Autonomous Port Navigation With Ranging Sensors Using Model-Based Reinforcement Learning
This work addresses a critical gap in autonomous shipping for sustainable port operations, though it is incremental as it applies an existing machine learning method to a new domain.
The paper tackles autonomous navigation for inland vessels in complex port environments with dynamic obstacles like small boats and buoys, proposing a model-based reinforcement learning algorithm that outperforms the dynamic window approach and a model-free reinforcement learning benchmark by generalizing to unseen scenarios through randomized training.
Autonomous shipping has recently gained much interest in the research community. However, little research focuses on inland - and port navigation, even though this is identified by countries such as Belgium and the Netherlands as an essential step towards a sustainable future. These environments pose unique challenges, since they can contain dynamic obstacles that do not broadcast their location, such as small vessels, kayaks or buoys. Therefore, this research proposes a navigational algorithm which can navigate an inland vessel in a wide variety of complex port scenarios using ranging sensors to observe the environment. The proposed methodology is based on a machine learning approach that has recently set benchmark results in various domains: model-based reinforcement learning. By randomizing the port environments during training, the trained model can navigate in scenarios that it never encountered during training. Furthermore, results show that our approach outperforms the commonly used dynamic window approach and a benchmark model-free reinforcement learning algorithm. This work is therefore a significant step towards vessels that can navigate autonomously in complex port scenarios.