Never too Prim to Swim: An LLM-Enhanced RL-based Adaptive S-Surface Controller for AUVs under Extreme Sea Conditions (2503.00527v1)

Abstract: The adaptivity and maneuvering capabilities of Autonomous Underwater Vehicles (AUVs) have drawn significant attention in oceanic research, due to the unpredictable disturbances and strong coupling among the AUV's degrees of freedom. In this paper, we developed LLM-enhanced reinforcement learning (RL)-based adaptive S-surface controller for AUVs. Specifically, LLMs are introduced for the joint optimization of controller parameters and reward functions in RL training. Using multi-modal and structured explicit task feedback, LLMs enable joint adjustments, balance multiple objectives, and enhance task-oriented performance and adaptability. In the proposed controller, the RL policy focuses on upper-level tasks, outputting task-oriented high-level commands that the S-surface controller then converts into control signals, ensuring cancellation of nonlinear effects and unpredictable external disturbances in extreme sea conditions. Under extreme sea conditions involving complex terrain, waves, and currents, the proposed controller demonstrates superior performance and adaptability in high-level tasks such as underwater target tracking and data collection, outperforming traditional PID and SMC controllers.

Examination of LLM-Enhanced RL-Based Adaptive S-Surface Controller for AUVs

This paper presents an exploration into the design and efficacy of an innovative controller for Autonomous Underwater Vehicles (AUVs), utilized to perform complex and high-level tasks while navigating extreme marine conditions. The controller proposed integrates strategic enhancements from LLMs to refine Reinforcement Learning-based (RL) adaptive S-surface control, setting a novel standard for AUV control systems.

Key Contributions and Innovation

The paper delineates three principal contributions:

1. Development of a Novel Controller Architecture: The proposed controller leverages reinforcement learning to forge advanced, expert-level decision-making capabilities in AUVs. By utilizing an S-surface controller to manage dynamic motions, this architecture assures cancellation of nonlinear influences and external perturbations typical in severe sea conditions.
2. Integration of LLMs for Parameter Optimization: Through joint optimization processes, LLMs play a crucial role in adjusting both the RL reward function and the controller parameters. This integration employs a multi-modal approach incorporating environmental data and performance logs, thus significantly enhancing adaptability and task-oriented performance.
3. Superior Performance in Challenging Environments: Extensive simulations demonstrate that the controller surpasses traditional PID and Sliding Mode Control (SMC) frameworks in extreme environmental models featuring waves, currents, and complex terrain. This result is underpinned by particularly robust 3D task performance, such as underwater target tracking and data collection activities.

Methodology and Technical Approach

The paper explores sophisticated methodologies, leveraging both theoretical and practical frameworks to underpin the proposed controller:

• Reinforcement Learning and S-Surface Controller: The synergy between RL policies and the S-surface controller facilitates a comprehensive approach toward achieving and maintaining high-precision control over AUVs. The RL methodology is articulated through an MDP framework, assigning and adapting high-level commands into actionable control signals. This complements the nonlinear S-surface dynamics for effective environmental engagement.
• Iterative Joint Optimization via LLMs: The intricacy of controller reward tuning necessitates a well-structured optimization mechanism. With LLMs, the paper promotes an iterative refinement process, targeting a finely balanced multi-objective optimization tailored specifically to dynamic underwater conditions.
• Simulation and Environmental Modeling: Embodying a detailed maritime simulation framework anchored in physic-based ocean modeling and environment awareness, the design systematically validates the control system’s adaptability and resilience. This ensures robust performance aside substantial, real-world-inspired disturbances like wave spectra derived from the JONSWAP model.

Discussion on Experimental Findings

Presented results embody strong numerical support for the controller’s superior stability and response dexterity. Under comparative trials, the adaptive S-surface controller maintains notable fidelity to designed trajectories against benchmark controllers across multiple evaluated tasks. Specifically, the experimental setup showcased performance metrics wherein tracking response time, error mitigation, and energy efficiency were quantitatively validated, confirming outperformances in rough sea simulations.

Implications and Future Outlook

While the paper delineates clear immediate benefits in optimizing AUV control systems, its methodology holds broader implications, potentially transferrable across various domains involving highly dynamic and unpredictable conditions. The paper Germane presents a significant step in using LLMs for refinements in control systems previously hindered by computational lag within marine environments.

Future research directives, as outlined, involve transitioning the theoretical and model-based gains of this paper into real-world applications. The anticipated sim2real transfer strategy will address intrinsic discrepancies, ensuring the controller's practical applicability and reliability once deployed in authentic scenarios.

The work contributes cogently to the field, demonstrating meticulous integration of advanced computational strategies in real-time autonomous systems. It serves as a foundation for ongoing exploration aimed at reconciling theoretical advances with practical demands facing next-generation autonomous systems navigating complex operational domains.