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Applying classical control methods to Micro Aerial Vehicles (MAVs) is a difficult process due to the complexity of the control laws with fast and highly non-linear dynamics. Such methods rely heavily on difficult to obtain models and are particularly ill-suited to the stochastic and dynamic environments in which MAVs operate. Instead, in this paper, we focus on a neuro-evolutionary method that learns to map MAV states (position, velocity) to MAV actions (e.g., actuator position). Our results show significant improvements in response times to minor altitude and heading corrections over a traditional PID controller. In addition, we show that the MAV response to maintaining altitude in the presence of wind gusts improves by a factor of five. Similarly, we show that the MAV response to maintaining heading in the presence of turbulence improves by factors of three.
@inproceedings{tumer-salichon_gecco10,
author = {M. Salichon and K. Tumer},
title = {A Neuro-Evolutionary Approach to Micro Aerial Vehicle Control},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
month = {July},
address = {Portland, OR},
abstract={Applying classical control methods to Micro Aerial Vehicles (MAVs) is a difficult process due to the complexity of the control laws with fast and highly non-linear dynamics. Such methods rely heavily on difficult to obtain models and are particularly ill-suited to the stochastic and dynamic environments in which MAVs operate. Instead, in this paper, we focus on a neuro-evolutionary method that learns to map MAV states (position, velocity) to MAV actions (e.g., actuator position). Our results show significant improvements in response times to minor altitude and heading corrections over a traditional PID controller. In addition, we show that the MAV response to maintaining altitude in the presence of wind gusts improves by a factor of five. Similarly, we show that the MAV response to maintaining heading in the presence of turbulence improves by factors of three.},
bib2html_pubtype = {Award Winners, Refereed Conference Papers},
bib2html_rescat = {Evolutionary Algorithms},
note = {{\bf <em>Nominated for best "Real World Applications" paper award.</em>}},
year = {2010}
}
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