A real-time strategy-decision program for sailing yacht races. (1st April 2017)

• Record Type: Journal Article
• Authors: Tagliaferri, F.; Viola, I.M.
• Is Part Of: Ocean engineering. Volume 134(2017)
• Page Start: 129
• Page End: 139
• Publication Date: 2017-04-01
• Subjects: Yacht race -- Tactics -- Routing -- Dynamic programming -- Artificial neural network -- Coherent risk measure; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2017.02.026
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: Optimal decisions for a skipper competing in a match race depend on a number of factors, including wind speed and direction variations, behaviour of the opponent, sea state, currents, racing rules. Expert sailors are able to combine observations on these various factors and process them to take optimal decisions. This study presents an attempt at emulating this decision process through a computer code that can be used in real time to advise on race strategy. The novelty of the proposed method consists in combining various approaches for the multiple factors affecting the decision process. The wind variability is modelled with the use of neural networks, to produce a short-term wind forecast. The willingness of the sailor to risk is modelled using coherent-risk measures. Experimental results are used to quantify the loss of speed due to the presence of a nearby opponent. Finally, all these factors are combined through dynamic programming to compute an optimal course, based also on information on the current and yachts performance. The program is tested modelling the last 13 races of the 34th America's Cup, and results show that the route computed is close to the shortest possible route computed assuming perfect knowledge of sea conditions. Abstract : Highlights: An application of Dynamic Programming (DP) to yacht races. The DP algorithm is supported by a time-dependent lattice. Artificial intelligence is used for wind forecasting. A novel optimised risk management model. Tested with simulations of America's Cup races.