Adaptive Probabilistic Tack Manoeuvre Decision for Sailing Vessels

TL;DR

This paper presents an adaptive probabilistic decision-making method for sailing vessels to optimize upwind tacking maneuvers, demonstrated on a small vessel, improving success rates without detailed weather data.

Contribution

It introduces a novel adaptive probabilistic approach for selecting tacking strategies based on past success, applicable to small sailing vessels in varying conditions.

Findings

Successfully identified optimal tacking strategies without detailed weather data.

Demonstrated on a 1m sailing vessel, Black Python.

Proposed improvements for strategy selection and weather impact recognition.

Abstract

To move upwind, sailing vessels have to cross the wind by tacking. During this manoeuvre distance made good may be lost and especially smaller vessels may struggle to complete a tack in averse wind and wave conditions. A decision for the best tack manoeuvre needs to be made based on weather and available tack implementations. This paper develops an adaptive probabilistic tack manoeuvre decision method. The order of attempting different tacking strategies is based on previous success within a timeout, combined with an exploration component. This method is successfully demonstrated on the 1m long sailing vessel Black Python. Four strategies for crossing the wind were evaluated through adaptive probabilistic choices, and the best was identified without detailed sensory knowledge of the actual weather conditions. Based on the positive results, further improvements for a better selection process are suggested and the potential of using the collected data to recognise the impact of weather conditions on tacking efforts is recognised.