OceanPlan: Hierarchical Planning and Replanning for Natural Language AUV Piloting in Large-scale Unexplored Ocean Environments
Year: 2024
Type: conference-paper
Abstract: 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.
Authors: Ruochu Yang, Fumin Zhang, Mengxue Hou
Institutions: Georgia Institute of Technology, University of Notre Dame
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Life below water
Open Access status: hybrid
Awards: CCRI: Medium: Collaborative Research: mu-Net: Infrastructure to Advance Mobile Underwater Wireless Networking Research, G1285204305, G1523888516, G1960259822, G2057323907, GCR: Biomolecular Systems Engineering - Unlocking the Potential of Biological Programming, G3018727821, NSF Convergence Accelerator Track E: Convergence Towards Nationwide Smart Precision Aquaculture Networks for Sustainable Shellfish Farming, G6098521345, G8876996369
OpenAlex ID: W4406864698