An Online Feedback Motion Planning and Control Strategy for UAVs in Wind. Doshi, A., Singh, S. P. N., & Postula, A. In Australian Conference on Robotics and Automation, 2012. Paper abstract bibtex This paper describes a model reduction strategy for obtaining a computationally efficient prediction of a fixed-wing UAV performing waypoint navigation under steady wind conditions. The strategy relies on the off-line generation of time parametrized trajectory libraries for a set of flight conditions and reduced order basis functions functions for determining intermediate locations. It is assumed that the UAV has independent bounded control over the airspeed and altitude, and consider a 2D slice of the operating environment. We found that the reduced-order model finds intermediate positions within 10% and at speeds of 10x faster than clock-time (even in wind conditions in excess of 50% of the UAV's forward airspeed) when compared against simulation results using a medium-fidelity flight dynamics model. The potential of this strategy for online planning operations is highlighted.
@INPROCEEDINGS{acra.windgustuav.2012,
author = {A. Doshi and S. P. N. Singh and A. Postula},
title = {An Online Feedback Motion Planning and Control Strategy for UAVs
in Wind},
booktitle = {Australian Conference on Robotics and Automation},
year = {2012},
abstract = {This paper describes a model reduction strategy for obtaining a computationally
efficient prediction of a fixed-wing UAV performing waypoint navigation
under steady wind conditions. The strategy relies on the off-line
generation of time parametrized trajectory libraries for a set of
flight conditions and reduced order basis functions functions for
determining intermediate locations. It is assumed that the UAV has
independent bounded control over the airspeed and altitude, and consider
a 2D slice of the operating environment. We found that the reduced-order
model finds intermediate positions within 10% and at speeds of 10x
faster than clock-time (even in wind conditions in excess of 50%
of the UAV's forward airspeed) when compared against simulation results
using a medium-fidelity flight dynamics model. The potential of this
strategy for online planning operations is highlighted.},
pdf = {doshiAcra12.pdf},
url = {http://www.araa.asn.au/acra/acra2012/papers/pap147.pdf}
}
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It is assumed that the UAV has independent bounded control over the airspeed and altitude, and consider a 2D slice of the operating environment. We found that the reduced-order model finds intermediate positions within 10% and at speeds of 10x faster than clock-time (even in wind conditions in excess of 50% of the UAV's forward airspeed) when compared against simulation results using a medium-fidelity flight dynamics model. The potential of this strategy for online planning operations is highlighted.","pdf":"doshiAcra12.pdf","url":"http://www.araa.asn.au/acra/acra2012/papers/pap147.pdf","bibtex":"@INPROCEEDINGS{acra.windgustuav.2012,\r\n author = {A. Doshi and S. P. N. Singh and A. Postula},\r\n title = {An Online Feedback Motion Planning and Control Strategy for UAVs\r\n\tin Wind},\r\n booktitle = {Australian Conference on Robotics and Automation},\r\n year = {2012},\r\n abstract = {This paper describes a model reduction strategy for obtaining a computationally\r\n\tefficient prediction of a fixed-wing UAV performing waypoint navigation\r\n\tunder steady wind conditions. The strategy relies on the off-line\r\n\tgeneration of time parametrized trajectory libraries for a set of\r\n\tflight conditions and reduced order basis functions functions for\r\n\tdetermining intermediate locations. It is assumed that the UAV has\r\n\tindependent bounded control over the airspeed and altitude, and consider\r\n\ta 2D slice of the operating environment. 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