A key component of robotic path planning is#R##N#ensuring that one can reliably navigate a vehicle to a desired#R##N#location. In addition, when the features of interest are dynamic#R##N#and move with oceanic currents, vehicle speed plays an important#R##N#role in the planning exercise to ensure that vehicles#R##N#are in the right place at the right time. Aquatic robot design is#R##N#moving towards utilizing the environment for propulsion rather#R##N#than traditional motors and propellers. These new vehicles are#R##N#able to realize significantly increased endurance, however the#R##N#mission planning problem, in turn, becomes more difficult as#R##N#the vehicle velocity is not directly controllable. In this paper,#R##N#we examine Gaussian process models applied to existing wave#R##N#model data to predict the behavior, i.e., velocity, of a Wave#R##N#Glider Autonomous Surface Vehicle. Using training data from#R##N#an on-board sensor and forecasting with the WAVEWATCH III#R##N#model, our probabilistic regression models created an effective#R##N#method for forecasting WG velocity.
['Phillip Ngo', 'Jnaneshwar Das', 'Jonathan Ogle', 'Jesse Thomas', 'Will Anderson', 'Ryan N. Smith']
Predicting the speed of a Wave Glider autonomous surface vehicle from wave model data