FPAS: Frontier-Based Path Planning with Adaptive Sampling for Large-Scale Unknown Environments

2026-06-22

AI Summary

This work addresses the challenges of low computational efficiency and suboptimal paths in long-range navigation within large-scale, unknown environments. The authors propose a frontier-guided path planning framework that innovatively leverages frontiers to guide goal-directed forward exploration and dead-end backtracking decisions. A key contribution is an adaptive graph sampling strategy based on frontier openness, which sparsely samples in open areas and densely samples in narrow passages to dynamically balance graph connectivity with computational cost. Experimental results demonstrate that the proposed method significantly outperforms existing baselines, achieving a superior trade-off between computational efficiency and path quality while maintaining a high goal-reaching success rate.

Abstract

In this work, we propose Frontier-based Path Planning with Adaptive Sampling (FPAS), a novel framework designed for efficient goal-reaching in large-scale, unknown environments. While existing planners often struggle with computational bottlenecks or inefficient paths during long-range navigation, FPAS overcomes these challenges by reinterpreting the frontier concept for goal-directed tasks. Specifically, our method leverages frontiers to effectively guide forward progression into unobserved regions and to select promising subgoals for backtracking from dead-ends or inefficient paths. Furthermore, FPAS introduces an adaptive sampling mechanism based on a frontier-derived openness metric. This mechanism dynamically adjusts the global graph's density by employing sparse nodes in open areas to alleviate computational burdens, while preserving denser sampling in narrow passages to ensure connectivity. Extensive evaluations demonstrate that FPAS substantially improves computational efficiency over baseline methods while maintaining highly competitive goal-reaching performance.

Problem

• path planning
• large-scale environments
• unknown environments
• computational efficiency
• goal-reaching

Innovation

• frontier-based planning
• adaptive sampling
• unknown environments
• goal-directed navigation
• computational efficiency