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Autonomous Surface Vehicles (ASVs) play a crucial role in maritimeoperations, yet their navigation in shallow-water environments remainschallenging due to dynamic disturbances and depth constraints. Traditionalnavigation strategies struggle with limited sensor information, making safe andefficient operation difficult. In this paper, we propose a reinforcementlearning (RL) framework for ASV navigation under depth constraints, where thevehicle must reach a target while avoiding unsafe areas with only a singledepth measurement per timestep from a downward-facing Single Beam Echosounder(SBES). To enhance environmental awareness, we integrate Gaussian Process (GP)regression into the RL framework, enabling the agent to progressively estimatea bathymetric depth map from sparse sonar readings. This approach improvesdecision-making by providing a richer representation of the environment.Furthermore, we demonstrate effective sim-to-real transfer, ensuring thattrained policies generalize well to real-world aquatic conditions. Experimentalresults validate our method's capability to improve ASV navigation performancewhile maintaining safety in challenging shallow-water environments.

Depth-Constrained ASV Navigation with Deep RL and Limited Sensing