AI-Enhanced Automatic Design of Efficient Underwater Gliders

2025-04-30

AI Summary

Conventional underwater glider design relies on manual trial-and-error and suffers from limited geometric expressivity, hindering simultaneous optimization of energy efficiency and structural complexity. Method: We propose the first end-to-end, AI-driven autonomous design framework that jointly optimizes hull geometry and control signals via gradient-based co-optimization, integrating a differentiable neural fluid surrogate model with a dimensionality-reduced parametric geometric representation. This approach overcomes the prohibitive computational cost of high-fidelity fluid–structure interaction simulations and enables inverse design under complex flow conditions. Contribution/Results: Experimentally, the optimized glider achieves significantly higher energy efficiency in pool tests compared to expert-designed baselines; wind-tunnel validation confirms fluid performance prediction errors below 5%. This work establishes a new paradigm for automated, physics-informed design of high-performance underwater vehicles.

Technology Category

• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

The development of novel autonomous underwater gliders has been hindered by limited shape diversity, primarily due to the reliance on traditional design tools that depend heavily on manual trial and error. Building an automated design framework is challenging due to the complexities of representing glider shapes and the high computational costs associated with modeling complex solid-fluid interactions. In this work, we introduce an AI-enhanced automated computational framework designed to overcome these limitations by enabling the creation of underwater robots with non-trivial hull shapes. Our approach involves an algorithm that co-optimizes both shape and control signals, utilizing a reduced-order geometry representation and a differentiable neural-network-based fluid surrogate model. This end-to-end design workflow facilitates rapid iteration and evaluation of hydrodynamic performance, leading to the discovery of optimal and complex hull shapes across various control settings. We validate our method through wind tunnel experiments and swimming pool gliding tests, demonstrating that our computationally designed gliders surpass manually designed counterparts in terms of energy efficiency. By addressing challenges in efficient shape representation and neural fluid surrogate models, our work paves the way for the development of highly efficient underwater gliders, with implications for long-range ocean exploration and environmental monitoring.

Problem

• Limited shape diversity in underwater glider design
• High computational costs in solid-fluid interaction modeling
• Challenges in efficient shape and control co-optimization

Innovation

• AI-enhanced automated computational framework
• Co-optimizes shape and control signals
• Differentiable neural-network-based fluid model