Multidisciplinary Monte Carlo Robust Optimization for AUVs under Polar Ice Multi-Source Uncertainties

Abstract

The exploration of polar regions has become increasingly critical due to rapid climate change and the strategic significance of Arctic resources. Autonomous Underwater Vehicles (AUVs) serve as the primary modality for data acquisition in these harsh, ice-covered environments. However, the operational reliability of AUVs is severely compromised by multi-source uncertainties, including complex under-ice hydrodynamics, acoustic communication failures, variable sea ice draft, and stochastic sensor errors. Traditional deterministic optimization methods often fail to account for these variabilities, leading to designs that are optimal in theory but fragile in practice. This paper proposes a comprehensive Multidisciplinary Design Optimization (MDO) framework integrated with Monte Carlo simulations to enhance the robustness of AUVs operating under polar ice. We define a stochastic coupling mechanism that links hydrodynamic, structural, energy, and navigation subsystems. By embedding Monte Carlo sampling within the optimization loop, we evaluate the probability of mission success and enforce reliability constraints against environmental and systemic uncertainties. The results demonstrate that the proposed robust optimization approach significantly reduces the variance in mission performance metrics compared to deterministic baselines. We provide a detailed analysis of the trade-offs between optimality and reliability, offering a theoretical foundation for the next generation of polar-class AUVs.