An energy-optimized A* algorithm for path planning of autonomous underwater vehicles in dynamic flow fields

Abstract

This paper presents the development and implementation of an energy-optimized A* algorithm for autonomous underwater vehicle (AUV) path planning in these complex environments. The core of the approach is the integration of a computationally efficient flow field model and a detailed AUV energy consumption model directly into the A* search heuristic. The energy model considers factors such as drag forces, relative velocity between the AUV and the flow, and AUV maneuvering. The A* cost function is modified to prioritize paths that minimize the predicted total energy expenditure, while simultaneously ensuring obstacle avoidance and path feasibility. The algorithm was implemented and validated using a simulated environment with varying flow conditions. Results demonstrate that the proposed energy-optimized A* algorithm achieves a significant reduction in energy consumption – up to 50% in tested scenarios – compared to a standard A* implementation, while successfully generating collision-free and dynamically feasible paths. This work contributes a practical and effective solution for energy-aware AUV navigation in dynamic underwater environments, enabling longer mission durations and improved operational efficiency.