An analysis of wind field estimation and exploitation for quadrotor flight in the urban canopy layer

Abstract

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.

Energetics in robotic flight at small scales

“…Adding the energy consumed to perform consecutive straight-line segments-that form a desired path-yields the total energy consumed for a specific path. With this information at hand, the derived wind-aware motion planner [64] is found superior to a naive planner unaware of aerodynamics, especially at wind speeds over 10 m s…”

“…Constraining the vehicle to operate at a fixed height and fixed ground speed v g , the energy consumed for moving from state q i to state q j can be then calculated through [64] …”

“…Moving a step forward, Ware & Roy [64] integrate an aerodynamics-based model for power consumption in rotorcraft forward flight [65,67] with empirical observations. The derived model is subsequently employed for finding minimum-energy trajectories for motion planning in a wind field.…”

“…The aforementioned works [63,64] highlight the significance of including aerodynamic effects in motion planning and control algorithms when considering energy-aware approaches. Providing compelling answers to this problem can significantly improve the performance of aerial vehicles by exploiting aerodynamics, such as the effect of ambient wind [64]. To this end, the question of how to model aerodynamic effects naturally arises.…”

Tightly Coupled Navigation and Wind Estimation for Mini UAVs

“…The knowledge of the instantaneous wind velocity constitutes a precious information for Unmanned Aircraft Vehicles (UAVs), e.g., for atmospheric energy harvesting, wind disturbance rejection, target tracking, [1][2][3][4][5] since wind significantly impacts flight performances, particularly for small and light vehicles. RIt may be useful to utilize fleets of UAVs to map the wind, e.g., for meteorological purposes, gas mapping, [6][7][8][9] with several advantages over manned flight as low cost or simplicity of deployment.…”