Wake-Informed 3D Path Planning for Autonomous Underwater Vehicles Using A* and Neural Network Approximations

• DOI: 10.48550/arxiv.2502.01918
• Publication Date: 2025-02-03
• Authors: Zachary Cooper-Ba...; Stephen R. Turnock; Karl Sammut
• Keywords: FOS: Computer and information sciences; I.2.8; I.2.9; I.5.1; Computer Science - Robotics; Computer Science - Machine Learning; I.5.1; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; I.2.8; I.2.9; 68T40, 68T07, 90C35; Robotics (cs.RO); Machine Learning (cs.LG)

Autonomous Underwater Vehicles (AUVs) encounter significant energy, control and navigation challenges in complex underwater environments, particularly during close-proximity operations, such as launch recovery (LAR), where fluid interactions wake effects present additional navigational energy challenges. Traditional path planning methods fail to incorporate these detailed structures, resulting increased consumption, reduced stability, heightened safety risks. This paper presents a novel wake-informed, 3D approach that fully integrates localized global currents into the algorithm. Two variants of A* algorithm - current-informed planner wake-informed are created assess its validity two neural network models then trained approximate planners for real-time applications. Both NN evaluated using important metrics expenditure, length, encounters with high-velocity turbulent regions. The results demonstrate consistently achieves lowest expenditure minimizes regions, reducing consumption by up 11.3%. observed offer computational speedup 6 orders magnitude, but exhibit 4.51 19.79% higher expenditures 9.81 24.38% less optimal paths. These findings underscore importance incorporating structures traditional algorithms benefits approximations enhance efficiency operational AUVs domains.