

It proposes a novel algorithm that uses convex approximation and adaptive sampling to better identify obstacles, enhancing path planning efficiency.

Findings

Significant reduction in planning time.

Shorter path lengths in experiments.

Effective obstacle approximation across multiple planners.

Abstract

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.