MarineFormer: A Spatio-Temporal Attention Model for USV Navigation in Dynamic Marine Environments

Navigating autonomously in marine environments with dynamic and static obstacles, and strong flow disturbances, such as in high-flow rivers, poses significant challenges for USVs. To address these challenges, we propose a novel methodology that leverages two types of attention: spatial attention, which learns to integrate diverse environmental factors and sensory information into navigation decisions, and temporal attention within a transformer framework to account for the dynamic, continuously changing nature of the environment. We devise MarineFormer, a Transformer-based navigation policy for dynamic Marine environments, trained end-to-end through reinforcement learning (RL). At its core, MarineFormer uses graph attention to capture spatial information and a transformer architecture to process temporal sequences in an environment that simulates a 2D turbulent marine condition involving multiple static and dynamic obstacles. We extensively evaluate the performance of the proposed method versus the state-of-the-art RL methods, as well as other classical planners. Our approach outperforms the state-of-the-art by nearly $20\%$ in episode completion success rate and additionally enhances the USV's path length efficiency.