Selective Densification for Rapid Motion Planning in High Dimensions with Narrow Passages

TL;DR

This paper introduces a multi-resolution sampling framework for high-dimensional motion planning that adaptively switches between sparse and dense samples, improving efficiency and success rates in complex spaces.

Contribution

The proposed method integrates multi-resolution sampling with online biasing, enabling rapid and reliable planning in challenging high-dimensional environments without extensive prior training.

Findings

Outperforms state-of-the-art planners in various high-dimensional spaces.

Demonstrates effectiveness on complex terrains and real robot scenarios.

Maintains planning speed and completeness across diverse configurations.

Abstract

Sampling-based algorithms are widely used for motion planning in high-dimensional configuration spaces. However, due to low sampling efficiency, their performance often diminishes in complex configuration spaces with narrow corridors. Existing approaches address this issue using handcrafted or learned heuristics to guide sampling toward useful regions. Unfortunately, these strategies often lack generalizability to various problems or require extensive prior training. In this paper, we propose a simple yet efficient sampling-based planning framework along with its bidirectional version that overcomes these issues by integrating different levels of planning granularity. Our approach probes configuration spaces with uniform random samples at varying resolutions and explores these multi-resolution samples online with a bias towards sparse samples when traveling large free configuration spaces. By seamlessly transitioning between sparse and dense samples, our approach can navigate complex configuration spaces while maintaining planning speed and completeness. The simulation results demonstrate that our approach outperforms several state-of-the-art sampling-based planners in $\mathbb{SE}(2)$, $\mathbb{SE}(3)$, and $\mathbb{R}^{14}$ with challenging terrains. Furthermore, experiments conducted with the Franka Emika Panda robot operating in a constrained workspace provide additional evidence of the superiority of the proposed method.