Driven by the need to enhance safety, improve efficiency and address labour shortages, autonomous vessel operations are increasingly moving beyond open-water navigation toward more complex missions demanding integration of multiple control strategies. Dock-to-dock operations represent a critical mission encompassing the full spectrum of motion control challenges: long-distance transit, switching between control phases, and precise low-speed maneuvering in confined waters under environmental disturbances.

This thesis evaluates control strategies for autonomous dock-to-dock sailing on inland waterways. A benchmark system using industry standard PID controllers is compared to an all-in-one Reinforcement Learning (RL) controller and a third, hybrid system is proposed trading off the improved performance of the RL controller with the inherent stability guarantees of the benchmark system. Simulation results show all three controllers can successfully perform the mission. The RL controller docks significantly faster while rejecting higher lateral wind forces but struggles to generalise to unseen docking scenarios, while the hybrid system improves interpretability at the cost of performance. Furthermore, initial real-life testing of the benchmark system validates the simulation results.

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.