LLM支援計画と分散オンボード行動ツリー実行による複数AUVミッション
LLM-Assisted Planning with Distributed Onboard Behaviour Tree Execution for Multi-AUV Missions
自然言語のミッション指示をLLMで解釈し、MILPで割当を最適化、分散行動ツリーで各AUVが自律実行する階層型フレームワークを提案。故障時には影響を受けた機体のみ選択的に再計画する。
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著者: Ehsan Khorrambakht, Cristiano Caissutti, Andrea Munafo, Andrea Caiti
分類: cs.RO
原文アブストラクト
Coordinating autonomous underwater vehicles requires translating operator intent into feasible assignments while preserving autonomous execution when communication is limited. We present a hierarchical framework that couples Large Language Model (LLM) interpretation of natural-language mission instructions with Mixed-Integer Linear Programming (MILP) and distributed Behaviour Tree (BT) execution. The LLM extracts assignment and ordering constraints, which the MILP optimizer combines with vehicle capabilities and temporal requirements. A BT-LLM Bridge distributes the resulting allocations to onboard BT Mission Managers. Per-vehicle Robotic Agents assess local faults and request selective replanning, allowing unaffected vehicles to continue their assigned tasks. Evaluation in a ROS-based AUV simulator compares Qwen-3-8B and Llama-3.1-8B on constraint extraction and tests recovery from sensor degradation and critical vehicle faults. Qwen-3-8B provides more accurate constraint extraction, and all tasks are completed in the tested failure scenarios. These simulation results support the feasibility of combining language-based mission specification with structured allocation and distributed execution.