LES: Locally Exploitative Sampling for Robot Path Planning

TL;DR

This paper introduces LES, a sampling method for robot path planning that exploits local information to accelerate convergence to optimal paths, outperforming exploration-biased methods in higher-dimensional tasks.

Contribution

LES is a novel exploitative sampling strategy that improves convergence speed in sampling-based path planning algorithms by focusing on local cost-to-come optimization.

Findings

Faster convergence to optimal solutions compared to state-of-the-art methods.

Effective in higher-dimensional robotic planning tasks.

Demonstrated through benchmarking experiments.

Abstract

Sampling-based algorithms solve the path planning problem by generating random samples in the search-space and incrementally growing a connectivity graph or a tree. Conventionally, the sampling strategy used in these algorithms is biased towards exploration to acquire information about the search-space. In contrast, this work proposes an optimization-based procedure that generates new samples to improve the cost-to-come value of vertices in a neighborhood. The application of proposed algorithm adds an exploitative-bias to sampling and results in a faster convergence to the optimal solution compared to other state-of-the-art sampling techniques. This is demonstrated using benchmarking experiments performed fora variety of higher dimensional robotic planning tasks.