  In the realm of maritime transportation, autonomous vessel navigation in
natural inland waterways faces persistent challenges due to unpredictable
natural factors. Existing scheduling algorithms fall short in handling these
uncertainties, compromising both safety and efficiency. Moreover, these
algorithms are primarily designed for non-autonomous vessels, leading to
labor-intensive operations vulnerable to human error. To address these issues,
this study proposes a risk-aware motion control approach for vessels that
accounts for the dynamic and uncertain nature of tide islands in a
distributionally robust manner. Specifically, a model predictive control method
is employed to follow the reference trajectory in the time-space map while
incorporating a risk constraint to prevent grounding accidents. To address
uncertainties in tide islands, a novel modeling technique represents them as
stochastic polytopes. Additionally, potential inaccuracies in waterway depth
are addressed through a risk constraint that considers the worst-case
uncertainty distribution within a Wasserstein ambiguity set around the
empirical distribution. Using sensor data collected in the Guadalquivir River,
we empirically demonstrate the performance of the proposed method through
simulations on a vessel. As a result, the vessel successfully navigates the
waterway while avoiding grounding accidents, even with a limited dataset of
observations. This stands in contrast to existing non-robust controllers,
highlighting the robustness and practical applicability of the proposed
approach.
