Autonomous Docking Using LiDAR-Based Tracking and Adaptive Pose Selection: Closed-Loop Sea Trials

Abstract

This paper presents a fully integrated autonomous docking system validated through closed-loop sea trials on the milliAmpere1 research ferry operating in a live maritime harbour with moving vessels. Real harbour environments require continuous situational awareness and adaptive decision-making under dynamic traffic conditions. The proposed architecture combines cartographic land masking, LiDAR-based clustering, probabilistic multi-target tracking (JIPDA), dynamic footprint estimation, adaptive docking pose selection, and real-time path replanning within a finite state machine framework. Rather than introducing new algorithms, the contribution lies in system-level integration and operational validation of a complete perception-to-control pipeline under realistic maritime constraints. The system is demonstrated in multiple closed-loop experiments including collision avoidance and adaptive docking with moving obstacles. Results highlight both performance characteristics and practical deployment considerations, including runtime behaviour, sensor limitations, and integration trade-offs. The work provides empirical evidence that robust autonomous docking in dynamic harbour environments can be achieved through carefully engineered integration of established methods.