Path Planning for Autonomous Sailing Vessels: Developing Robust and Efficient Survey Strategies

Autonomous sailing vessels offer a promising solution for maritime research, offering low maintenance and sustainable platforms for environmental monitoring and data collection. These vessels utilize wind power, eliminating the need for conventional fuel and enabling long-duration operations with minimal environmental impact. Their applications range from oceanographic studies to maritime surveillance, where persistent and autonomous data collection is essential. This thesis explores the challenges and methodologies associated with path planning for autonomous sailing, particularly in the context of survey operations. Unlike traditional motorized vessels, sailing autonomy must account for wind variability, sail dynamics, and limited maneuverability, requiring specialized path-planning techniques to ensure efficient and reliable navigation. The research investigates various sail and hull configurations, the dynamics of windpowered propulsion, and the application of autonomy frameworks such as MOOS-IvP. A key focus is on optimizing continuous coverage path planning (CPP) to maximize efficiency while adapting to environmental constraints. By integrating real-time wind data and vessel performance characteristics, the study refines survey strategies that enhance mission effectiveness. Different survey strategies are implemented and evaluated using both simulation and real-world testing on the Charles River. These trials demonstrate the feasibility of fixed-path decomposition approaches and adaptive moving horizon control methods, evaluating methods with the impact of wind conditions on autonomous sailing performance. The results contribute to the development of robust and efficient survey strategies that improve the autonomy and reliability of wind-powered marine vessels.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Ahlers, Matthew C.

Advisor dc:contributor.advisor

• Benjamin, Michael R.

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/163006

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/163006