Autonomous Aerial Drone Landing Site Selection on a Maritime Vessel

This thesis focuses on the development of an autonomous system capable of iden- tifying, tracking, and landing on suitable sites aboard a moving ship. Leveraging modifications to the Hazard-Aware Landing Optimization (HALO) algorithm, origi- nally designed for static terrain, the system integrates robust mapping, point cloud registration, and site selection algorithms to enable reliable performance in dynamic maritime conditions. A simulation environment was developed, utilizing Microsoft AirSim and ShipMo3D. This simulation incorporated a quadrotor equipped with Light Detec- tion and Ranging (LiDAR) to map ship decks and evaluate potential landing sites. Key innovations included dynamic point cloud registration using FilterReg and the integration of a modified Landing Period Indicator (LPI) algorithm. The results demonstrated the system’s ability to autonomously map ship decks, identify suitable landing sites, and execute landings on a ship moving under diffi- cult sea conditions. This work establishes a foundation for further development in autonomous maritime operations.

Name thesis:degree_name

Master of Applied Science (M.App.Sc.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Engineering, Aerospace

Grantor dc:publisher

Carleton University

Year dc:date.issued

2025

Author dc:creator

• Giroux, Eric Daniel

Statement dc:rights

• Copyright © 2024 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, distribution to students, research and scholarship. Theses may only be shared by linking to the Carleton University Institutional Repository and no part may be copied without proper attribution to the author; no part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2025-16500

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/43643