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Journal Article
A Hierarchical LLM-Based Framework for Heterogeneous Multi-Robot Orchestration in High-Risk Energy Facility Maintenance
- Authors
- Jungi Lee, Seu-Jan Kim, Geonhyup Lee, Kangmin Kim, Jimin Jeon, Seok-Kap Ko, Kyoobin Lee
- Issue Date
- 2026-04
- Citation
- IEEE Access, v.14, pp.66881-66898
- ISSN
- 2169-3536
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- Maintenance and operation of critical energy infrastructure require extreme precision and strict adherence to safety protocols to prevent catastrophic failures. While recent advancements in general-purpose Vision-Language-Action (VLA) foundation models have shown promise in robotics, their inherent stochasticity and lack of procedural precision often result in unacceptable safety risks in high-stakes industrial environments. To address these limitations, this paper proposes a novel Hierarchical Multi-Robot Orchestration Framework designed for the coordinated control of heterogeneous robots in energy facility maintenance. The proposed framework decouples high-level cognitive reasoning from low-level execution by utilizing a local Large Language Model (LLM) as a strategic orchestrator. Grounded in a Structured Environmental Representation ( $\mathcal {M}$ ), the LLM employs Chain-of-Thought (CoT) reasoning to decompose complex maintenance missions into verifiable sub-tasks, effectively bridging the gap between linguistic intent and physical constraints. These tasks are then dispatched to specialized execution modules: a manipulation unit utilizing Action Chunking with Transformers (ACT) adapted for high-precision industrial tasks, an autonomous navigation unit for Simultaneous Localization and Mapping (SLAM)-based pathfinding, and a vision-guided logistics unit for retrieval. Experimental evaluations in a simulated power plant environment validate the efficacy of the framework. A comparative ablation study demonstrates that utilizing structured environmental grounding significantly enhances the reasoning reliability of large-scale models compared to unstructured text baselines. Specifically, the GPT-OSS (20B) model achieved a peak planning success rate of 91.7%, with a notable proficiency in handling long-horizon Strategic commands (73.3%), validating that structural clarity is essential for complex causal inference. Furthermore, the integrated execution layer demonstrated exceptional reliability, achieving a 90% success rate specifically in distribution panel maintenance tasks, confirming that the hierarchical decoupling of probabilistic reasoning and deterministic execution provides a reliable solution for autonomous maintenance.
- Keyword
- Autonomous maintenance, hierarchical task planning, large language models, learning from demonstration, multi-robot orchestration
- KSP Keywords
- AND operation, Causal Inference, Critical energy, Energy infrastructure, Facility Maintenance, Hierarchical decoupling, High risk, Industrial environment, Industrial tasks, Large-scale models, Learning from demonstration
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
