A Non-uniform Sampling Approach for Fast and Efficient Path Planning

TL;DR

This paper introduces a non-uniform sampling method for autonomous vehicle path planning that improves efficiency by adaptively sampling obstacle-rich and obstacle-free regions, reducing computation and enhancing convergence.

Contribution

The paper presents a novel non-uniform partitioning scheme and sampling approach integrated into RRT* that guarantees completeness and optimality while improving speed and memory efficiency.

Findings

Faster convergence rate of RRT* with the non-uniform sampler

Reduced memory footprint compared to uniform sampling

Elimination of expensive collision checks in convex cells

Abstract

In this paper, we develop a non-uniform sampling approach for fast and efficient path planning of autonomous vehicles. The approach uses a novel non-uniform partitioning scheme that divides the area into obstacle-free convex cells. The partitioning results in large cells in obstacle-free areas and small cells in obstacle-dense areas. Subsequently, the boundaries of these cells are used for sampling; thus significantly reducing the burden of uniform sampling. When compared with a standard uniform sampler, this smart sampler significantly 1) reduces the size of the sampling space while providing completeness and optimality guarantee, 2) provides sparse sampling in obstacle-free regions and dense sampling in obstacle-rich regions to facilitate faster exploration, and 3) eliminates the need for expensive collision-checking with obstacles due to the convexity of the cells. This sampling framework is incorporated into the RRT* path planner. The results show that RRT* with the non-uniform sampler gives a significantly better convergence rate and smaller memory footprint as compared to RRT* with a uniform sampler.