Probability of Trajectory Deviation of Unmanned Aerial Vehicle in Presence of Wind

Author:

Banerjee Portia1,Corbetta Matteo1,Jarvis Katelyn2,Smalling Kyle3,Turner Andrew4

Affiliation:

1. KBR, NASA Ames Research Center, Moffett Field, California 94035

2. NASA Ames Research Center, Moffett Field, California 94035

3. NASA Langley Research Center, Hampton, Virginia 23681

4. National Institute of Aerospace, NASA Langley Research Center, Hampton, Virginia 23681

Abstract

Incorporating unmanned aerial vehicles (UAVs) into the United States National Airspace System would demand enhanced airspace safety technologies for the safety of the UAVs, people, and property on the ground. One of the safety-critical factors to consider is the risk of a UAV deviating from its planned trajectory, which may result in loss of separation between other vehicles or obstacles or may cause early depletion of battery power. In this paper, we studied the effect of wind on UAV trajectory deviation by incorporating wind velocity as a drag component in a six-degrees-of-freedom trajectory simulation comprising a rotorcraft lumped-mass model. Both steady-state wind and wind turbulence effects were investigated. We validated our approach using real flight data from UAV experiments conducted at NASA Langley Research Center. The proposed approach would enable risk-informed decision making by timely mitigation of current and future collision events in an uncertain and dynamic environment.

Funder

Aeronautics Research Mission Directorate

Publisher

American Institute of Aeronautics and Astronautics (AIAA)

Subject

Management of Technology and Innovation,Management, Monitoring, Policy and Law,Energy (miscellaneous),Safety Research,Transportation,Aerospace Engineering

Reference20 articles.

1. YoungS.AncelE.MooreA.DillE.QuachC.FosterJ.DarafshehK.SmallingK.VazquezS.EvansE.OkoloW. “Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations,” NASA TM-2020-220440, 2020.

2. Uncertainty Quantification of Expected Time-of-Arrival in UAV Flight Trajectory

3. End-of-discharge and End-of-life Prediction in Lithium-ion Batteries with Electrochemistry-based Aging Models

4. In-Flight Detection of Vibration Anomalies in Unmanned Aerial Vehicles

5. In-Time Non-Participant Casualty Risk Assessment to Support Onboard Decision Making for Autonomous Unmanned Aircraft

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