新算法通过自适应采样提升大环境导航效率。
FPAS: Frontier-Based Path Planning with Adaptive Sampling for Large-Scale Unknown Environments
- 用前沿点引导前进与回溯，动态规划子目标。
- 自适应采样使开放区稀疏、狭窄区密集，降低计算负担。
- 适合大规模未知环境下的实时机器人路径规划。
本文提出一种名为基于前沿的自适应采样路径规划（FPAS）的新框架，用于在大规模未知环境中高效实现目标到达。现有规划器在长距离导航中常面临计算瓶颈或路径低效问题。FPAS通过重新定义前沿概念，将其用于指导进入未观测区域，并选择有前景的子目标以从死胡同或低效路径回溯。此外，该方法引入基于前沿开放度的自适应采样机制：在开阔区域采用稀疏节点以减轻计算压力，而在狭窄通道保持高密度采样以保障连通性。大量实验表明，相比基线方法，FPAS显著提升了计算效率，同时保持了极具竞争力的目标达成性能。
原文摘要 · Abstract (English)
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.
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