TY - GEN
T1 - Predicting the speed of a Wave Glider autonomous surface vehicle from wave model data
AU - Ngo, Phillip
AU - Das, Jnaneshwar
AU - Ogle, Jonathan
AU - Thomas, Jesse
AU - Anderson, Will
AU - Smith, Ryan N.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10/31
Y1 - 2014/10/31
N2 - A key component of robotic path planning for monitoring dynamic events is reliable navigation to the right place at the right time. For persistent monitoring applications (e.g., over months), marine robots are beginning to make use of the environment for propulsion, instead of depending on traditional motors and propellers. These vehicles are able to realize dramatically higher endurance by exploiting wave and wind energy, however the path planning problem becomes difficult as the vehicle speed is no longer directly controllable. In this paper, we examine Gaussian process models to predict the speed of the Wave Glider autonomous surface vehicle from observable environmental parameters. Using training data from an on-board sensor, and wave parameter forecasts from the WAVEWATCH III model, our probabilistic regression models create an effective method for predicting Wave Glider speed for use in a variety of path planning applications.
AB - A key component of robotic path planning for monitoring dynamic events is reliable navigation to the right place at the right time. For persistent monitoring applications (e.g., over months), marine robots are beginning to make use of the environment for propulsion, instead of depending on traditional motors and propellers. These vehicles are able to realize dramatically higher endurance by exploiting wave and wind energy, however the path planning problem becomes difficult as the vehicle speed is no longer directly controllable. In this paper, we examine Gaussian process models to predict the speed of the Wave Glider autonomous surface vehicle from observable environmental parameters. Using training data from an on-board sensor, and wave parameter forecasts from the WAVEWATCH III model, our probabilistic regression models create an effective method for predicting Wave Glider speed for use in a variety of path planning applications.
UR - http://www.scopus.com/inward/record.url?scp=84911476973&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84911476973&partnerID=8YFLogxK
U2 - 10.1109/IROS.2014.6942866
DO - 10.1109/IROS.2014.6942866
M3 - Conference contribution
AN - SCOPUS:84911476973
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 2250
EP - 2256
BT - IROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014
Y2 - 14 September 2014 through 18 September 2014
ER -