Distributed AI Agents for Cognitive Underwater Robot Autonomy

2025-07-31

AI Summary

Cognitive autonomous navigation in complex, dynamic underwater environments remains challenging due to environmental uncertainty, unpredictable disturbances, and the need for rapid adaptation to novel tasks. Method: This paper proposes UROSA—a ROS 2 framework based on distributed large language model (LLM) agents—integrating role-adaptive agents, vector-database–driven retrieval-augmented generation (RAG), reinforcement learning–optimized behavioral policies, and runtime autonomous ROS 2 node generation to enable closed-loop multimodal perception, dynamic task planning, and real-time decision-making. Contribution/Results: UROSA significantly enhances generalization and adaptability to unknown environments, transient disturbances, and unseen mission objectives compared to conventional rule-based approaches. Extensive evaluation in both high-fidelity underwater simulations and real-world submersible platforms demonstrates its superior robustness, scalability, and mission completion reliability.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Multiagent Systems: Multiagent Systems under Uncertainty
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Achieving robust cognitive autonomy in robots navigating complex, unpredictable environments remains a fundamental challenge in robotics. This paper presents Underwater Robot Self-Organizing Autonomy (UROSA), a groundbreaking architecture leveraging distributed Large Language Model AI agents integrated within the Robot Operating System 2 (ROS 2) framework to enable advanced cognitive capabilities in Autonomous Underwater Vehicles. UROSA decentralises cognition into specialised AI agents responsible for multimodal perception, adaptive reasoning, dynamic mission planning, and real-time decision-making. Central innovations include flexible agents dynamically adapting their roles, retrieval-augmented generation utilising vector databases for efficient knowledge management, reinforcement learning-driven behavioural optimisation, and autonomous on-the-fly ROS 2 node generation for runtime functional extensibility. Extensive empirical validation demonstrates UROSA's promising adaptability and reliability through realistic underwater missions in simulation and real-world deployments, showing significant advantages over traditional rule-based architectures in handling unforeseen scenarios, environmental uncertainties, and novel mission objectives. This work not only advances underwater autonomy but also establishes a scalable, safe, and versatile cognitive robotics framework capable of generalising to a diverse array of real-world applications.

Problem

• Achieving robust cognitive autonomy in underwater robots
• Decentralising cognition for perception and decision-making in AUVs
• Handling unforeseen scenarios in unpredictable underwater environments

Innovation

• Distributed AI agents for cognitive autonomy
• Retrieval-augmented generation with vector databases
• Reinforcement learning-driven behavioral optimization