Modular Trajectory Planning for Autonomous Vessels in Inland Waters Using Electronic Nautical Charts
J. Vogel (TU Delft - Mechanical Engineering)
J. Alonso-Mora – Mentor (TU Delft - Learning & Autonomous Control)
Kasper van der El – Mentor (Damen Shipyards)
L. Ferranti – Graduation committee member (TU Delft - Learning & Autonomous Control)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Autonomous navigation on inland waterways is challenging: channel geometry can vary widely, traffic rules are qualitative, and vessels must interact safely with other traffic while operating near infrastructure. This thesis presents a modular two-stage planning framework for a high-speed passenger ferry (Damen Waterbus) operating between Rotterdam and Dordrecht. A global planner extracts routes from Electronic Navigational Charts (ENCs) and refines them to produce a starboard-biased global path. A local planner uses Biased Model Predictive Path Integral (BIASED-MPPI) with ancillary controllers for path-following, goal reaching, and cruise speed regulation. Its cost function adapts based on the traffic scenario. Together, the planners generate starboard-biased trajectories that respect vessel limits, traffic and waterway constraints.
Global paths are built using the waterway-axis object extracted from the ENCs, and are made starboard-biased by projecting to the Fairway. This path is then refined using simple windowed smoothing schemes (minimum, average, and a hybrid), applied to the projection distances. The best smoothing configurations are explored via a grid search over various parameters. The generated paths are evaluated by metrics that assess feasibility. Across diverse intersections, smoothing noticeably reduces irregularity and sharp turns. The hybrid variant offers the most consistent gains, though feasibility in difficult geometries remains the dominant failure mode.
The local planner runs in real time and handles three encounter types (head-on, crossing, overtaking) using a standardised COLREG state definition to trigger rule-consistent behaviour. Variants without learned priors or with reduced replanning frequency are compared to highlight the effect of priors and update rate on success, stability, and computation time. Human-likeness is assessed against the Triton dataset, consisting of Waterbus AIS data via dock-to-dock distance and travel-time benchmarks.
Overall, the study demonstrates that combining ENC-informed global planning with a COLREG-aware MPPI controller yields safe, efficient, and human-like dock-to-dock trajectories in structured inland waterways, while identifying where the proposed method still limits performance.