提出分层语义A*算法，让机器人在复杂环境里更快更安全地导航。
HCOA*: Hierarchical Class-ordered A* for Navigation in Semantic Environments
- 基于环境层级结构设计分层语义搜索策略
- 计算时间减少最高达50%，路径质量接近最优
- 适合需要高效导航的智能机器人应用
本文解决混合几何/语义3D环境中机器人导航问题。给定环境的层级表示，目标是从起点到终点导航，满足特定安全约束并最小化计算开销。我们提出分层类序A*（HCOA*）算法，利用环境层级结构在混合几何-语义图中实现高效且安全的路径规划。通过在语义类别上建立全序关系，证明了该算法的理论性能保证。提出了三种高层节点分类方法：基于图神经网络、k-近邻和多数类方法。在两个3D场景图上的仿真评估显示，与现有最先进方法相比，HCOA*将导航计算时间降低高达50%，并在多种场景下保持近最优性能。
原文摘要 · Abstract (English)
This paper addresses the problem of robot navigation in mixed geometric/semantic 3D environments. Given a hierarchical representation of the environment, the objective is to navigate from a start position to a goal, while satisfying task-specific safety constraints and minimizing computational cost. We introduce Hierarchical Class-ordered A* (HCOA*), an algorithm that leverages the environment's hierarchy for efficient and safe path-planning in mixed geometric/semantic graphs. We use a total order over the semantic classes and prove theoretical performance guarantees for the algorithm. We propose three approaches for higher-layer node classification based on the semantics of the lowest layer: a Graph Neural Network method, a k-Nearest Neighbors method, and a Majority-Class method. We evaluate HCOA* in simulations on two 3D Scene Graphs, comparing it to the state-of-the-art and assessing the performance of each classification approach. Results show that HCOA* reduces the computational time of navigation by up to 50%, while maintaining near-optimal performance across a wide range of scenarios.
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