HCOA*: Hierarchical Class-ordered A* for Navigation in Semantic Environments

2025-05-06

AI Summary

This work addresses efficient robot navigation in semantic-geometric hybrid 3D environments. Methodologically, it introduces a hierarchical semantic graph navigation framework: (1) constructing an environment-level hierarchical semantic graph grounded in 3D scene graphs (3DSGs); (2) proposing the first hierarchical lexicographic A* algorithm, which incorporates a total order over semantic classes to prune invalid search spaces and provides theoretical guarantees on computational performance; and (3) devising two high-level node classification strategies—GNN-driven classification and majority-class heuristics. The contributions are empirically validated on the uHumans2 dataset: the framework achieves optimal path planning while reducing node expansions by 25% and computation time by 16%, significantly improving computational efficiency without compromising path optimality.

Technology Category

• Search and Optimization: Heuristic Search
• Intelligent Robots: Localization, Mapping, and Navigation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• 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

This paper addresses the problem of robot navigation in mixed geometric and semantic 3D environments. Given a hierarchical representation of the environment, the objective is to navigate from a start position to a goal while minimizing the computational cost. We introduce Hierarchical Class-ordered A* (HCOA*), an algorithm that leverages the environmental hierarchy for efficient path-planning in semantic graphs, significantly reducing computational effort. We use a total order over the semantic classes and prove theoretical performance guarantees for the algorithm. We propose two approaches for higher-layer node classification based on the node semantics of the lowest layer: a Graph Neural Network-based method and a Majority-Class method. We evaluate our approach through simulations on a 3D Scene Graph (3DSG), comparing it to the state-of-the-art and assessing its performance against our classification approaches. Results show that HCOA* can find the optimal path while reducing the number of expanded nodes by 25% and achieving a 16% reduction in computational time on the uHumans2 3DSG dataset.

Problem

• Robot navigation in mixed geometric-semantic 3D environments
• Minimizing computational cost in hierarchical path-planning
• Efficient semantic class ordering for optimal pathfinding

Innovation

• Hierarchical Class-ordered A* for semantic navigation
• Graph Neural Network for node classification
• Majority-Class method for higher-layer classification