An analysis of wind field estimation and exploitation for quadrotor flight in the urban canopy layer
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Roy_An analysis.pdf
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1.81 MB
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Checksum (MD5)
537b36463280b067f18dcb7df045614b
Author(s)
Ware, John W.
Roy, Nicholas
Date Issued
May 2016
Journal
IEEE International Conference on Robotics and Automation, 2016
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Ware, John, and Nicholas Roy. “An Analysis of Wind Field Estimation and Exploitation for Quadrotor Flight in the Urban Canopy Layer.” IEEE, 2016. 1507–1514.
Version
Author's final manuscript
Abstract
Although unmanned air vehicles' increasing agility and autonomy may soon allow for flight in urban environments, the impact of complex urban wind fields on vehicle flight performance remains unclear. Unlike synoptic winds at high altitudes, urban wind fields are subject to turbulence generated by the buildings and terrain. The resulting spatial and temporal variation makes inference about the global wind field based on local wind measurements difficult and prevents the use of most simple wind models. Fortunately, the structure of the urban environment provides exploitable predictability given a suitable computational fluid dynamics solver, a representative 3D model of the environment, and an estimate of the expected prevailing wind speed and heading. The prevailing wind speed and direction at altitude and computational fluid dynamics solver can generate the corresponding wind field estimate over the map. By generating wind fields in this way, this work investigates a quadrotor's ability to exploit them for improved flight performance. Along with the wind field estimate, an empirically derived power consumption model is used to find minimum-energy trajectories with a planner both aware of and naive to the wind field. When compared to minimum-energy trajectories that do not incorporate wind conditions, the wind-aware trajectories demonstrate reduced flight times, total energy expenditures, and failures due to excess air speed for trajectories across MIT campus.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA.2016.7487287
