Depth-Constrained ASV Navigation with Deep RL and Limited Sensing
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918170703429632 |
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| author | Zhalehmehrabi, Amirhossein Meli, Daniele Santo, Francesco Dal Trotti, Francesco Farinelli, Alessandro |
| author_facet | Zhalehmehrabi, Amirhossein Meli, Daniele Santo, Francesco Dal Trotti, Francesco Farinelli, Alessandro |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_18253 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Depth-Constrained ASV Navigation with Deep RL and Limited Sensing Zhalehmehrabi, Amirhossein Meli, Daniele Santo, Francesco Dal Trotti, Francesco Farinelli, Alessandro Robotics Artificial Intelligence 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. |
| title | Depth-Constrained ASV Navigation with Deep RL and Limited Sensing |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2504.18253 |