OceanPlan：大規模未探査海洋環境における自然言語AUV操縦のための階層的計画・再計画
OceanPlan: Hierarchical Planning and Replanning for Natural Language AUV Piloting in Large-scale Unexplored Ocean Environments
大規模で未知の海洋環境でAUVを自然言語で操縦するため、LLMとタスク・動作計画を組み合わせた階層的計画・再計画フレームワークを提案し、長時間ミッションの実現を検証した。
著者: Ruochu Yang, Fumin Zhang, Mengxue Hou
分類: cs.RO
原文アブストラクト
We develop a hierarchical LLM-task-motion planning and replanning framework to efficiently ground an abstracted human command into tangible Autonomous Underwater Vehicle (AUV) control through enhanced representations of the world. We also incorporate a holistic replanner to provide real-world feedback with all planners for robust AUV operation. While there has been extensive research in bridging the gap between LLMs and robotic missions, they are unable to guarantee success of AUV applications in the vast and unknown ocean environment. To tackle specific challenges in marine robotics, we design a hierarchical planner to compose executable motion plans, which achieves planning efficiency and solution quality by decomposing long-horizon missions into sub-tasks. At the same time, real-time data stream is obtained by a replanner to address environmental uncertainties during plan execution. Experiments validate that our proposed framework delivers successful AUV performance of long-duration missions through natural language piloting.
