Intelligent acoustic rotor speed estimation for an autonomous helicopter

“…Therefore, there has been an increased interest in system identification and states estimation of UAVs. For example, many studies were conducted on Quadrotor state-estimation [12][13][14][15][16]. Nobahari used the Continuous Ant-Colony filter (CACF) in order to estimate the vertical velocity of Quadrotor UAV during landing procedure [17].…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aerial Vehicles parameter estimation using Artificial Neural Networks and Iterative Bi-Section Shooting method

Hatamleh

Al-Shabi

Al-Ghasem

et al. 2015

Applied Soft Computing

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“…The sound signature of a UAV was studied in [17]. The Flyper autonomous helicopter was mounted on a platform, where a set of microphones recorded its acoustic signal.…”

Section: Introductionmentioning

confidence: 99%

UAV Thrust Model Identification Using Spectrogram Analysis

2021

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This paper deals with a non-contact method to identify the aerodynamic propeller constants of the Parrot AR.Drone quadrotor. The experimental setup consists of a microphone installed in the flight arena to record audio data. In terms of methodology, a spectrogram analysis is adopted to estimate the propeller velocity based on the filtered sound signal. It is known that, in a hovering maneuver, when the UAV mass increases, the propellers rotate faster to produce the necessary thrust increment. In this work, the rotorcraft takes off with its factory settings, first with no hull, corresponding to a mass of 413 g, and after with a small hull, corresponding to a mass of 444 g, and a bigger hull, corresponding to a mass of 462 g. In the sequence, the velocity of the propellers are estimated for each of these three cases using spectrograms of audio recorded by a microphone, corresponding to the sound generated by the four rotors. Finally, the estimated velocity is used to identify the aerodynamic parameters, thus validating the proposal.

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“…The identification accuracy are 83% and 61%, respectively. In [14], a coaxial UAV called Flyper helicopter, is mounted in a platform, where a set of microphones records its acoustic signal. Then, the rotor speeds are estimated without any prior knowledge about its acoustic properties.…”

Section: Spectrogram Approach: State Of the Artmentioning

confidence: 99%

Spectrogram Analysis: An Approach to Identify The Quadrotor Thrust Model

Brandão¹,

Pizetta²,

Pizziolo³

et al. 2018

Preprint

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Abstract:This paper deals with a non-contact method to identify the aerodynamic propeller parameters of the Parrot AR.Drone quadrotor. The experimental set consists in a camera recording the vehicle flights, the audio signal is extracted and is used a spectrogram analysis to estimates the propeller velocity. First, the aerial vehicle takes off and starts a hovering maneuver. The experiment is repeated with different additional masses attached to its rigid body. If the weight over the UAV increases/decreases, then the propeller must rotates faster/slower to produce a higher/lower thrust, and consequently, the sound frequency increases/decreases. Finally, this proposition is validated experimentally, and the estimated velocity is used to identify the quadrotor thrust parameters.

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Intelligent acoustic rotor speed estimation for an autonomous helicopter

Cited by 3 publications

References 18 publications

Unmanned Aerial Vehicles parameter estimation using Artificial Neural Networks and Iterative Bi-Section Shooting method

Unmanned Aerial Vehicles parameter estimation using Artificial Neural Networks and Iterative Bi-Section Shooting method

UAV Thrust Model Identification Using Spectrogram Analysis

Spectrogram Analysis: An Approach to Identify The Quadrotor Thrust Model

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