Trajectory Tracking with Collision Avoidance for Large Vessel Draft Surveys

Abstract

This thesis explores the innovative use of unmanned aerial vehicles to conduct draft surveys of large maritime vessels. This process is critical for determining vessel load through water displacement measurements. At the port of Narvik, a crew typically performs this task manually, where LKAB’s iron ore cargo vessels are surveyed while docked. The usual method requires going around the ship in a small boat to check draft markings, which can be difficult, especially in bad weather or at dark times. The close quarters and dangerous conditions often create safety risks and operational difficulties.

The primary aim of this research is to automate the draft survey pro- cess by using a self-flying quadrotor that is equipped with an autonomous guidance and control system. This system utilizes Nonlinear Model Predictive Control for precise position control, allowing for optimal trajectory tracking and effective collision avoidance, along with a reduced attitude controller. The goal is for the UAV to autonomously follow a predetermined path around the vessel, and systematically capture images or videos of the draft markings for analysis.

This research presents a theoretical model and simulation framework for implementing the developed system. The findings contribute to the field of UAV applications in the maritime sector by proposing an integration of control theory, UAV technology, and maritime operation needs. This study not only paves the way for further technological advancements in autonomous UAV systems but also enhances our understanding of their practical implementations in industry-specific scenarios.