Modeling of Urban Wind Field Effects on Unmanned Rotorcraft Flight

Galway, David; Etele, Jason; Fusina, Giovanni

doi:10.2514/1.c031325

Cited by 33 publications

(14 citation statements)

References 24 publications

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“…Orr et al [9], [10] used a computational fluid dynamics (CFD) solver to generate static wind fields and assess the waypoint following performance of a fixed-wing UAV. Another effort was made by Galway [11], [12] to simulate an autonomous helicopter in a sparse urban environment. Galway used a CFD model to compute wind fields around cross sections of individual and small groups of buildings for a spanning set of prevailing wind speeds and headings.…”

Section: Related Workmentioning

confidence: 99%

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

Ware

Roy

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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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.

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Section: Related Workmentioning

confidence: 99%

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

Ware

Roy

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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“…The results emphasize the effects of wind on a small size UAV, and the need for a robust flight control technique which can deal with adverse wind conditions. In another study, Galway et al, 20 documented the urban wind gust effects on the flight performance of a Yamaha RC helicopter. A simulation tool was used where a selection algorithm determined if the current vehicle position was influenced by the building wake.…”

Section: Introductionmentioning

confidence: 98%

Simulation Tool for Testing and Validating UAV Autopilots in Wind Gust Environments

Raza

Etele

2012

AIAA Atmospheric Flight Mechanics Conference

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Small unmanned aerial vehicles operating in urban settings are prone to wind gust conditions. The development of effective flight control strategies are required to successfully deal with wind gust effects. A simulation environment was previously established to allow for testing potential techniques under realistic wind gust conditions. In order to experimentally validate the predictions of this simulation tool a quadrotor UAV testbed has been developed. Its validation of the simulation tool is presented in this paper. An average of absolute difference is evaluated to quantify the accuracy and predictive capability of the simulation tool. Overall, the experimental flights remained within the flight envelope predicted by the simulation tool.

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“…Nowadays, small rotary-wing VTOL UAVs are becoming widely used in many fields such as urban photography [1], fire detection and control [2], and agriculture operations [3]. One limitation of small UAVs is that they are sensitive to wind disturbance, leading to degraded flight accuracy and safety.…”

Section: Introductionmentioning

confidence: 99%

A K Nearest Neighborhood-Based Wind Estimation for Rotary-Wing VTOL UAVs

Wang

Misra

Bai

2019

Drones

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Wind speed estimation for rotary-wing vertical take-off and landing (VTOL) UAVs is challenging due to the low accuracy of airspeed sensors, which can be severely affected by the rotor’s down-wash effect. Unlike traditional aerodynamic modeling solutions, in this paper, we present a K Nearest Neighborhood learning-based method which does not require the details of the aerodynamic information. The proposed method includes two stages: an off-line training stage and an on-line wind estimation stage. Only flight data is used for the on-line estimation stage, without direct airspeed measurements. We use Parrot AR.Drone as the testing quadrotor, and a commercial fan is used to generate wind disturbance. Experimental results demonstrate the accuracy and robustness of the developed wind estimation algorithms under hovering conditions.

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Modeling of Urban Wind Field Effects on Unmanned Rotorcraft Flight

Cited by 33 publications

References 24 publications

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

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

Simulation Tool for Testing and Validating UAV Autopilots in Wind Gust Environments

A K Nearest Neighborhood-Based Wind Estimation for Rotary-Wing VTOL UAVs

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