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

2025-07-21

AI Summary

Sampling-based motion planners suffer from degraded performance in high-dimensional configuration spaces—particularly those containing narrow passages—due to low sampling efficiency. To address this, we propose an online adaptive multi-resolution sampling framework that dynamically balances sparse and dense sampling without requiring prior training or hand-crafted heuristics. Our key innovation is a selective refinement mechanism enabling real-time adjustment of sampling granularity. By integrating uniform random sampling across multi-resolution spatial exploration with a bidirectional search strategy, the algorithm prioritizes sparse exploration in SE(2), SE(3), and ℝ¹⁴ configuration spaces. Experimental results demonstrate that our approach significantly outperforms state-of-the-art sampling-based planners on complex terrains and the Franka Emika Panda robotic platform, achieving superior computational efficiency, probabilistic completeness, and practical deployability.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Improves sampling efficiency in high-dimensional motion planning
• Addresses narrow passage challenges in complex configuration spaces
• Enhances generalizability without extensive prior training

Innovation

• Multi-resolution sampling for efficient planning
• Bidirectional version enhances planning speed
• Seamless sparse-dense sample transition