Development of a Sensor Suite for a Flapping Wing ...

AIAA 2008-224

46th AIAA Aerospace Sciences Meeting and Exhibit 7 - 10 January 2008, Reno, Nevada

Development of a Sensor Suite for a Flapping-Wing UAV Platform Jared A. Grauer∗ and James E. Hubbard Jr.† Department of Aerospace Engineering, University of Maryland, College Park, MD, 20742

This study investigates the attitude flight dynamics of a flapping-wing ornithopter. To do so, a custom suite of sensors is designed and constructed, incorporating conventional MEMS sensors used with fixed-wing and rotary-wing UAVs, as well as additional sensors needed to capture motions unique to flapping-wing vehicles. Lessons learned in the design process are discussed and recommendations are made for similar applications. Characteristic dynamics of the wing stroke are presented for straight and level flight, where time synchronous averaging was applied to enhance the signal to noise ratios of the periodic measurement signals.

Nomenclature a g p q r δlat δlon δthr θ ξ φ φw ψ ω

linear acceleration vector gravitational acceleration roll rate pitch rate yaw rate lateral control input longitudinal control input throttle input pitch angle orientation vector roll angle wing angle yaw angle rotation rate vector

Subscript a actual cf computed using a complimentary filter d desired ma computed using magnetometers and accelerometers x body X axis, located at the CG and pointed out the nose y body Y axis, located at the CG and pointed out the mean position of the right wing z body Z axis, located at the CG and pointed down

∗ Graduate † Langley

Student, Department of Aerospace Engineering, Member AIAA. Distinguished Professor, Department of Aerospace Engineering/National Institute of Aerospace, Associate Fellow

AIAA

1 of 13 American Institute Aeronautics Copyright © 2008 by the American Institute of Aeronautics and Astronautics, Inc. All of rights reserved. and Astronautics

I.

Introduction

rnithopters can be defined as aerodynes which generate lift and thrust forces from the flapping of O wings. Due to the miniaturization of electronics packages with micro electro-mechanical systems (MEMS) technology and the production of light-weight materials with composite technology, ornithopters are currently being explored as a potential alternative to fixed-wing and rotary-wing unmanned air vehicle (UAV) platforms for performing autonomous and semi-autonomous missions.1–3 Flapping-wing vehicles usually mimic either insect, bat, or avian flight and while these aircraft have their own set of drawbacks, they have numerous advantages over fixed-wing and rotary-wing counter parts at the small scales and low reynolds numbers currently under investigation. For example fixed-wing UAVs suffer from a decrease in the lift-to-drag ratio as airfoils are scaled down into the regime where viscous forces become significant.4 Miniature rotary-wing vehicles are maneuverable and have the ability to hover, but suffer from large power requirements and are generally limited to indoor flight.5 While ornithopters cannot hover, they can fly both slowly and quickly in outdoor environments. Additionally it is expected that the highly dynamic flight can allow for a degree of agility and maneuverability on par with birds. Donning an avian appearance, ornithopters have a degree of contextual camouflage, making them well suited for outdoor missions such as surveillance, atmospheric data collection, crop surveying, wildlife population monitoring, and airport wildlife control. Although there have been many developmental design studies,6–8 flight simulations,9–11 and aerodynamic models of ornithopters,12–14 it remains unclear what the flight characteristics of ornithopters are from a vehicle dynamics viewpoint, and to what degree design modifications affect those flight characteristics. This work presents a first step in the direction of experimentally characterizing the flapping flight of ornithopters. This paper begins with an introduction to the ornithopter selected for this study, followed by an overview of the sensing hardware. Design alternatives and problems encountered are mentioned to help guide those following similar paths. Typical flight measurements are then presented and characteristics of the flight motion are discussed.

II.

Ornithopter Test Bed

The ornithopter used in this study, shown in Figure 1, is a modified version of the Slow Hawk II series of ornithopters by Sean Kinkade.15 Although both smaller and larger versions are available, this ornithopter was selected for this study for its ease of piloting and the available payload capacity for adding sensors. This particular vehicle has a wingspan of 4 ft and a stock mass of 450 grams. The wings sail is an arrangement of rip-stop polyester segments, fastened together with Dacron tape. Although a flexible membrane, the wing sail maintains shape with a set of small carbon fiber spars spread along the trailing edge, a secondary carbon fiber spar running from the aft section of the ornithopter to the wing tip, and a primary carbon fiber spar spanning the leading edge. The airframe is cut from a single piece of 1/8 in carbon fiber. To keep the vehicle robust to crashes, the head stock has been reinforced with additional plies of carbon fiber. The ornithopter is flown in much the same manner as other RC vehicles: a pilot launches the ornithopter by hand and then uses a 72 MHz transmitter to broadcast joystick commands to an on-board receiver, which in turn directs power from a 7.4V lithium polymer battery to the actuators. The throttle joystick is used to generate lift and thrust forces by controlling the rotation speed of a DC motor, which spins a set of gears and flaps the primary wing spars. The elevator and aileron joysticks control the orientation of a tail flap, which creates aerodynamic torques to rotate the ornithopter. The elevator control pitches the tail flap up and down to create pitching moments, whereas the aileron command rolls the tail flap to create coupled rolling and yawing moments. Pitching moment inputs will be referred to as longitudinal control and the coupled rolling and yawing moment inputs will be referred to as lateral control. Ornithopter dynamics differ from conventional fixed-wing UAVs in that a significant amount of mass is periodically shifted in order to produce lift and thrust, altering the mass distribution of the vehicle. The center of gravity and moments of inertia must be known because the vehicle dynamics are typically written in terms of a reference frame placed at the vehicle center of gravity. Additionally the center of gravity must be known to correct accelerometer measurements for acceleration due to rotation. Together the wing spars and wing sail comprise roughly 18% of the total mass of the ornithopter. To estimate the center of gravity and rigid body moments of inertia, a component build-up method was used with measurements of the wing angle, as shown in Figure 2. This ornithopter is currently configured to have a wing angle range of ±25◦ , but a wing angle range of ±90◦ is shown to illustrate trends. In the wing angle range for this ornithopter,

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Figure 1. Ornithopter test bed with reference coordinate frames.

the center of gravity shifts roughly 2 in along the body z axis and the moments of inertia shift up to 0.25 slug-in2 . The movement of the tail flap has a negligible effect on the ornithopter mass distribution. 2

5 Ixx Iy y 4.5

Inertia (slug-in2 )

CG Location (in)

0

−2 X −4

Y Z

−6

Iz z

4

3.5

3

−8

−10 −100

−50

0

50

100

2.5 −100

−50

Wing Angle (deg)

0

50

100

Wing Angle (deg)

(a) Center of gravity.

(b) Primary moments of inertia.

Figure 2. Changes in the estimated rigid body mass distribution due to the flapping motion of the ornithopter.

III.

Sensor Suite Design

In order to observe the dynamics of ornithopter flight a variety of sensors are needed. At this point it is uncertain which sensors are required to fully observe the dynamics of flapping flight, so common sensors are included, as well as those thought to be necessary from intuition gained by flying the ornithopters. This section presents the sensing hardware and the avionics board created for collecting and storing flight data. III.A.

Measurement of Control Inputs and Responses

Control inputs are needed to compute trim conditions and linearized models of the dynamics. In simulation and identification applications, it is often assumed that control inputs can be measured very precisely.16 The throttle input is typically normalized so that values range between 0 and 1. When the tail is flat relative to the body axes of the vehicle, the longitudinal and lateral controls are zero. As the aft section of the tail is raised, the longitudinal control takes on negative values. As the tail is rolled to the right the lateral control

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takes on negative values. The desired inputs δthr,d , δlon,d , and δlat,d commanded by a pilot can be measured using a variety of methods. On the ground, additional wires and an A/D converter can be added to the joystick potentiometers inside the transmitter, giving a direct measurement of the joystick angles as the pilot moves them. Alternatively, most RC transmitter units are equipped with a trainer port for switching control over to a second transmitter, manned by a student pilot. One of the connections in this port contains a digital pulse train concatenating all of the controls available on the transmitter, updated at 50 Hz. This signal can be spliced and the relevant pulses can be decoded and recorded. Both of these methods can provide clean measurements of the input signals, but require a second off-board data record and a method of synchronizing the on-board data set with the off-board data set. Measurements of the pilot inputs can also be taken on-board the vehicle by splicing the ground and digital control signals running between the receiver and the actuators. These signals are typically of TTL logic and update at 50 Hz, which are amenable to measurement with a microprocessor using digital interrupt or capture and compare modules. This method allows for a single data record with the addition of only a few wires. For this ornithopter, the tail flap is attached to the servo motors using rubber O-rings, resulting in a second order response of the tail flap to orientation commands. It has been observed in flight and wind tunnel testing that strong wind gusts and added inertia from lateral structural vibrations produce errors in the orientation of the tail flap. For these reasons it was decided to measure the actual control deflections of the tail flap, δlon,a and δlat,a , allowing the detection of this error and the development of actuator models. A variety of sensors can be added, such as potentiometers and shaft encoders, to measure the angular deflection of the servo horns. However additional sensors would add mass to the most flexible portion of the ornithopter airframe. One alternative is to use the potentiometer available inside the servo motor to measure the true orientation of the tail flap. Figure 3(a) shows an opened servo motor with wires attached to the potentiometer. This configuration produces analog voltage signals ranging from 0.40V to 1.15V. In order to obtain better resolution on the measurements, the operational amplifier circuit shown in Figure 3(b) was added and resistor values were chosen so that the signal is centered in and completely fills the voltage range of the A/D converter. This method adds only a few more wires and an amplification circuit, which can be constructed using very small operational amplifier packages and size 0402 resistors.

(a) Opened servo motor with wires attached to the poten- (b) Amplifier circuit for boosting servo potentiometer signals and tiometer for actuator response measurement. increasing measurement resolution. Figure 3. Measurements of servo motor response.

III.B.

Measurement of Wing Angle

The forcing generated by the flapping wing is the predominant motion in the flight of this particular ornithopter. The forces generated on the aircraft center of gravity are highly correlated with the wing angle φw and its first derivative. The wing angle is zero when the wings are level and takes on positive values during the upstroke motion. In order to measure the angle of the wing, a non-contact magnetic potentiometer, shown in Figure 4, was constructed. The sensor consists of a printed circuit board with a two-dimensional halleffect sensor by GMW Associates17 on the front of the board to measure the magnetic fields of a permanent 4 of 13 American Institute of Aeronautics and Astronautics

magnet. An interpolator chip on the back of the board then converts these measurements into a single voltage proportional to the rotation of the magnet. On the ornithopter, a permanent magnet is fastened onto the wing hinge and rotates with the primary wing spar, allowing the sensor to directly measure the wing angle. To mount the sensor to the ornithopter a small plastic housing was printed using a rapid prototyping machine. The electronics are powered by a 5V source and consume 21 mA. The output measurement signal varies linearly between 0.5V and 4.5V over the 360◦ range of rotation. Resolution of the hall-effect module is specified as 9 bits and response time is given as 5 ms. The sensor has dimensions 0.5 in by 0.5 in and mass 0.67 grams.

Figure 4. Wing angle sensor.

III.C.

Inertial Measurement Unit

An inertial measurement unit (IMU) typically consists of a combination of magnetometers, rotational gyroscopes, and linear accelerometers, and is useful for providing attitude information of the vehicle. Magnetometers are used in this context to measure the projections of the magnetic field of the Earth and provide an inertial reference. Measurements are corrupted by distortions in the local magnetic field, such as the on-board DC motor and metal components. Typically UAVs are flown within a small area where the magnetic field can be approximated as uniform and calibration procedures such as those presented by Gebre-Egziabher18 can be used. Rotational gyroscopes measure the body rotation ω = [p, q, r]T but are notorious for having a bias exhibiting random walk. Initial gyroscope calibrations can be performed using a rate table, however errors will be introduced due to temperature changes between the laboratory and the flight field. In post-processing these calibration errors can be corrected using kinematic compatibility relations with other sensors.16 Accelerometers measure both the dynamic acceleration of motion and the static acceleration of gravity. Calibration can be performed by taking static measurements of the IMU in six orientations, giving four measurements of the bias and two measurements of gravity for each of the three channels. Additionally the IMU can be mounted on a rate table to obtain other values of acceleration. When translational accelerations are small, the accelerometers function as tilt sensors, providing a second inertial reference. Measurements from an IMU can provide attitude information to an observer, or in slow moving applications such as helicopters19 and underwater vehicles,20 can be used directly to estimate orientation. Specifically, magnetometer and accelerometer signals can be combined using the TRIAD or QUEST algoˆ θ, ˆ ψ] ˆ T . This estimate however is noisy due to rithm21 to yield one estimate of the orientation ξˆma = [φ, ma the high band-width of the accelerometers and the high frequency noise added to the magnetometer signals by the DC motor. Orientation estimates can be blended with integrated gyroscope signals using a complimentary filter. Mahony et al.22 describe several ways of designing and implementing the complimentary filter, a common realization of which is given by Equation 1, where a recursive formulation is used with a proportional gain Kp to regulate estimation errors. ˙ ξˆcf = Kp (ξˆma − ξˆcf ) + ω

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(1)

The IMU selected for this study is the Mag3 sensor by MEMSense,23 which houses orthogonal triads of MEMS magnetometers, gyroscopes, and accelerometers. The analog outputs are band-limited to 50 Hz and have sensitivities of 1 V/gauss, 5 mV/deg/s, and 400 mV/g, respectively. Additionally gyroscope references and temperature measurements are provided to help correct for the bias problem. The IMU is powered by a 6V source and consumes 30 mA. The sensor has dimensions 0.91 in by 1.50 in, and has mass 10.3 grams. Due to the highly dynamic motion observed, the IMU model was chosen with ±1.9 gauss, ±300 deg/s, and ±10 g ranged on the magnetometer, gyroscope, and accelerometer. III.D.

Avionics Board Integration

In order to collect measurements from the sensors and record data, an avionics board, shown in Figure 5, was designed using Eagle by Cadsoft,24 commercially manufactured, and manually populated using convective reflow soldering equipment. Fully assembled, the board has dimensions 1.75 in by 2.75 in and mass 27.9 grams. Since the DC motor used for producing the flapping motion draws heavily on the battery supply, a dedicated 7.4V lithium polymer battery with mass 23.9 grams was added for powering the avionics board. Supply power is distributed by four voltage regulators: two 5V regulators to power the microprocessor and sensors, one 3.3V regulator to power the memory card, and one 6V regulator to power the IMU. Relatively large capacitors connect each of the regulator inputs and outputs with a ground plane to help smooth variations in the supply voltage. The board is controlled with a PIC18F8722 microprocessor by Microchip,25 running at 40 MHz. In order to keep the hardware flexible, programming pins were added with which to update the microprocessor software. To debug the avionics system and the microprocessor software, UART module pins were added, which can be used to convert messages to the RS-232 protocol, to be read on a computer. Pins are also provided for connecting to the digital receiver signals, the servo potentiometers, and the wing angle sensor. For convenience the IMU is directly bolted to the avionics board and connected using detachable JST connectors. This configuration was found to save space on the ornithopter, as the IMU may be offset from the surface of the avionics board. Since the gyroscopes are temperature dependant, the IMU was placed far away from the voltage regulators. To avoid aliasing, all analog sensor signals are filtered with RC circuits with cutoff frequencies at 50 Hz. Analog to digital conversion is performed using three 16-bit ADS8344 modules by Texas Instruments,26 which operate on the first serial peripheral interface (SPI) bus of the microprocessor. Although the A/D convert could have been driven with a 10 MHz clock cycle, more accurate results were obtained using the slower 2.5 MHz clock cycle. Data is stored on-board using standard SD memory cards,27 which can hold over 1 GB of data. Other applications which do not require large amounts of storage capacity may use smaller EEPROM modules. The memory card operates on the second SPI bus of the microprocessor and requires two logic converters since memory cards operate at 3.3V while the microprocessor operates at 5V. Memory cards were found to be sensitive to the impedances of SPI lines, which led to the addition of RC circuits in the design between all digital devices on the bus. Unlike transmitting data to a ground station, the use of a memory card does not require contact with a second computer and a checksum algorithm. Additionally writing to a memory card is less power-intensive than broadcasting the data to a ground station. However using a memory card for data logging also has many drawbacks. For instance, the memory card can only be written to in blocks of 512 bytes, so that data must be stored in a memory buffer in 8-bit bytes until filled. If the number of measurements is not a power of 2, an additional buffer and computation time must be used since the measurements will not exactly fill a 512-byte buffer. Additionally, the process of writing to the buffer requires 15 ms of dedicated processing time, so that there are relatively large gaps in the collection of data. For this study gaps in the data is not a problem because the signals are periodic, but for many other applications this could be a problem. In these instances a wireless modem configuration like that presented by Conroy19 could be used.

IV.

Representative Measurements

Measurements showing the range of the desired and actual control inputs are shown in Figure 6. The throttle command is normalized as a percentage whereas the longitudinal tail deflection ranges from −50◦ to −18◦ and the lateral tail deflection ranges from −40◦ to +40◦ . The measurements of the digital commands were very clean, having variances of 0.0418%, 0.0027◦ , and 0.0032◦ in the throttle, longitudinal control, and

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lateral control channels. The longitudinal and lateral control servos have variances 0.0360◦ and 0.1719◦ . The lateral control servo motor was determined to be more worn than the longitudinal control servo motor and showed a visible amount of chattering, resulting in a higher variance than the longitudinal control servo motor. The avionics board was attached to the ornithopter along the longitudinal body axis. The ornithopter was flown for a series of flight tests, trimmed in the mean for straight and level flight. In this flight condition the signals measured are periodic with the flapping frequency. Time synchronous averaging28 was employed to average the signals over the duration of the wing stroke to gain a representative set of signals with an enhanced signal to noise ratio. The ornithopter wing beat frequency is approximately 5 Hz. The avionics board was configured to sample the sensors at 146 Hz, allowing 16 sets of data points to be taken for each channel over the duration of half a wing stroke, before the data buffer must be written to the memory card. Since the flight data is sampled at different points in the stroke of the wing, each measurement channel was interpolated into 100 points along the stroke. Using a set of 30 flap cycles, the measurements were averaged over the wing stroke. Synchronously averaged measurements are shown in Figure 7 and the discrete Fourier transforms of the time histories used to compute these measurements are shown in Figure 8. Accelerometer signals in Figure 7(d) have been corrected to the estimated center of gravity of the vehicle using the component buildup method, the wing stroke angle measurement, and Equation 2. The vector [∆x(t), ∆y(t), ∆z(t)]T is the relative distance between the IMU and the center of gravity. The angular accelerations have been estimated using smoothed differentiation of the gyroscope signals.29        (q 2 + r2 ) −(pq − r) ˙ −(pr + q) ˙ ∆x(t) ax ax 1       (2) =  ay  +  −(pq + r) ˙ (p2 + r2 ) −(qr − p) ˙   ∆y(t)   ay  g −(pr − q) ˙ −(qr + p) ˙ (p2 + q 2 ) ∆z(t) az CG az IM U The wing angle motion is approximately sinusoidal and has a frequency of 4.69 Hz. As seen in the frequency plots of Figure 8, this frequency and its harmonics dominant the signal spectra. The magnetometer signals vary with the flapping angle, indicating that the ornithopter is changing orientation over the wing stroke, but these signals are of secondary importance in this study since magnetometer readings are dependant on the orientation, location, and time measured, as well as the fact that they can not uniquely determine attitude. The gyroscope signals show a fair amount of rotation in the roll and yaw directions, but the largest rotation is in the pitching direction and is due to the flapping. As the wing angle increases during the upstroke the ornithopter pitches down, and as the wing angle decreases during the down stroke the ornithopter pitches up. The resultant aerodynamic forces generated on the wings acts forward of the center of gravity, creating an aerodynamic torque on the vehicle. The accelerations in the x and y directions are fairly small, but the flapping motion creates large accelerations in the vertical direction, ranging from -4 g to +3 g. These accelerations also include the static acceleration due to gravity, which as the pitch angle increases in magnitude, contributes more along the x axis and less along the z axis. The ornithopter seemed to be flying with a slightly positive angle of attack, which decreases the acceleration along the x axis due to the gravitational acceleration. The acceleration shown along this axis is positive due to the thrust generation of the ornithopter, which has been experimentally found to be positive over the entire stroke of the wing.30 The relatively large accelerations rule out attitude estimation techniques which use the accelerometer as an inclinometer.

V.

Conclusions

This paper has presented an investigation into the attitude dynamics of a flapping-wing UAV platform. The ornithopter used for this work was a relatively large ornithopter which had a slow wing beat frequency, a high wing loading, and an adequate payload capacity to add sensors without appreciably augmenting the flight dynamics. A small and light weight avionics board was designed, constructed, and flown which housed MEMS inertial sensors commonly used in UAV applications, as well as additional sensors thought to be needed from intuition gained by flying the ornithopters. Sensor data was logged on the avionics board with a commercially available memory card which could be removed from the ornithopter and downloaded on a computer. Flight data was presented for the mean flight condition of straight and level flight. Measurements were periodic with the flapping frequency, allowing for the use of time synchronous averaging techniques to produce a characteristic set of measurements with enhanced signal to noise ratios for each wing beat. Results 7 of 13 American Institute of Aeronautics and Astronautics

were presented and discussed. The sensor suite performed well in this application. The small battery pack used to power the avionics was large enough to suffice data collection for numerous flights. Sensors selected had measurement ranges which did not saturate and gave acceptable resolutions on the quantities measured. There was a concern that the DC motor would disrupt the measurement of the wing angle since the sensor measures the magnetic field, but there was no difference observed in the measurements with the motor turned off. The magnetometer measurements however were noticeably distorted by the DC motor. The motor should either be shielded, which adds weight to the vehicle, or the magnetometer needs to be calibrated while the motor is running and the wings are flapping, which can be awkward to perform. Since characterizing the wing stroke was one of the objectives of this study, the periodic sampling inherent with using a memory card was sufficient when time synchronous averaging was used. As system identification and closed-loop control become more of a priority, it will be necessary to migrate the avionics design away from the on-board data logging and over to streaming the data to a ground station with a modem. Data streaming on a microprocessor however takes a significant amount of processing and introduces a bottleneck into the data acquisition process. To help recover bandwidth, individual tasks can be delegated to smaller, less capable microprocessors. For instance there could be a central processor interfacing with high level commands from the ground station. A second microprocessor could be configured to continually sample the analog sensors. A third microprocessor could be tasked with streaming sensor measurements as they become available. It was observed from the results that the accelerations experienced in even the most benign condition of straight and level flight are much too large to use attitude estimators such as TRIAD and QUEST, which use the accelerometer as a tilt sensor to provide a second inertial reference measurement. In some applications this measurement could be substituted by photovoltaic sensors and knowledge of the sun location, or by a series of ultrasonic ranging units. Photovoltaic sensors would however restrict the vehicle flights to outdoor areas with the sun clearly visible. A configuration of four ultrasonic range sensors would be needed to calculate roll and pitch angles, which would add weight to the vehicle and increase the load on the battery, as well as restrict the vehicle to low altitudes. The ornithopter used in this study is one of the larger models commercially available. As the ornithopters are scaled down to smaller sizes, the wing beat frequency increases and the wing loading decreases so that the flapping motion has a less pronounced effect on the motion of the ornithopter. In these cases, it may still be possible to use non-model based attitude estimation techniques. Alternatively, model-based observers such as the Kalman filter could accept the inertial measurements and propagate estimates of the aircraft states. However an accurate model must first be developed and additional sensors may need to be added to observe dependencies on other state variables.

VI.

Acknowledgements

The authors would like thank the NASA Langley Research Center, the National Institute of Aerospace, and the University of Maryland for their support in this research. Also the authors would like to thank the members of the Morpheus Laboratory for their teamwork and motivation.

References 1 Raney, D. and Slominski, E., “Mechanization and Control Concepts for Biologically Inspired Micro Air Vehicles,” Tech. rep., National Air and Space Administrations, November 2004, NASA Technical Reports Server ID Number 20050140920. 2 Keennon, M. and Grasmeyer, J., “Development of the Black Widow and Microbat MAVs and a Vision of the Future of MAV Design,” International Air and Space Symposium and Exposition, American Institute of Aeronautics and Astronautics, July 2003. 3 Beasley, B., A study of planar and nonplanar membrane wing planforms for the design of a flapping-wing micro air vehicle, Master’s thesis, University of Maryland, June 2006. 4 Mueller, T. and DeLaurier, J., “An Overview of Micro Air Vehicle Aerodynamics,” Fixed and Flapping Wings Aerodynamics for Micro Air Vehicle Applications, edited by T. Mueller, Progress In Aeronautics and Astronautics, American Institute of Aeronautics and Astronautics, Reston, Va, 2001, pp. 1–10. 5 Bohorquez, F. and Pines, D., “Hover Performance of Rotor Blades at Low Reynolds Numbers for Rotary Wing Micro Air Vehicles,” 2nd AIAA Unmanned Unlimited conference and workshop and exhibit, American Institute of Aeronautics and Astronautics, September 2003. 6 DeLaurier, J., “An ornithopter wing design,” Canadian Aeronautics and Space Journal, Vol. 40, No. 1, March 1994, pp. 10–18. 7 R¨ abinger, H., “Wie Ornithopter Fliegen,” www.ornithopter.de, 2001.

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8 Benedict, M., Aeroelastic Design and Manufacture of an Efficient Ornithopter Wing, Master’s thesis, Indian Institute of Technology, June 2004. 9 Wu, J. and Popovi´ c, Z., “Realistic Modeling of Bird Flight Animations,” ACM Transactions on Graphics, 2003. 10 Rashid, T., The Flight Dynamics of a Full-Scale Ornithopter , Master’s thesis, University of Toronto, 1995. 11 Ulrich, E. and West, B., “Simulation of the Flight Dynamics of a Flapping UAV,” AIAA Student Conference Region I-MA, American Institute of Aeronautics and Astronautics, April 2005. 12 DeLaurier, J., “An aerodynamic model for flapping-wing flight,” Aeronautical Journal of the Royal Aeronautical Society, April 1993, pp. 125–130. 13 Sane, S. and Dickinson, M., “The Control of Flight Force by a Flapping Wing: Lift and Drag Production,” The Journal of Experimental Biology, 2001, pp. 2607–2626. 14 Ho, S., Nassef, H., Pornsinsirirak, N., Tai, Y., and Ho, C., “Unsteady aerodynamics and flow control for flapping wing flyers,” Progress in Aerospace Sciences, Vol. 39, 2003, pp. 635–681. 15 Kinkade, S., www.flappingflight.com, 2007. 16 Klein, V. and Morelli, E., Aircraft System Identification: Theory and Practice, AIAA Education Series, American Institute of Aeronautics and Astronautics, 1801 Alexander Bell Drive, Reston, Virginia, 2006. 17 GMW Associates, www.gmw.com, 2SA-10 Integrated 2-Axis Hall Sensor , January 2005. 18 Gebre-Egziabher, D., Elkaim, G., Powell, J. D., and Parkinson, B. W., “Calibration of Strapdown Magnetometers in Magnetic Field Domain,” Journal of Aerospace Engineering, April 2006, pp. 87–102. 19 Conroy, J. and Pines, D., “A Custom Micro Air Vehicle Avionics Package for System Identification and Vehicle Control Applications,” American Helicopter Society, January 2007. 20 Walter, M., “Tortuga: Autonomous Underwater Vehicle,” Project webpage at www.ram.umd.edu and project article available at www.auvsi.org/competitions/2007/Journals/UMD.pdf. 21 Hall, “Attitude Determination,” www.aoe.vt.edu/ cdhall/courses/aoe4140/attde.pdf, March 2003. 22 Mahony, R., Hamel, T., and Pflimlin, J.-M., “Complimentary filter design on the special orthogonal group SO(3),” Proceedings of the 44th IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers, December 2005, pp. 1477–1484. 23 MEMSense, www.memsense.com, 2007. 24 CadSoft Computer, Inc., www.cadsoftusa.com, Eagle 4.0 for Linux and Windows, 2000. 25 Microchip Technology, Inc., www.microchip.com, PIC18F8722 Family Data Sheet, 2004. 26 Texas Instruments, www.ti.com, 16-Bit, 8-Channel Serial Output Sampling Analog-to-Digital Converter , 2006. 27 Sandisk Corporation, www.sandisk.com, SanDisk SD Card Product Manual, 2nd ed., November 2004. 28 Hochmann, D. and Sadok, M., “Theory of Synchronous Averaging,” IEEE Aerospace Conference Proceedings, Institute for Electrical and Electronics Engineers, 2004, pp. 3636–3653. 29 Morelli, E., “Practical Aspects of the Equation-Error Method for Aircraft Parameter Estimation,” AIAA Atmospheric Flight Mechanics Conference, American Institute of Aeronautics and Astronautics, August 2006. 30 Harmon, R. and Hubbard, J., “Characterization of an Ornithopter Wing,” AIAA Young Professionals Conference, American Institute of Aeronautics and Astronautics, Johns Hopkins Applied Physics Laboratory, November 2007.

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(a) Obverse of board, showing 1) connector pins for digital control inputs, angle sensor, and servo potentiometer signals; 2) IMU; 3) LED and push button user interface; 4) connector pins for the UART, battery, and programming the microprocessor; and 5) the removable memory card and housing.

(b) Reverse of board, showing 1) voltage regulators for board and additional sensors, 2) RC filters for matching digital impedances on the SPI buses, 3) digital logic converters for communicating with the memory card, 4) analog anti-aliasing filters, 5) amplifiers for boosting servo potentiometer signals, 6) 16-bit A/D converters, and 7) microprocessor. Figure 5. Avionics board.

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1.2 1.2 1.2

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Time Time Time (s)(s) (s) (b) Longitudinal deflection Figure 6. Actuator inputs and responses.

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1 11

2 22

Time Time Time(s)(s) (s) (c) Lateral deflection

3 33

24

0.6

0.4

Normalized Magnetic Field (%)

16

8

Wing Angle (deg)

X Y Z

0

−8

−16

−24 0

0.2

0

−0.2

−0.4

0.25

0.5

0.75

−0.6 0

1

0.25

Normalized Wing Stroke

0.5

0.75

1

0.75

1

Normalized Wing Stroke

(a) Wing angle

(b) Magnetometer

300

3

2

200

1

Acceleration (g)

Rotation (deg/s)

100

0

0

−1

−100 −2

−200

−300 0

−3

0.25

0.5

0.75

1

Normalized Wing Stroke

−4 0

0.25

0.5

Normalized Wing Stroke

(c) Gyroscope

(d) Accelerometer

Figure 7. Synchronously averaged measurements using 30 wing strokes at 4.69 Hz and 100 interpolated pointed over the duration of each wing stroke.

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25

1 X

20

0.8

15

0.6

Magnitude

Magnitude

Y

10

5

0

Z

0.4

0.2

0

5

10

15

20

25

30

0

35

0

5

10

Frequency (Hz)

15

20

25

30

35

25

30

35

Frequency (Hz)

(a) Wing angle

(b) Magnetometer

250

3

2.5 200

Magnitude

Magnitude

2 150

100

1.5

1

50 0.5

0

0

5

10

15

20

25

30

35

Frequency (Hz)

0

0

5

10

15

20

Frequency (Hz)

(c) Gyroscope

(d) Accelerometer

Figure 8. Fourier transforms of the measurement time histories.

13 of 13 American Institute of Aeronautics and Astronautics