Autonomous Port Navigation With Ranging Sensors Using Model-Based Reinforcement Learning
CoRR(2023)
Abstract
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.
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