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S. Sun
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1
High-Speed Free-Flight Wind Tunnel Experiment With A Compromised Quadrotor
Creating A Dataset Suitable For Identification Of Aerodynamic Forces and Moments
To aid in the continued effort of making unmanned flight safer, this paper presents the experimenting effort towards determining aerodynamic forces on a off-the-shelf quad-rotor under compromised circumstances. For the first time an Incremented Nonlinear Dynamic Inversion controller is used for compromised flight in wind tunnel conditions. For the Parrot Bebop v1, five aerodynamically different configurations were tested. These configurations include reduced rotor effectiveness on one or two rotors and the inclusion of one or two bumpers. In order to gather a suitable dataset for these highly dynamic models, free-flight model identification is deemed necessary. To facilitate free-flight aerodynamic model identification, data was provided by the on-board inertial sensors and a motion-tracking OptiTrack system. Sensor fusion through Extended Kalman Filtering was chosen to counter frame vibrations and sensor bias. A sufficient convergence rate was found for estimating the biases. Further modeling efforts are required to validate these results. A rotor rate Kalman Filter was also designed to improve rotor rate differentiation. Excitation of the system was performed with position-controlled doublets, designed to observe theoretical modes from existing quadrotor models. This resulted in five main types of excitation, designed to maximize yawing motions, thrust variation and drag effects. The differences seen for each configuration show interesting behavior, and further investigation is recommended. The resulting dataset is considered uniquely suitable for estimating coupled dynamics with saturated actuators and loss of yaw control.
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To aid in the continued effort of making unmanned flight safer, this paper presents the experimenting effort towards determining aerodynamic forces on a off-the-shelf quad-rotor under compromised circumstances. For the first time an Incremented Nonlinear Dynamic Inversion controller is used for compromised flight in wind tunnel conditions. For the Parrot Bebop v1, five aerodynamically different configurations were tested. These configurations include reduced rotor effectiveness on one or two rotors and the inclusion of one or two bumpers. In order to gather a suitable dataset for these highly dynamic models, free-flight model identification is deemed necessary. To facilitate free-flight aerodynamic model identification, data was provided by the on-board inertial sensors and a motion-tracking OptiTrack system. Sensor fusion through Extended Kalman Filtering was chosen to counter frame vibrations and sensor bias. A sufficient convergence rate was found for estimating the biases. Further modeling efforts are required to validate these results. A rotor rate Kalman Filter was also designed to improve rotor rate differentiation. Excitation of the system was performed with position-controlled doublets, designed to observe theoretical modes from existing quadrotor models. This resulted in five main types of excitation, designed to maximize yawing motions, thrust variation and drag effects. The differences seen for each configuration show interesting behavior, and further investigation is recommended. The resulting dataset is considered uniquely suitable for estimating coupled dynamics with saturated actuators and loss of yaw control.
With most of the current research in quadrotor Loss-Of-Control (LOC) being focussed on specific failure cases e.g. sensor faults and Single-Rotor Failure (SRF), the growth that is expected in the drone industry will not be able to be sustained, in regards to the safety of individuals in urban areas. Without an assurance of reliability regarding the safety of drones this is just not feasible. With the National Aeronautics and Space Administration (NASA) outlining flight traffic rules for drones it seems to be just a matter of time until it will be normal to see such vehicles flying around. Therefore it is of the utmost importance to improve the overall resilience of quadrotors. This work seeks to show the importance of modelling hazards such as the Vortex Ring State (VRS) and blade flapping to broaden the approach on solving LOC of quadrotors. Through the adaptation of the definition of LOC of aircraft to quadrotors and a comparative analysis of quadrotor flights, of both the nominal and SRF configuration, a Quantitative LOC Definition (QLD) for quadrotors is created. This definition is then validated through the analysis of thrust stand measurements and quadrotor flights. Resulting in a measure for the identification of LOC events.
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With most of the current research in quadrotor Loss-Of-Control (LOC) being focussed on specific failure cases e.g. sensor faults and Single-Rotor Failure (SRF), the growth that is expected in the drone industry will not be able to be sustained, in regards to the safety of individuals in urban areas. Without an assurance of reliability regarding the safety of drones this is just not feasible. With the National Aeronautics and Space Administration (NASA) outlining flight traffic rules for drones it seems to be just a matter of time until it will be normal to see such vehicles flying around. Therefore it is of the utmost importance to improve the overall resilience of quadrotors. This work seeks to show the importance of modelling hazards such as the Vortex Ring State (VRS) and blade flapping to broaden the approach on solving LOC of quadrotors. Through the adaptation of the definition of LOC of aircraft to quadrotors and a comparative analysis of quadrotor flights, of both the nominal and SRF configuration, a Quantitative LOC Definition (QLD) for quadrotors is created. This definition is then validated through the analysis of thrust stand measurements and quadrotor flights. Resulting in a measure for the identification of LOC events.