Predicting the speed of a Wave Glider autonomous surface vehicle from wave model data
Ngo, Phillip, Das, Jnaneshwar, Ogle, Jonathan, Thomas, Jesse, Anderson, Will, & Smith, Ryan (2014) Predicting the speed of a Wave Glider autonomous surface vehicle from wave model data. In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 14 - 18 September 2014, Chicago, Ill.
A key component of robotic path planning is ensuring that one can reliably navigate a vehicle to a desired location. In addition, when the features of interest are dynamic and move with oceanic currents, vehicle speed plays an important role in the planning exercise to ensure that vehicles are in the right place at the right time. Aquatic robot design is moving towards utilizing the environment for propulsion rather than traditional motors and propellers. These new vehicles are able to realize significantly increased endurance, however the mission planning problem, in turn, becomes more difficult as the vehicle velocity is not directly controllable. In this paper, we examine Gaussian process models applied to existing wave model data to predict the behavior, i.e., velocity, of a Wave Glider Autonomous Surface Vehicle. Using training data from an on-board sensor and forecasting with the WAVEWATCH III model, our probabilistic regression models created an effective method for forecasting WG velocity.
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|Item Type:||Conference Paper|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Deposited On:||20 Oct 2015 04:06|
|Last Modified:||20 Oct 2015 04:07|
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