Paper UAV Gyrocopter "Juan de la Cierva" V1.0

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Authors: Carlos Matilla, Weiwei Cheng Chen, Alfonso J. Muñoz-Aycuens, Carmelo J. Fernández-Agüera, David Garcia, Luis de la Fuente, Rebeca Robles, Miquel Larsson, Alfonso Jurado, Jose Quesada, Pablo Calderon, Andrés Nicolas Quezada, Ignasi Albiol, Alberto Castellano, Manuel Carmona.

Advisors: José Joaquín Vila, Alberto Polo, Erno Peter

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Index I. INTRODUCTION...................................................................................................................................................................................3 II. Mechanics..............................................................................................................................................................................................3 A. Aerial System Requirements and choice...................................................................................................................................3 B. Gyrocopter: General description and brief history....................................................................................................................4 C. Prototyping...............................................................................................................................................................................4 D. Fabrication Process....................................................................................................................................................................6 III. Flight tests.............................................................................................................................................................................................6 A. Take off:.....................................................................................................................................................................................6 B. Fly:..............................................................................................................................................................................................6 C. Landing:......................................................................................................................................................................................6 D. Safety..........................................................................................................................................................................................6 IV. Electronics and Autopilot.....................................................................................................................................................................7 A. Links and Frequencies................................................................................................................................................................7 B. Method of Autonomy:.................................................................................................................................................................7 C. Ground Station: ..........................................................................................................................................................................8 D. Electronics Safety.......................................................................................................................................................................8 V. Modeling, Simulation and Flight Testing of an Autonomous Gyrocopter.............................................................................................8 VI. Artificial Vision..................................................................................................................................................................................10 A. Camera ....................................................................................................................................................................................10 B. Control .....................................................................................................................................................................................10 C. Target detection .......................................................................................................................................................................10 D. Target recognition ....................................................................................................................................................................11 E. Letter recognition ....................................................................................................................................................................11 F. Target positioning and communication ...................................................................................................................................12 G. Avoiding duplicates ................................................................................................................................................................12 VII. Acknowledge.....................................................................................................................................................................................12 VIII. References and Bibliography..........................................................................................................................................................13

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Abstract— FuVe, Future Vehicles and Entrepreneurs, proudly presents the UAV “Juan de la Cierva” project. The goal of this project is the design and construction of an autonomous gyrocopter for SUAS(Student Unmanned Air Systems) Competition. In this paper, we will present the vehicle, systems, the decisions we too and the reasons for those decisions. This publication is divided in Introduction, Mechanics, Flight Test, Autopilot and Electronics and Artificial Vision doing special effort to show the safety measures we planned and did. However, due to the special difficulties of the vehicle this year the project is focused in the basic tasks of SUAS leaving to next years the fullfilment of all the requirements of the competition.

I. INTRODUCTION FuVe, Future Vehicles and Entrepreneurs, is an association formed by students from different universities and knowledge branches which wants to educate these students through active learning. In 2012, FuVe faced it first challenge: the construction and development of an autonomous underwater vehicle(AUV) for the RoboSub Competition which took place in July and was organized by AUVSI(Association for Unmanned Vehicle Systems International) and ONR(Office of Naval Research). That project, called “Isaac Peral y Caballero”, was a success. It participated in the competition, won research awards and was invited to several tradeshows and conferences.

After RoboSub competition, the team of “Isaac Peral y Caballero” followed their work to take part in RoboSub 2013 while two members of the AUV project decided to leave the team and start a new one for SUAS Competition, the “Juan de la Cierva” team. The name of the project come from the inventor of the Gyrocopter, Juan de la Cierva.

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Time: The team was started in September 2012.

Human Resources: Only 2 people started the team in September 2012. After building a 10 member team, half of them left the project in March, 3 months before the competition.

Money: We started to received the money in February.

University Credits: We do not earn any university credit for this project. Furthermore, June is one of the exams period in Spain. We could not change the date of several of these exams, so FuVe members sacrificed their credits in order to participate in SUAS Competition.

II. MECHANICS

A. Aerial System Requirements and choice SUAS Competition provides several KPPs(Key Performance Parameters) which measures the success of the mission: Parameter

A. Team Facts: Part of our choices were motivated by the own conditions of the team. Therefore, in order to explain our decisions, it is necessary to speak about these conditions: 3

Objective

Autonomy

During way point navigation and area search.

All phases of flight, including takeoff and landing

Imagery

Identify any two target characteristics (shape, background color, orientation, alphanumeric, and alphanumeric color)

Identify all five target characteristics

Target Location

Determine target location ddd.mm.ssss within 250 ft

Determine target location within 50 ft

Mission time

Less than 40 minutes total Imagery/location/id entification provided at mission conclusion

20 minutes Imagery/location/identi fication provided in real time

Operational Availability (Ao)

Complete 50% of missions within original tasking window (no more than one time out)

Complete 100% of missions within original tasking window (no time outs used)

The main idea of the team was to create a project which was capable not only to participate in the 2013 SUAS Competition otherwise to continue thorough years.

“Isaac Peral y Caballero” and “Juan de la Cierva” project

Threshold

In-flight reAdd a fly to way Adjust search area tasking point Therefore, a number of requirements were done in order to select one type of aircraft: • Slow flying for proper image acquisition (Almost stationary) Team FuVe-Escuela Técnica Superior de Ingenieros Navales

• Low altitude stable flight for hight reconnaissance missions • Low Susceptibility to wind & turbulence • High range of action • Simple maintenance • Safety and high durability.

detail

The Gyrcopter had several applications in the 1920´s and 1930´s. Nevertheless, with the death of Juan de la Cierva and the invention of the helicopter, gyrocopters were almost forgotten and left only to leisure uses.

These necessities lead us to a concrete type of aerial system: the Gyrocopter.

Gyrocopter Cannot hoover Cannot take-off vertically Cannot land purely vertically Needs very short take-off strip or not needed Needs its length to land Very little sensibility to turbulences Larger flight envelope than fixed wings Easy to pilot Excellent manoeuvrability Easy for maintenance Less expensive than helicopter

Helicopter Can hoover Can take-off vertically Can land vertically Take-off strip not needed

Fixed Wing Don't try to hoover Cannot take-off vertically Cannot land vertically Needs a take-off strip

Needs its length to land Medium sensibility to turbulences Larger flight envelope than fixed wings Complex to pilot Superior manoeuvrability Relatively heavy maintenance Rather expensive

Needs a landing strip Sensible to turbulences Limited flight envelope Rather easy to pilot Average manoeuvrability Relatively easy for maintenance Slightly less expensive than gyrocopter Needs less power than gyrocopter

Needs more Needs a lot of power than power fixed-wing / trikes Table: Comparison between gyroplane, helicopter and fixed wing aircraft

B. Gyrocopter: General description and brief history.

Gyrocopters were the first rotary wing aircraft in the world. They were invented by Juan de la Cierva(18951936), a Spanish inventor who was worried about the unsafety of the first planes when they lost lift and therefore, was committed to find a safer way to fly which could not lose lift.

After a lot of researching, he found a way to achieve his objective, a vehicle which uses an engine to provide thrust and an unpowered rotor which generates lift thanks to the wind which passes through it from below following the fenomenon called autorrotation.

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C. Prototyping Firstly, we took an RC Gyrocopter in order to acquire experience with this type of vehicles and use, whenever possible, that vehicle to the competition. However, it was not possible. The construction, design and payload capability were unacceptable to complete the mission thresholds in SUAS competition. Therefore, we had to learn how gyrocopters work to achieve one that is valid to the fulfill the tasks. This is the process we followed: •

JC MK.1 : On this version, we were trying to overcome the main rotor problems we found on the commercial model we were trying to fly. First of all, original rotor blades did not achieve the quality levels we were looking for. Center of mass and weights were not symmetrical on both blades (sometimes more than 2.5 inches displaced and 1.8 OZ or 50g. difference between them) , that brought us great amount of vibrations during prerotation of the rotor, making in some case our vehicle to overturn prior take off. To detect faulty blades, we had to create quality control methods to accurate measure this differences on a go/no go criteria and on-rotation blade tracking methods. In order to solve the problem, we looked for higher grade helicopter blades on the market (Since we couldn't find any gyrocopter blade of the size we needed). We found a solution on brand called Vario Blades. On this new brand, we achieved 100% reliability till date, but we had to design a new parts to assemble this new blades to the commercial rotor head. On our new design we introduced Team FuVe-Escuela Técnica Superior de Ingenieros Navales

composite materials and a new heavy duty one point joint which allowed us to get drag hinge movement on the blades. We also introduced some structural changes, which included Z shaped keel which allowed us to send the main airflow stream directly to the rudder, and a higher landing gear to avoid tailstrike during takeoffs.

Rotor head MK. 2 thanks to MESIMA •

Rotor head MK. 1 •

JC MK. 2: When we started using helicopter blades, we knew this only was a temporary solution, since original blades (contrary to helicopter’s), did have an angle of attack. Mk.1 put as far from the STOVL characteristics we were looking for. To do so, we started designing a new blade assembly that could give us the option to easily change to a desire angle of attack prior to take-off. After some tuning, it became a great success, allowing us to take off as short as 5.5 yards on first attempts. We also found Z bar structure slowed down our building times, so, instead of making the structure taller, we kept the original “all horizontal keel” and made the wooden vertical stabilizer higher, also changing the horizontal stabilizer union height to the main motor’s. That way we achieved enough air stream through them. To avoid counter rotary torque from the main engine, we installed a commercial coaxial gear box, which gave it more stability, but lowering our motor reliability rate due to the large amount of small sized bolts and nuts it required. With this model, we can only carry a 5000 mAh Battery for the main engine and a 1300 mAh for the other systems on-board. With this configuration, the autonomy is quite low, around 5 minutes and our expected mission is to take off and flight through the waypoints only. That is why we decided to take this model to the next level.

JC MK.3 : This time, we tried to increase payload, efficiency and reliability, without dropping the high stability achieved by concealing counter rotary torque. We found the solution by installing 2 transverse mounted motors on the same pusher configuration, turning on opposite directions, but holding them outside the main structure by a transverse beam a. That way, both engines could get a more constant and higher rate airflow to them, improving efficiency. In addition, we can lift a total weight of 10.58 pounds. With this capability, we are able to carry an 11000 mAh battery for the main engine and 2200 mAh for the other systems on board, which make our gyrocopter capable of more than 11 minutes flying.

JC MK. 3 Vehicle

Thrust

JC MK. 1/2

5.29 pounds(2,4 kg)

JC MK. 3

10.14 pounds (4,6 kg)

Table: Thrust

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D. Fabrication Process The basis criteria followed in the fabrication process is the time optimization. Due to the innovation of the design, tests and consequent crashes, it was very important to have lots of spare parts and methods of easy and quick production.

The aluminum structure is resistant enough to stand the operational conditions. In addition, it is easy to replicate and its assembly is simple.

In crashes, the most damage parts are the main structure and the tail empennage. Firstly, we produced the empennage with plywood. Nevertheless, it consumed quite time to repair and duplicate. That is why, we changed the plywood for balsa wood. It resistance was worse but the difference was not significant. Once the vehicle was crashed, both materials were catastrophically damaged.

Furthermore, balsa wood reduced the weight.

We followed same decisions in other parts of the design like tail structure, rotor, etc.

Another factor is the vibrations when the pre-rotation is coming, so the main rotor should be aligned, for this, it’s necessary use long rope, fix it on the tip of the main rotor and cross middle of the rotor. For our safety, we also used a platform for pre-rotation. This platform hold the autogyro during pre-rotation. When Pre-rotation rpm’s are achieved ,only needs to leave the platform taxing to the front full speed and it will lift off the ground with almost none pitch.

Headwind direction during takeoff and landing its highly recommended.

B. Fly: The autogyro is not an helicopter or plane, but have something that is very similar, the flight is slower than plane but controls are different than planes. Autogyro’s movements are very complex, because need specific throttle when change the direction, so need change the directional very slowly, because it tends to tip over.

Also, it’s very difficult to see in the sky, because the fuselage is very thin and when it is far away, see it is impossible. The solution that we have taken is painting the body with color that we can recognize in the sky.

However, the autogyro is more stable than planes and helicopters, has long autonomy (more than helicopters).

JC Mk. 3 going to the air from the take off platform

C. Landing: There are two ways you can land with an autogyro.

III. FLIGHT TESTS

Flight tests have been really difficult. We did more than 55 flight attempts but only few were succesful. The first complete flight took place the first week of May 2013 An Autogyro is a completely different aircraft, with very little technical information about it, and it took quite a long time for our team to learn correct configurations and flying skills.

A. Take off: The advantages of the autogyro is more stable than other aircrafts. However, it is not completely true during take off.

First, Autogyro needs pre-rotation. This is very important because if it doesn’t reach enough rpm’s during prerotation, the gyrocopter becomes unstable during takeoff “simillar to stall” and will roll to one side.

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First of all its like an airplane, on high speed forward motion and lower descent speed.

Second option its called high flaring landing, where vertical landing condition its almost achieved, descending without forward speed at all when landing on the headwind direction.

D. Safety This is the most important part•

Turn motors off and un plugg battery connections

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Everybody should be at least 5 meter away from the aircraft during prerotation.

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No go flying criteria when the wind speed its over 9 knots. Always measured before flight by an air speed meter

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Autopilot Switch off/on radio

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Only mechanics and pilot can touch autogyro on the runway. To do so, the pilot always have to be acknowledge

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To pre-rotate, the main rotor should be on 0º.

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The gyrocopter must prer-rotate in the platform

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Main rotor should be aligned.

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Autogyro shouldn’t start any turning maneuver before reaching 10 meters height.

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Further than 30 meters, pilot should use the camera on board and on screen display to follow the flight.

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Use leds when the day light is going down

Focusing on safety again, we decided that even in the navigation processor, there are certain tasks that need to be specially reliable. That’s why we decided to use dedicated hardware for RC signal encoding and signal loss detection, so that in case of a reset of the main processor or in the event of navigation software hanging, the human pilot would still be able to recover the aircraft.

Being the gyrocopter an unusual type of aircraft, we decided it would be crucial to find a flexible solution for it’s autopilot. One that would give us ease of iteration and customization. And it also had to fit in our budget. For these reasons, we decided to go for open source projects, preferably with big user communities. As a result, our aircraft features a Raspberry Pi for high level processing and computer vision and an Ardupilot mega board for autopilot implementation and navigation system. However, since gyrocopter aren’t supported in ardupilot code, we wrote the whole software from scratch to fit our exact needs and extract the most power out of the hardware.

Gyrocopter with night leds

IV. ELECTRONICS AND AUTOPILOT

There are basically two task that the UAV must accomplish. One is to complete the mission objectives and retrieve all possible information from targets and the other is to keep flying and navigate.

For safety reasons, we decided that the most reasonable approach was to physically separate this two parallel tasks into different processors. This is the key decision that led us to our final systems design.

A. Links and Frequencies In the table below we saw which links and frequencies we use in order to command and communicate with the gyrocopter.

Link Frequency 3DR Telemetry

915 MHz

FPV

5,8 GHz

Futaba transmitter

2,4 GHz

B. Method of Autonomy: While flying autonomous, the aircraft tries to follow a path compound by various waypoints. Waypoints can be modified on the fly from the ground station in case that's needed, or you can command the aircraft to get back home. Once it runs out of waypoints, it will simply go back home and wait for the pilot to take over control and land.

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C. Ground Station: On this we decided to go minimalistic. We chose the simplest approach and the most lightweight ground station posible. Once again, we took the open-source road and built our system under processing. This allowed us to iterate over and over on our framework and even design custom tools on the go for quick data analysis. Further, you can even connect to the aircraft using a simple remote terminal that we provide to maintain the highest flexibility on the system, making it possible to port remote aircraft communication and control to virtually any platform.

D. Electronics Safety Our safety systems design is based on two lines of action.

On one hand we have monitored the radio systems to detect issues with connection, both on telemetry and on manual RC. In the event of signal loss for a 30 seconds or command, the aircraft gives up its current mission and goes back to the launch waypoint autonomously. On the other hand, we have a separate ppm encoder processor permanently scanning the input from RC system to ensure the pilot can safely switch to manual mode as fast as possible, independently of main processor's performance.

This provides a safe environment to test flight where we can always step back to manual control and prevent autopilot malfunction.

Besides, we have designed an extra safety system, strongly based on the advantages of gyroplanes. If battery drainage occurs during flight, gyroplane will cut the main engine and set the rotor into safe landing mode where autorrotation allows it to behave similarly to a parachute, and helps us land in a smoother way. Same action will be activated if the signal is lost for more than 3 minutes or command.

V. MODELING, SIMULATION AND FLIGHT TESTING OF AN AUTONOMOUS GYROCOPTER The goal of this section is to write the design and model of an gyrocopter in order to do the controller to the autonomous flight We will concentrate in the longitudinal dynamic and the aerodynamic study of the rotor.

= Angle of beating of the rotor = Tc c = Ut Up T= B1 A1

Advance angle of the rotor = Characteristic Thrust of the rotor blade chord = Tangential speed of the blade = Normal speed of the blade Thrust of the rotor = Coef. Lateral beating = Coef. Longitudinal beating

The aerodynamic parameters which define the behavior of the rotor are: I. Coef. Lift Cl= a* II. Coef. Drag Cd= III. Advance parameter

IV. Resistance parameter V.Induced speed VI. Induced Model

The model of blade element for beating of the see-saw rotor is: VII. Equation of beating

VIII. Tangential speed of the blade Ut= ( +(u-qhR) IX. Normal Speed of the blade Up=

(

u

X. Rotor Lift

MODELING

T= Nomenclature

a = pendent of lift blade profile = Angle of attack of the rotor Cd = coef aerodynamic drag Cl = coef. Aerodynamic lift = Incuded speed of the rotor

XI.

Rotor Drag

H Dynamic equations:

= Angular speed of the rotor V = Advance speed of the rotor

XII. Vertical component

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=Tcos(i)-Hsin(i)

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XIII. Horizontal component XIV.

=Tsin(i)-Hcos(i)

ROC, CAS, Vx, Vy, Vz, Longitudinal loop CAS Control Power of the Engine controls CAS PID for control CAS

Tail Lift:

XV. Engine thrust T= XVI.

(

Characteristic Thrust

uur M=

)

uur d uur Mi = H dt i XVII. ur ur ur d ur F = m V + mω × V dt

∑

XVIII. XIX. XX. XXI. Fx = m (U + CW - RV) XXII. Fz = m (W + PV - QU) XXIII. Fy = m (V + RU - PW)

CAS control simulation: Kp= 75 Kd = 10 Ki =21 CAS reference 15 m/s

The equations 1-20 give us a dynamic model of the autogyro, with the 6 degrees of freedom equations.

They are implemented in Matlab/Simulink in order to test in open loop and the control loops

PID pitch Control Angle of rotor controls pitch of Gyrocopter

Rotor Model illustration MATLAB Simulink

CONTROL We use a PID controller providing feedback of one of each control parameters using the measures of the sensors.

Control Imputs Directional and Lateral Loop We implement two independent controlo loops and decoupled in order to control the stability directional and lateral

Power of the = Angle of Attack of the rotor = Seat angle of the rotor = Angle of the rudder = Outputs

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B. Control With only 45 grams, the incredibly popular hardware Raspberry Pi is in charge of the camera handling. It runs a version of Raspbian with some kernel tweaks to perfectly work in conjunction with gphoto2. Pictures are taken periodically and processed afterwards. When a target is uniquely found and identified, the results are sent to the GSC so that it can render them, for this Raspberry Pi communicates with Ardupilot, which handles all UAV's communications.

C. Target detection

VI. ARTIFICIAL VISION The Machine Vision has been developed using Python, which we believe is a great programming language to tackle this problem. The target detection and recognition is carried out using a custom devised software solution that runs in a piggyback Raspberry Pi in communication with Ardupilot.

For this purpose, we've avoided reinventing the wheel, using the bleeding edge Open Source machine vision framework SimpleCV, which relies on libraries like OpenCV, SciPy, NumPy, Orange or PIL for doing the hard work. In a nutshell, SimpleCV provides a friendly high-level API for doing artificial vision and artificial intelligence, without leaving out the chance to customize behavior or performance when needed.

For the target localization, we start calculating the color distance of the image taken with respect to a set of reference colors. These resulting images are noised filtered. Then we look for blobs of certain sizes, depending on the flight parameters (such as the altitude or the current zooming). Sometimes the blobs found may conform to a bigger blob, but due to image quality or the letter within the target splitting the shape, they are recognized apart. To avoid this artifact, a merging of nearby blobs is performed.

The section of the image that contains this blob is cropped for target detection. The color of this blob is easily deduced from the color distance where it has been found.

Exemplified now is the process with a test image

While developing the computer vision system, we made a set of real scale targets similar to targets seen in previous editions of the competition. These were used to refine the algorithm and calibrate the process for best results.

A. Camera Pictures are taken using a Nikon Coolpix s3300. This specific camera model was selected due to several reasons: - It's a lightweight compact camera, weighing only 128 grams.

- It's capable of taking pictures with a maximum resolution of 4608 x 3456 pixels (16 Megapixels).

- Its CCD sensor provides a good shutter response.

- Great maximum light sensitivity with 3200 ISO. - 6x optical zoom.

processed: Test image taken.

Last but not least, this camera is fully compatible with libgphoto2, which means that a Linux box can control it using gphoto2 tool. Therefore, the camera can be controlled programmatically: take pictures, download them from the camera, changing camera settings like shutter speed, aperture, exposure, white balance, ISO and others.

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Classification trees are part of supervised learning, which means the system needs to be trained. For this purpose, a training set has been designed so that the decision tree is aware of the following shapes: triangle, square, rectangle, trapezoid, pentagon, hexagon, circle, semicircle, quarter of a circle, star, cross and rhombus.

For every shape, an equal number of images has been used, striving and reaching a 100% correctness in training.

Once ready, a testing set was gathered using different images from the Internet and cropped images from real flight tests. With this this real-world testing set, we got a 73% success rate. In order to improve these results, a custom feature extractor, subclassing the previous one, was developed adding the following features:

Color distance to pink color, in which we can see that the pink trapezoid is seen in white color, target is detected.

* Number of contours using the Ramer–Douglas–Peucker algorithm. * Rectangle distance.

With the extra features, the generated decision tree changed and success rate raised to a 100% in a 100 sample dataset. It’s important to note that this doesn't mean the recognition system is perfect, only that the system gets a perfect score against our test dataset.

Continuing the previous example, the target recognition works like this:

Finally we crop the image that contains the blob found.

This is the one that will be used from now on.

D. Target recognition Once the image is sliced, the amount of pixels to be processed is significantly reduced. This is extremely important to be able to have an effective target recognition because this operation is more CPU-intensive than the target detection.

Target recognition is done using a decision tree, a decision support tool frequently used in Machine Learning. This decision tree will be fed with data (features) obtained from a feature extractor.

We've used MorphologicalFeatureExtractor that is part of SimpleCV. By default this extractor uses the following features: * Area over perimeter * Aspect Ratio * 7 Hu's invariant moments These features provide a robust dataset invariant under translation, changes in scale and rotation.

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This picture shows the blob found colored in red to demonstrate program functioning. This shape is passed to the decission tree.

E. Letter recognition Currently, the letter recognition system is being developed and we are not sure if it will be ready for the competition.

The approaches discussed for solving the problem are: - Using an OCR system: passing it a cropped image of the letter, using Tesseract OCR which comes bundled in SimpleCV. An alternative to this approach would be trying to generate an ideal shape using OpenCV from the letter and passing this representation to the OCR. - Using a machine learning approach: since the set of letters is very specific (always uppercase and following a

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common pattern), a trained system for a subset problem could perform better.

The color of the letter will be calculated using the mean color of the pixels that conform the blob.

The final results are stored in Raspberry Pi and communicated to the Ardupilot, so that it can broadcast them to the GSC. Storing results in the Raspberry allows analysis in test flight crashes or access partial results in case of running out of power.

Following the example: VII. ACKNOWLEDGE

The letter is detected in this case doing a color distance to the other color present within the blob’s area, in this case, blue. Blob found is colored in red.

We would like to thank all the people, companies and institutions that believed in us and made this dream possible, specially to Sertec, Escuela Técnica Superior de Ingenieros Navales, Universidad Politécnica de Madrid and SENASA. To our advisors, José Joaquín Vila who helped us with his incredible mind, his hands and his will, Alberto Polo, who was commited to help and participate and Erno Peter, who has and will be always there.

To the persons who were involved in the project like Jose Maria Ortega, Gorka Marcos. To the personnel of the Technical School of Naval Engineering who helped us so much.

To the team of the “Isaac Peral y Caballero” project who support us with their knowledge, tools and energy.

F. Target positioning and communication We need to calculate where the target is located. For this the Raspberry communicates with the Ardupilot, using the serial port requesting the following flight parameters: GPS position, orientation, altitude, roll and pitch.

The GPS position given by Ardupilot represents the UAV's position. Ideally, without roll and pitch, this position would be in the center of the image. However, when roll or pitch are present, they offset the GPS position within the picture.

Thus, we need to take this into account to pinpoint the real position of the target.

Using the altitude, the field of view of the camera and the current zoom, we calculate the size of the area covered by the picture. This is used to calculate the distance in meters to the target, which finally is turned into a GPS position using a projection algorithm.

G. Avoiding duplicates While the camera is taking pictures, instead of using an Optic Flow strategy we use a simpler system: using the data extracted from previous analysis, some little logic is used for differentiating if we are seeing a duplicate target or not.

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Breingan, Randy. Rommey, Jefferson. Iglesias, Santiago. Shaler, Will “Team UARE: The AnDrone” http://www.auvsiseafarer.org/documents/2012Documents/journals/UAR E2012_JournalPaper.pdf Wilson, Mark. Tenpenny, Coby. Walter, Colby.

“Kansas State University Salina, UAS Club” http://www.auvsiseafarer.org/documents/2012Documents/journals/KsusJ ournalPaper.pdf Leonard-Godbout, Sebastien. Labrie, Justin. EzeauTremblay, Antoine. R-Turgeon, Vicent. Bouchard, Jonathan. Demers, Eric. Lemaire, William. Kirouac, Simon-William. Huot, Julien. Asselin, Pierluc.

“VAMUDES: Autonomous Aerial Vehicle” http://www.auvsiseafarer.org/documents/2012Documents/journals/VAM UdeSAuvsiJournalPaper.pdf

VIII. REFERENCES AND BIBLIOGRAPHY • • •

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2013 SUAS Competition Rules http://www.auvsiseafarer.org/documents/2013Documents/SUAS2013Co mpetitionRulesFinalV5Updated2012-10-31.pdf Maffia, Edgardo. “La Biblia de los Autogiros” Thomsom, Douglas. Houston, Stewart. “Advances in the Understanding of Autogyro Flight Dynamics” http://eprints.gla.ac.uk/4962/1/Advances_in_the..._Fligh t_Dynamics.pdf UK Civil Aviation Authority “The aerodynamics of gyroplanes” http://www.caa.co.uk/docs/33/Paper2009_02red.pdf “Comparison between gyroplane, helicopter and fixed wing aircrafts” http://www.phenix.aero/PHE-1210.html Beaty, C. “Gyroplane Thrustlines vs. Center of Gravity” http://www.rotaryforum.com/forum/showthread.php? t=13060 Cuerva, A. Espino, J.L. Lopez, O. Meseguer, J. Sanz, A. “Teoría de los Helicópteros”

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