Depth-Constrained ASV Navigation with Deep RL and Limited Sensing
This addresses the challenge of safe and efficient ASV navigation for maritime operations in shallow waters, representing an incremental advance by combining existing RL and GP methods for a specific domain.
The paper tackles the problem of autonomous surface vehicle navigation in shallow-water environments with depth constraints and limited sensing, proposing a reinforcement learning framework integrated with Gaussian Process regression to estimate bathymetric maps from sparse sonar data, and demonstrates improved navigation performance and safety in experiments.
Autonomous Surface Vehicles (ASVs) play a crucial role in maritime operations, yet their navigation in shallow-water environments remains challenging due to dynamic disturbances and depth constraints. Traditional navigation strategies struggle with limited sensor information, making safe and efficient operation difficult. In this paper, we propose a reinforcement learning (RL) framework for ASV navigation under depth constraints, where the vehicle must reach a target while avoiding unsafe areas with only a single depth 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 estimate a bathymetric depth map from sparse sonar readings. This approach improves decision-making by providing a richer representation of the environment. Furthermore, we demonstrate effective sim-to-real transfer, ensuring that trained policies generalize well to real-world aquatic conditions. Experimental results validate our method's capability to improve ASV navigation performance while maintaining safety in challenging shallow-water environments.