Distributed AI Agents for Cognitive Underwater Robot Autonomy

TL;DR

This paper introduces UROSA, a novel distributed AI architecture using large language models within ROS 2 to enhance autonomous underwater robot capabilities, demonstrating improved adaptability and decision-making in complex environments.

Contribution

The paper presents UROSA, a pioneering distributed AI framework integrating LLMs and ROS 2 for advanced underwater robot cognition and real-time adaptability.

Findings

UROSA outperforms traditional rule-based systems in complex scenarios.

Empirical validation shows high reliability in real-world underwater missions.

The architecture enables dynamic role adaptation and on-the-fly node generation.

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.