Article Details

Abstract

To address the problems of context fragmentation and macro-micro structure disconnection in graph retrieval for the Robot Operating System (ROS), this paper proposes a Hierarchical Graph Retrieval-Augmented Generation framework (HiGraphRAG). The framework repairs physical connectivity faults through global graph reorganization, constructs a bottom-up hierarchical summary index based on Gaussian Mixture Models (GMM), and adopts a perspective expansion mechanism to realize the accurate recall of multi-granularity structured knowledge. Experimental results on a self-built ROS dataset show that compared with the mainstream GraphRAG paradigm, HiGraphRAG achieves an overall generation quality win rate of 56.4% and a knowledge diversity win rate of 60.2%. The framework effectively bridges system information gaps and significantly improves the comprehensiveness and knowledge empowerment of large language models in complex reasoning.

Keywords

Retrieval-Augmented Generation; Knowledge Graph; robot operating system; hierarchical index; hierarchical retrieval

References

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About the authors

Shi Yalong (1999-), male, Han ethnicity, from Yichang, Hubei, master's student, research interests include large models; Corresponding author: Zhao Jun (1984-), male, Han ethnicity, from Zigong, Sichuan, associate professor, doctoral student, research interests include networked control systems and time-varying formation control of multi-agent systems; Hou Jing (1968-), male, Han ethnicity, from Zigong, Sichuan, professor, doctoral student, research interests include artificial intelligence and intelligent detection.