An analysis of regression models for predicting the speed of a wave glider autonomous surface vehicle

Phillip Ngo, Wesam Al-Sabban, Jesse Thomas, Will Anderson, Jnaneshwar Das, Ryan N. Smith

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

An important aspect of robotic path planning for is ensuring that the vehicle is in the best location to collect the data necessary for the problem at hand. Given that features of in- terest are dynamic and move with oceanic cur- rents, vehicle speed is an important factor in any planning exercises to ensure vehicles are at the right place at the right time. Here, we examine different Gaussian process models to find a suitable predictive kinematic model that enable the speed of an underactuated, au- tonomous surface vehicle to be accurately pre- dicted given a set of input environmental pa-rameters.

Original languageEnglish (US)
Title of host publicationAustralasian Conference on Robotics and Automation, ACRA
PublisherAustralasian Robotics and Automation Association
ISBN (Electronic)9780980740448
StatePublished - Jan 1 2013
Externally publishedYes
Event2013 Australasian Conference on Robotics and Automation, ACRA 2013 - Sydney, Australia
Duration: Dec 2 2013Dec 4 2013

Other

Other2013 Australasian Conference on Robotics and Automation, ACRA 2013
CountryAustralia
CitySydney
Period12/2/1312/4/13

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

Cite this

Ngo, P., Al-Sabban, W., Thomas, J., Anderson, W., Das, J., & Smith, R. N. (2013). An analysis of regression models for predicting the speed of a wave glider autonomous surface vehicle. In Australasian Conference on Robotics and Automation, ACRA Australasian Robotics and Automation Association.