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Measuring rotor speed for wind vector estimation on multirotor aircraft
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ScienceDirect Materials Today: Proceedings 5 (2018) 26703–26708
www.materialstoday.com/proceedings
DAS_2017
Measuring rotor speed for wind vector estimation on multirotor aircraft Dino Hüllmanna,*, Niels Paula, Harald Kohlhoffa, Patrick P. Neumanna, and Achim J. Lilienthalb a
Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 87, 12205 Berlin, Germany Örebro University, School of Science and Technology, AASS Research Centre, 70182 Örebro, Sweden
b
Abstract For several applications involving multirotor aircraft, it is crucial to know both the direction and speed of the ambient wind. In this paper, an approach to wind vector estimation based on an equilibrium of the principal forces acting on the aircraft is shown. As the thrust force generated by the rotors depends on their rotational speed, a sensor to measure this quantity is required. Two concepts for such a sensor are presented: One is based on tapping the signal carrying the speed setpoint for the motor controllers, the other one uses phototransistors placed underneath the rotor blades. While some complications were encountered with the first approach, the second yields accurate measurement data. This is shown by an experiment comparing the proposed speed sensor to a commercial non-contact tachometer. © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of the Committee Members of 34th DANUBIA ADRIA SYMPOSIUM on Advances in Experimental Mechanics (DAS 2017). Keywords: wind vector estimation; rotor speed; UAV; tachometer
1. Introduction Ambient wind plays an important role in aviation since it can affect the flight characteristics significantly. Especially small unmanned aerial vehicles (UAV) are very sensitive to wind due to their low inertia, so that high
* Corresponding author. Tel.: +49-30-8104-4790; fax: +49-30-8104-1917. E-mail address: [email protected] 2214-7853 © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of the Committee Members of 34th DANUBIA ADRIA SYMPOSIUM on Advances in Experimental Mechanics (DAS 2017).
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Hüllmann et al. / Materials Today: Proceedings 5 (2018) 26703–26708
wind speed and strong wind gusts may limit their use [1]. Hence, for applications like search and rescue operations, infrastructure inspection and environmental monitoring it can be crucial to know the local wind conditions. Furthermore, wind can have a direct influence in some scenarios, for example if the propagation of gas plumes is examined [2]. In such scenarios, it is often important to have an appropriate estimate of both the wind direction and wind speed in real-time. Usually, meteorological services provide averaged wind estimates for heights greater than 10 m above ground level, but the wind conditions can vary considerably in a local area over time and at lower heights [3], at which small UAV are often operated. Therefore, it can be necessary to measure the wind in-situ, for example by using mobile wind sensors (anemometers). However, such sensors can only provide measurements for their immediate surroundings while one might be interested in the wind distribution over the whole area of interest. This can be obtained, for instance, by performing the measurements on board the UAV. In case of fixed-wing aircraft, typically Pitot tubes are used as sensors for wind speed measurements. Pitot tubes measure the difference between the total and static pressure caused by an air stream, which can be used to calculate the velocity of the air [4]. Special sensor designs have been developed, for instance the five-hole probe shown in [5], that allow to measure the wind direction as well. However, this method cannot be easily adopted to rotary-wing aircraft like small multicopters, because the rotor wash causes strong perturbations of the aerodynamic field surrounding the aircraft. To overcome this influence, the sensor can be placed far off the centre of the airframe, as done by Bruschi et al. [6]. However, such a design has several disadvantages: First, the total weight is increased lowering both the flight time and the ability to carry other payload. Secondly, the flight characteristics become more unstable due to the displaced centre of mass. Therefore, other, indirect approaches to measure the wind vector have been developed. In particular methods based on observing the state variables of aircraft enjoy great popularity. For the first time, this was done by Divitiis [7], who measures the state variables of an aircraft with two coaxial rotors and computes the wind vector using a variational technique and an artificial neural network. Neumann and Bartholmai [8][9] presented an easier approach by inferring the wind vector from simple pose measurements of the aircraft. However, their papers only address the estimation of the horizontal wind components. In this paper, we show how that approach can be extended to the 3D case. This extension, however, requires measurements of the rotational speed of the rotors. Therefore, we present two approaches to this and show results from an experiment comparing the sensor we propose with a commercial non-contact tachometer. 2. Theory: Wind Vector Estimation Fig. 1a shows the wind triangle [8] which gives a relation between the ground speed of an apircraft, , its airspeed, , and the wind speed, . The term airspeed refers to the speed of an aircraft relative to the air. This yields the following equation: =
+
(1)
a)
b) Thrust
Airspeed
Drag
Gravitation
Fig. 1. (a) Wind triangle; (b) Principal forces acting on the multicopter.
Hüllmann et al. / Materials Today: Proceedings 5 (2018) 26703–26708
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The multicopter is considered to be a rigid body, thus its translational dynamics can be described according to the Newton-Euler equations: ⋅
=
(2)
is the acceleration of the aircraft expressed in its body frame and are where denotes the mass, C the external forces acting on the airframe expressed in . We assume that, principally, the gravitational, thrust and drag (or friction) force contribute to . Let , be the thrust force generated by rotor , the total thrust becomes [10] =∑ where rotors and
,
∑(
=
)
(3)
is an aircraft specific thrust constant, is the air density, the rotor swept area, the radius of the the angular velocity of rotor . In the body coordinate frame the thrust force is defined as = 0 0
.
(4)
With ⨀ denoting the Hadamard (or Schur) product, the drag force [9] can be written as =
(
⨀
⨀
⨀
)
(5)
is the vector of drag coefficients and is the vector of projected surface areas of the airframe. The where gravitational force is given in the fixed coordinate system by = 0 0 −
⋅
.
(6)
It can be transformed into the body coordinate system by the rotation matrix =
⋅
: (7)
Therewith, the equation of motion (2) becomes ⋅
=
+
+
.
(8)
Starting from (8) a solution for can be derived using (5). By inserting the result into (1) the actual wind vector, , can be computed. A typical small UAV is equipped with sensors like an inertial measurement unit (IMU), global positioning system (GPS) and a compass. This instrumentation allows to measure or estimate , and . Values for , , C , , , , and are given or can be determined experimentally. In contrast, the rotor speeds have to be measured online. 3. Measuring the Rotor Speed Usually, radio controlled multirotor aircraft are driven by electric brushless motors that are powered by electronic speed control (ESC) circuits. Typically, the flight controller computes a speed setpoint for each rotor and transmits this value to the respective ESC as a pulse-width modulated (PWM) signal. There are basically two approaches to measure the rotational speed of a rotor: One is to pick the signal controlling the ESC, either in software or electrically, the second one is to measure the rotor speed directly at the motor or rotor, respectively.
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Hüllmann et al. / Materials Today: Proceedings 5 (2018) 26703–26708
3.1. First Approach: Exploiting the PWM Signal In [11] the first approach was taken and a relation between the PWM signal controlling the ESC and the speed of a motor with unmounted rotor blades was found. However, the experiment took place under laboratory conditions and therefore does not take all relevant effects into account. For instance, a laboratory power supply was used during the experiment that supplies the ESC with a constant voltage, but the voltage provided by the battery of the aircraft varies greatly during a real flight. On one hand, the voltage depends on the instantaneous power demand of the aircraft which in turn depends on the current flight conditions. On the other hand, the voltage drops continuously as it correlates with the charging level of the battery. A non-linear relation between the voltage, the PWM signal and the rotational speed of the rotors was observed during real flight experiments. Moreover, it seemed that aerodynamic effects influence the rotor speed as well, in a way that is not reflected in the PWM signal. 3.2. Second Approach: Optical Measurement For the reasons mentioned before we refrained from the first approach and took the second one. There are several methods to measure rotational speed: Typical approaches are to install a hall sensor or to tap the counterelectromotive force, also known as back EMF, from the brushless motors. Another common way is to use noncontact tachometers which measure the reflection of a laser beam. Usually, this requires a small reflector to be stuck on the rotating component. Since the electrical system of a multicopter is its most crucial part, the goal is to affect it as little as possible, which can be achieved best by optical sensing. Therefore, the measurement principle of a non-contact tachometer is implemented in a slightly modified way: The rotor blades themselves and the ambient light replace the reflector and the laser source. Hence, only a photodiode is required to measure the rotor speed. Admittedly, this approach is unsuitable for flying at night, but it can be easily adopted to other rotary-wing aircraft.
Fig. 2. The red housing contains a phototransistor.
Fig. 2 shows the implemented solution with a phototransistor placed next to the motor, so that it can measure the bright-dark-boundaries while the rotor is spinning. The phototransistor is connected to an analog-to-digital converter (ADC) that delivers the quantised value to a microcontroller for further processing. Basically, the proposed sensor generates a pulse wave signal with its period depending on the instantaneous rotor speed. The latter is computed using a mixed method of frequency and period measurements as shown in [12]. 4. Experiment Setup and Result To validate the proposed phototransistor-based sensor system, its output is compared to a commercial non-contact tachometer using the setup shown in Fig. 3. As can be seen in the figure, the rotor arm is fixed to the ground to avoid
Hüllmann et al. / Materials Today: Proceedings 5 (2018) 26703–26708
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a lift-off. In addition, a usual desk light is used to illuminate the phototransistor to get a clear signal, since the spectrum of the ceiling light did not match the sensitivity range of the phototransistor, which is optimised for daylight conditions.
Fig. 3. Setup to compare the proposed sensor and a non-contact tachometer (UNI-T UT372).
A “thrust trajectory” was generated manually by varying the throttle stick of the aircraft’s remote control. Fig. 4 shows the result of the comparison of the proposed sensor and the non-contact tachometer. There are three things to note: First, the tachometer measurements are inaccurate if the rotor spins faster than 6000 rpm. This is caused by the thrust force generated at this rotational speed, which lifts the rotor arm and thereby moves the reflector out of the laser beam. Secondly, the tachometer signal contains more noise and outliers, which can be seen for example at 10 s and around 30 s in the measurement data shown in Fig. 4. Thirdly, there is a small lag in the signal from the tachometer although the fastest sampling rate available was chosen. This can be especially seen at the end of the measurement series, where it takes some time until the device recognises that the rotor stopped spinning.
Fig. 4. Rotor speed measured by the proposed sensor and a commercial non-contact tachometer.
In fact, the proposed sensor is suitable to generate accurate ground truth measurements with a lag of 40 ms caused by the smoothing filter, cf. [12], and thus is sufficient to meet the accuracy required for the wind vector estimation
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Hüllmann et al. / Materials Today: Proceedings 5 (2018) 26703–26708
algorithm. In addition, the measurement principle is highly resistant to disturbances since it is based on binary data, i.e. the bright-dark-boundaries of the spinning rotors. 5. Conclusion and Outlook We derived an approach to estimate the wind vector based on an equilibrium of the principal forces acting on multirotor aircraft. Several parameters are required to perform this computation, however, most of them are already measured by off-the-shelf multicopters or they are constants that need to be identified once. Beyond that, the rotor speed has to be measured in real-time. Unlike for the other parameters, this requires additional sensors. In this paper, we showed a solution to this problem based on a phototransistor detecting the rotational speed of the rotors. A comparison with a commercial laser-based non-contact tachometer showed that the proposed sensor generates accurate, low-noise and low-latency measurements, which are sufficient for the intended wind vector estimation algorithm. Moreover, the proposed solution is very cost-efficient and can be applied to any rotary wing aircraft. Next, the remaining parameters characterising the aircraft have to be identified. We are going to do this by letting the multicopter hover next to 3D ultrasonic anemometers under varying wind conditions. Afterwards the performance of the algorithm can be benchmarked in real flight scenarios. References [1] W. L. Chan, C. S. Lee, F. B. Hsiao, Meas. Sci. Technol. 22.10 (2011). [2] R. A. Russell, D. Thiel, R. Deveza, A. Mackay-Sim, IEEE Int. Conf. Robot. 1 (1995) 556-561. [3] J.-F. Geleyn, Tellus A 40A.4 (1988) 347-351. [4] R. Klopfenstein Jr, ISA T. 37.4 (1998) 257-263. [5] A. Van den Kroonenberg, T. Martin, M. Buschmann, J. Bange, P. Vörsmann, J. Atmos. Ocean. Tech. 25.11 (2008) 1969–1982. [6] P. Bruschi, M. Piotto, F. Dell’Agnello, J. Ware, N. Roy, Procedia Engineer. 168 (2016) 802-805. [7] N. de Divitiis, J. Aircraft 40.4 (2003) 759-767. [8] P. P. Neumann, S. Asadi, A. J. Lilienthal, M. Bartholmai, J. H. Schiller, IEEE Robotics & Automation Magazine 19.1 (2012) 50-61. [9] P. P. Neumann, M. Bartholmai, Sensor. Actuat. A-Phys. 235 (2015) 300-310. [10] X. Xiang, Z. Wang, Z. Mo, G. Chen, K. Pham, E. Blasch, Digit. Avion. Syst. Con. (2016). [11] D. Hüllmann, N. Paul, P. P. Neumann, 34th Danubia-Adria Symposium on Advances in Experimental Mechanics (2017) 75-77. [12] R. Petrella, M. Tursini, L. Peretti, M. Zigliotto, IEEE International Aegean Conference on Electrical Machines and Power Electronics (2007).
ScienceDirect Materials Today: Proceedings 5 (2018) 26703–26708
www.materialstoday.com/proceedings
DAS_2017
Measuring rotor speed for wind vector estimation on multirotor aircraft Dino Hüllmanna,*, Niels Paula, Harald Kohlhoffa, Patrick P. Neumanna, and Achim J. Lilienthalb a
Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 87, 12205 Berlin, Germany Örebro University, School of Science and Technology, AASS Research Centre, 70182 Örebro, Sweden
b
Abstract For several applications involving multirotor aircraft, it is crucial to know both the direction and speed of the ambient wind. In this paper, an approach to wind vector estimation based on an equilibrium of the principal forces acting on the aircraft is shown. As the thrust force generated by the rotors depends on their rotational speed, a sensor to measure this quantity is required. Two concepts for such a sensor are presented: One is based on tapping the signal carrying the speed setpoint for the motor controllers, the other one uses phototransistors placed underneath the rotor blades. While some complications were encountered with the first approach, the second yields accurate measurement data. This is shown by an experiment comparing the proposed speed sensor to a commercial non-contact tachometer. © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of the Committee Members of 34th DANUBIA ADRIA SYMPOSIUM on Advances in Experimental Mechanics (DAS 2017). Keywords: wind vector estimation; rotor speed; UAV; tachometer
1. Introduction Ambient wind plays an important role in aviation since it can affect the flight characteristics significantly. Especially small unmanned aerial vehicles (UAV) are very sensitive to wind due to their low inertia, so that high
* Corresponding author. Tel.: +49-30-8104-4790; fax: +49-30-8104-1917. E-mail address: [email protected] 2214-7853 © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of the Committee Members of 34th DANUBIA ADRIA SYMPOSIUM on Advances in Experimental Mechanics (DAS 2017).
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Hüllmann et al. / Materials Today: Proceedings 5 (2018) 26703–26708
wind speed and strong wind gusts may limit their use [1]. Hence, for applications like search and rescue operations, infrastructure inspection and environmental monitoring it can be crucial to know the local wind conditions. Furthermore, wind can have a direct influence in some scenarios, for example if the propagation of gas plumes is examined [2]. In such scenarios, it is often important to have an appropriate estimate of both the wind direction and wind speed in real-time. Usually, meteorological services provide averaged wind estimates for heights greater than 10 m above ground level, but the wind conditions can vary considerably in a local area over time and at lower heights [3], at which small UAV are often operated. Therefore, it can be necessary to measure the wind in-situ, for example by using mobile wind sensors (anemometers). However, such sensors can only provide measurements for their immediate surroundings while one might be interested in the wind distribution over the whole area of interest. This can be obtained, for instance, by performing the measurements on board the UAV. In case of fixed-wing aircraft, typically Pitot tubes are used as sensors for wind speed measurements. Pitot tubes measure the difference between the total and static pressure caused by an air stream, which can be used to calculate the velocity of the air [4]. Special sensor designs have been developed, for instance the five-hole probe shown in [5], that allow to measure the wind direction as well. However, this method cannot be easily adopted to rotary-wing aircraft like small multicopters, because the rotor wash causes strong perturbations of the aerodynamic field surrounding the aircraft. To overcome this influence, the sensor can be placed far off the centre of the airframe, as done by Bruschi et al. [6]. However, such a design has several disadvantages: First, the total weight is increased lowering both the flight time and the ability to carry other payload. Secondly, the flight characteristics become more unstable due to the displaced centre of mass. Therefore, other, indirect approaches to measure the wind vector have been developed. In particular methods based on observing the state variables of aircraft enjoy great popularity. For the first time, this was done by Divitiis [7], who measures the state variables of an aircraft with two coaxial rotors and computes the wind vector using a variational technique and an artificial neural network. Neumann and Bartholmai [8][9] presented an easier approach by inferring the wind vector from simple pose measurements of the aircraft. However, their papers only address the estimation of the horizontal wind components. In this paper, we show how that approach can be extended to the 3D case. This extension, however, requires measurements of the rotational speed of the rotors. Therefore, we present two approaches to this and show results from an experiment comparing the sensor we propose with a commercial non-contact tachometer. 2. Theory: Wind Vector Estimation Fig. 1a shows the wind triangle [8] which gives a relation between the ground speed of an apircraft, , its airspeed, , and the wind speed, . The term airspeed refers to the speed of an aircraft relative to the air. This yields the following equation: =
+
(1)
a)
b) Thrust
Airspeed
Drag
Gravitation
Fig. 1. (a) Wind triangle; (b) Principal forces acting on the multicopter.
Hüllmann et al. / Materials Today: Proceedings 5 (2018) 26703–26708
26705
The multicopter is considered to be a rigid body, thus its translational dynamics can be described according to the Newton-Euler equations: ⋅
=
(2)
is the acceleration of the aircraft expressed in its body frame and are where denotes the mass, C the external forces acting on the airframe expressed in . We assume that, principally, the gravitational, thrust and drag (or friction) force contribute to . Let , be the thrust force generated by rotor , the total thrust becomes [10] =∑ where rotors and
,
∑(
=
)
(3)
is an aircraft specific thrust constant, is the air density, the rotor swept area, the radius of the the angular velocity of rotor . In the body coordinate frame the thrust force is defined as = 0 0
.
(4)
With ⨀ denoting the Hadamard (or Schur) product, the drag force [9] can be written as =
(
⨀
⨀
⨀
)
(5)
is the vector of drag coefficients and is the vector of projected surface areas of the airframe. The where gravitational force is given in the fixed coordinate system by = 0 0 −
⋅
.
(6)
It can be transformed into the body coordinate system by the rotation matrix =
⋅
: (7)
Therewith, the equation of motion (2) becomes ⋅
=
+
+
.
(8)
Starting from (8) a solution for can be derived using (5). By inserting the result into (1) the actual wind vector, , can be computed. A typical small UAV is equipped with sensors like an inertial measurement unit (IMU), global positioning system (GPS) and a compass. This instrumentation allows to measure or estimate , and . Values for , , C , , , , and are given or can be determined experimentally. In contrast, the rotor speeds have to be measured online. 3. Measuring the Rotor Speed Usually, radio controlled multirotor aircraft are driven by electric brushless motors that are powered by electronic speed control (ESC) circuits. Typically, the flight controller computes a speed setpoint for each rotor and transmits this value to the respective ESC as a pulse-width modulated (PWM) signal. There are basically two approaches to measure the rotational speed of a rotor: One is to pick the signal controlling the ESC, either in software or electrically, the second one is to measure the rotor speed directly at the motor or rotor, respectively.
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Hüllmann et al. / Materials Today: Proceedings 5 (2018) 26703–26708
3.1. First Approach: Exploiting the PWM Signal In [11] the first approach was taken and a relation between the PWM signal controlling the ESC and the speed of a motor with unmounted rotor blades was found. However, the experiment took place under laboratory conditions and therefore does not take all relevant effects into account. For instance, a laboratory power supply was used during the experiment that supplies the ESC with a constant voltage, but the voltage provided by the battery of the aircraft varies greatly during a real flight. On one hand, the voltage depends on the instantaneous power demand of the aircraft which in turn depends on the current flight conditions. On the other hand, the voltage drops continuously as it correlates with the charging level of the battery. A non-linear relation between the voltage, the PWM signal and the rotational speed of the rotors was observed during real flight experiments. Moreover, it seemed that aerodynamic effects influence the rotor speed as well, in a way that is not reflected in the PWM signal. 3.2. Second Approach: Optical Measurement For the reasons mentioned before we refrained from the first approach and took the second one. There are several methods to measure rotational speed: Typical approaches are to install a hall sensor or to tap the counterelectromotive force, also known as back EMF, from the brushless motors. Another common way is to use noncontact tachometers which measure the reflection of a laser beam. Usually, this requires a small reflector to be stuck on the rotating component. Since the electrical system of a multicopter is its most crucial part, the goal is to affect it as little as possible, which can be achieved best by optical sensing. Therefore, the measurement principle of a non-contact tachometer is implemented in a slightly modified way: The rotor blades themselves and the ambient light replace the reflector and the laser source. Hence, only a photodiode is required to measure the rotor speed. Admittedly, this approach is unsuitable for flying at night, but it can be easily adopted to other rotary-wing aircraft.
Fig. 2. The red housing contains a phototransistor.
Fig. 2 shows the implemented solution with a phototransistor placed next to the motor, so that it can measure the bright-dark-boundaries while the rotor is spinning. The phototransistor is connected to an analog-to-digital converter (ADC) that delivers the quantised value to a microcontroller for further processing. Basically, the proposed sensor generates a pulse wave signal with its period depending on the instantaneous rotor speed. The latter is computed using a mixed method of frequency and period measurements as shown in [12]. 4. Experiment Setup and Result To validate the proposed phototransistor-based sensor system, its output is compared to a commercial non-contact tachometer using the setup shown in Fig. 3. As can be seen in the figure, the rotor arm is fixed to the ground to avoid
Hüllmann et al. / Materials Today: Proceedings 5 (2018) 26703–26708
26707
a lift-off. In addition, a usual desk light is used to illuminate the phototransistor to get a clear signal, since the spectrum of the ceiling light did not match the sensitivity range of the phototransistor, which is optimised for daylight conditions.
Fig. 3. Setup to compare the proposed sensor and a non-contact tachometer (UNI-T UT372).
A “thrust trajectory” was generated manually by varying the throttle stick of the aircraft’s remote control. Fig. 4 shows the result of the comparison of the proposed sensor and the non-contact tachometer. There are three things to note: First, the tachometer measurements are inaccurate if the rotor spins faster than 6000 rpm. This is caused by the thrust force generated at this rotational speed, which lifts the rotor arm and thereby moves the reflector out of the laser beam. Secondly, the tachometer signal contains more noise and outliers, which can be seen for example at 10 s and around 30 s in the measurement data shown in Fig. 4. Thirdly, there is a small lag in the signal from the tachometer although the fastest sampling rate available was chosen. This can be especially seen at the end of the measurement series, where it takes some time until the device recognises that the rotor stopped spinning.
Fig. 4. Rotor speed measured by the proposed sensor and a commercial non-contact tachometer.
In fact, the proposed sensor is suitable to generate accurate ground truth measurements with a lag of 40 ms caused by the smoothing filter, cf. [12], and thus is sufficient to meet the accuracy required for the wind vector estimation
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Hüllmann et al. / Materials Today: Proceedings 5 (2018) 26703–26708
algorithm. In addition, the measurement principle is highly resistant to disturbances since it is based on binary data, i.e. the bright-dark-boundaries of the spinning rotors. 5. Conclusion and Outlook We derived an approach to estimate the wind vector based on an equilibrium of the principal forces acting on multirotor aircraft. Several parameters are required to perform this computation, however, most of them are already measured by off-the-shelf multicopters or they are constants that need to be identified once. Beyond that, the rotor speed has to be measured in real-time. Unlike for the other parameters, this requires additional sensors. In this paper, we showed a solution to this problem based on a phototransistor detecting the rotational speed of the rotors. A comparison with a commercial laser-based non-contact tachometer showed that the proposed sensor generates accurate, low-noise and low-latency measurements, which are sufficient for the intended wind vector estimation algorithm. Moreover, the proposed solution is very cost-efficient and can be applied to any rotary wing aircraft. Next, the remaining parameters characterising the aircraft have to be identified. We are going to do this by letting the multicopter hover next to 3D ultrasonic anemometers under varying wind conditions. Afterwards the performance of the algorithm can be benchmarked in real flight scenarios. References [1] W. L. Chan, C. S. Lee, F. B. Hsiao, Meas. Sci. Technol. 22.10 (2011). [2] R. A. Russell, D. Thiel, R. Deveza, A. Mackay-Sim, IEEE Int. Conf. Robot. 1 (1995) 556-561. [3] J.-F. Geleyn, Tellus A 40A.4 (1988) 347-351. [4] R. Klopfenstein Jr, ISA T. 37.4 (1998) 257-263. [5] A. Van den Kroonenberg, T. Martin, M. Buschmann, J. Bange, P. Vörsmann, J. Atmos. Ocean. Tech. 25.11 (2008) 1969–1982. [6] P. Bruschi, M. Piotto, F. Dell’Agnello, J. Ware, N. Roy, Procedia Engineer. 168 (2016) 802-805. [7] N. de Divitiis, J. Aircraft 40.4 (2003) 759-767. [8] P. P. Neumann, S. Asadi, A. J. Lilienthal, M. Bartholmai, J. H. Schiller, IEEE Robotics & Automation Magazine 19.1 (2012) 50-61. [9] P. P. Neumann, M. Bartholmai, Sensor. Actuat. A-Phys. 235 (2015) 300-310. [10] X. Xiang, Z. Wang, Z. Mo, G. Chen, K. Pham, E. Blasch, Digit. Avion. Syst. Con. (2016). [11] D. Hüllmann, N. Paul, P. P. Neumann, 34th Danubia-Adria Symposium on Advances in Experimental Mechanics (2017) 75-77. [12] R. Petrella, M. Tursini, L. Peretti, M. Zigliotto, IEEE International Aegean Conference on Electrical Machines and Power Electronics (2007).