Improving MAV Control by Predicting Aerodynamic Effects of Obstacles

John Bartholomew, Andrew Calway, Walterio Mayol-Cuevas, Improving MAV Control by Predicting Aerodynamic Effects of Obstacles. IEEE/RSJ International Conference on Intelligent Robots and Systems. September 2015. PDF, 1700 Kbytes. External information


Building on our previous work, in this paper we demonstrate how it is possible to improve flight control of a MAV that experiences aerodynamic disturbances caused by objects on its path. Predictions based on low resolution depth images taken at a distance are incorporated into the flight control loop on the throttle channel as this is adjusted to target undisrupted level flight. We demonstrate that a statistically significant improvement (p << 0:001) is possible for some common obstacles such as boxes and steps, compared to using conventional feedback-only control. Our approach and results are encouraging toward more autonomous MAV exploration strategies.

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