Proceedings of the ASME 2011 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2011 August 29-31, 2011, Washington, DC, USA



Long Di and YangQuan Chen Center for Self-Organizing and Intelligent Systems Department of Electrical & Computer Engineering Utah State University Logan, Utah 84322 [email protected], [email protected]

ABSTRACT Radio control (RC) aircrafts have been favorite toys of aviation hobbyists for years. Because of their simple configurations and low expense, they can also be used for reconnaissance and surveillance with information-gathering devices under commands of a skillful human pilot. However, control with human in the loop not only degrades the reliability of the flight performance, but also bring restrictions in endurance and accuracy. In order to resolve these issues and extend the usage of RC aircrafts, getting them capable of autonomous navigation is a preferred solution. This paper reports our approach by designing and integrating an autonomous system on a regular RC aircraft to achieve full autonomy while keeping the additional costs almost equivalent to the cheap RC platform. The current platform will be briefly presented, the system architecture and major components will be introduced, and detailed autonomous demonstration flight results will be provided at the end.

namic research, etc. With an experienced RC pilot, they can even be used for reconnaissance and surveillance purposes. Although RC aircrafts have potentials in many areas, their reliability and many other aspects are affected by the fact that there are always humans in the control loop. When the RC aircraft is far away from the pilot, it is difficult for the pilot to identify the instantaneous attitudes and altitude. Therefore, the aircraft has to always stay in a certain range where the pilot’s line of sight can reach. When the RC aircraft is flying, there is no feedback to the pilot, such as when the fuel will be drained, how well the actuators perform, etc, which are all based on pilot’s accumulated experience. The most critical drawback of RC aircrafts is their fail-safe features. If any component malfunctions and jeopardizes the safety of the aircraft, only the pilot can save the situation and prohibit the plane from getting damage. If the aircraft crashes in an open area, it causes more challenges for people to retrieve it because there is no feedback about the GPS position available.

Keywords: low-cost inertial sensors, attitude heading reference system, autopilot, unmanned aerial vehicle.

For the purposes of resolving the drawbacks and extending the usage of RC aircrafts, converting them into unmanned aerial vehicles (UAVs) by installing navigation and communication units is a reasonable approach. However, most autonomous navigation units available in the current market are not really applicable to cheap RC platforms because of their higher costs [1]. Therefore, designing and integrating an autonomous system on an RC aircraft that can both improve its autonomy and maintain the overall cost as low as possible becomes the preferred solution. Many other researchers have made efforts towards this

INTRODUCTION RC aircrafts are great toys because they are inexpensive to obtain, easy to assemble and joyful to control. RC aircrafts not only bring the hobbyist similar experience as flying real airplanes, but also can be used in many applications, such as stunt flight show, flying targets for military shooting training, aerody1

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space, which is able to arrange and encapsulate all the avionics and various payloads. However, it usually needs a runway to take off and land, and the gas engine also generates pollution and causes potential safety issues. RC sailplanes are also known as RC gliders, but unlike other RC aircrafts, they do not require propulsion from motors. Based on the lift generated from wind blowing and warm rising air, they are able to perform sustainable flight. They can be produced using different materials, such as wood, foam and plastic. Due to its speciality, RC sailplane has a long and narrow fuselage that causes inconvenience for avionics arrangement, and weight is a critical concern that prohibits the glider to carry a variety of payloads. Most important of all, the flight performance of RC sailplanes is closely related to the weather conditions, which introduces many uncertainties. Therefore, RC sailplanes are not an appropriate platform for UAV development. RC rotary aircrafts have different configurations, aerodynamics and control settings in comparison with RC fixed-wing aircrafts. Instead of using control surfaces such as rudder and elevator to manipulate the attitudes, rotary aircrafts only rely on rotors to achieve fully flight control. RC rotary aircrafts can utilize various power sources such as electrical batteries, nitro, gas, etc. The advantage of using this type of design for UAV development is their ability to hover, take off and land vertically, and move in tiny steps, so they can not only be applied to outdoor use but also indoor applications. The drawback of this RC platform is that it is more fragile than other designs, and both endurance and stability are also critical issues that affect its performance. RC flying-wing aircraft is a special fixed-wing design without tail and there is no obvious division between the fuselage and the wings. Since it does not have a tail, there are no rudder and elevators. Its roll and pitch angle controls are achieved through the elevons, which is a combination of elevator and ailerons. Compared with traditional RC aircrafts, it introduces less drag and consequently has higher fuel efficiency [5]. However, it is inherently more difficult to control and less manoeuvrable because of the tailless configuration. Most RC flying-wing aircrafts are made of foam and they generally use electrical motors and batteries as power source. After comparing all four types of RC aircrafts, the flying-wing design seems to have the most potential to satisfy the requirements of low-cost UAV development.

direction, such as [2], [3], [4]. The system integrations generally involve both aerospace and electrical expertise, and if people just purchase off-the-shelf autopilot and avionics systems, they are usually not only expensive but also not open-source, which means it is impossible for the researchers to test their own algorithms or implement new functions. If people decide not to purchase off-the-shelf RC aircrafts but build their own RC testbed, the whole design process will involve considerations in aerodynamics and stability, and before equipping it with all the avionics and payloads, it has to prove its capability of stable RC flights, which will bring extra cost and testing time. In order to achieve the goal stated above, this paper will show our systematic approaches on developing a low-cost RC aircraft based UAV system, focusing on the RC platform selection, system integration, surveys on additional alternative hardware and flight performance analysis. The main objective of this paper is to provide other UAV researchers or UAV practitioners a possible solution scheme on low-cost UAV development for research and civilian uses. The main contents of the paper are organized as follows: Section 2 provides our evaluations on the selection of an RC aircraft. In Section 3, our UAV platform and airborne system are introduced by focusing on the major components and then giving descriptions on the current system architecture and flight control tunings. A survey on other alternative low-cost navigation solutions is presented in Section 4. After that, detail flight results are shown in Section 5. Finally Section 6 concludes this paper and gives future improvement suggestions.

PLATFORM SELECTION RC aircrafts carry radio receiver, motor and servos, and they are remotely controlled by a hand-held transmitter. When the joystick position on the transmitter changes, the receiver also correspondingly adjusts the control surfaces so the aircraft can follow the command from the pilot. There are several different types of RC platforms available in the market and they can be divided into following categories. (1) (2) (3) (4)

Fixed-wing with tail aircraft; Flying wing aircraft; Sailplane; Rotary aircraft.

SYSTEM DESCRIPTION Current UAV Platform Description As explained in the previous section, the RC flying wing was chosen to be our UAV development platform. One of the leading RC flying wing manufacturers in the market is Zagi, which produces different configurations of airframes and all radio control required accessories [1]. However, due to cost reasons, we decided to design our own UAV platform based on a raw airframe without any accessories from the Unicorn Wings.

Most RC aircrafts are fixed-wings with tail. These aircrafts have all the conventional control surfaces, such as elevator, rudder and ailerons, so that they can perform close to full-sized airplanes. They also have wide ranges in size, and one common feature for these RC aircraft is that they usually use gas engines to achieve high speed and great maneuverability. This type of RC aircraft is generally made of wood or metal and they often appear in flight shows for RC aviation hobbyists. For the development of a UAV platform, the advantage is its flexibility and commodious internal 2

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FIGURE 1. Flying Wing Raw Airframe.

The airframe is made from two pieces of EPP (Expanded Polypropylene) foam, and we use strapping tape to cover the whole surface so the main body is well protected. When the left and right EPP wings are glued together, the wingspan is about 48 inches (122 cm). Figure 1 shows the raw airframe. After calculating the central gravity point and balancing all the accessories, we can find the near-optimal position for each component. Figure 2 shows the placement of all the components on the raw airframe. Once everything is ready to be installed, we go through

FIGURE 3. 48” UAV Layout.

UAV platform has following positive specialities: (1) Light weight. The main material built of this airframe is foam and tape, and the total weight of the main body is less than 3.5 lb (1.59 kg), which leaves extra capacity for payload weight given its current motor lift. (2) Runway free. The UAV uses a bungee to take off and features belly landing, therefore it does not need a dedicated runway to operate. (3) Durability. The UAV has flown for numerous hours under different weather conditions with nothing to be modified. The material is resilient to temperature changes and because all the cables are embedded into the foam, they get great preservation from wearing out. (4) Safety. The main body of the UAV is soft so it can absorb most impact and protect the on-board avionics that are secured inside the wing. Because of its small size and weight, and since it uses electrical power, there is little risk of injuring people or damaging properties. (5) Flexibility. The foam structure makes it easy to cut and create spaces for supplementary batteries and new payloads. All the avionics can be moved around to create a better aerodynamics for the airframe. (6) Open-source solution. The autopilot and navigation units are all based on open-source projects, and with support of the community, people share ideas and resolve others’ questions, so it becomes more convenient and solid for our project development. Other researchers can also get easy access to the resources and improve their own designs. (7) Low cost. Based on the open-source software and hardware, it reduces significant amount of investment on the basic exploitation. All the on-board components are selected regarding price and performance to achieve the desired low-cost scenario. The component costs are summarized in Table 2 with a separation of the RC units and automation units.

FIGURE 2. Component Placement.

a detailed self-made construction manual and place all the flying accessories such as electrical motor and servos, autopilot, RC receiver, wireless communication unit and navigation unit in the airframe. A finished 48” flying-wing UAV that is ready for autonomous navigation is shown in Figure 3 and the specification for the 48” UAV is listed in the table below. TABLE 1. 48” UAV Testbed Specification. Weight 3.3lb or 1.5kg Endurance Capability 45 minutes Cruise Speed 15 m/s Control Inputs Elevons & throttle Operational Range 5 miles Operating Battery Voltage 10.5V-12.5V Operating Temperature 14 -104◦ K

Paparazzi Autopilot and GCS The autopilot is the brain of a UAV. It plays an essential role in autonomous navigation by collecting and processing sensor

Compared with other UAV systems, this 48” flying-wing 3

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TABLE 2. UAV COST TABLE. Component Cost(USD) RC Units Airframe 69.95 Motor Controller 41.99 Electrical Motor 79.90 Servos 43.98 RC Receiver 29.95 Batteries 218.97 Total 484.74 Automation Units Autopilot 125 GPS receiver 87.90 Modem 179 IMU 99.99 Total 491.89

FIGURE 4. Paparazzi TWOG Autopilot.

tential of this autopilot. A sample TWOG is shown in Figure 4. Although the Paparazzi autopilot can function independently from the ground control station (GCS), the received sensor data can be interpreted by the autopilot and transmitted to the GCS through a wireless communication device. The Paparazzi GCS software is part of the Paparazzi open-source project and is one of the most powerful tools available. Its concise graphic user interface (GUI) offers an incredible simplicity to monitor and control the UAV. When new commands are sent, it will display them in the console box and generate voice messages to notify the operator. It also shows the UAV status in great detail, such as the ground speed, battery voltage, throttle percentage, flight mode, communication quality and so on. The 2D map window of the GCS is another important segment. It displays the predefined waypoints in the flight plan and highlights the path of the UAV. The flight plan can be modified to meet new mission requirements and the UAV can automatically terminate its mission if an unforeseen problem occurs. The Paparazzi GCS is granted with a higher level of control authority to guarantee its effectiveness. By collaborating with the on-board autopilot, the GCS can guide the UAV to accomplish diverse flight tasks.

TABLE 3. AUTOPILOT COMPARISON. Micropilot Cloud Cap Procerus Paparazzi MP2028 Piccolo SL Kestrel V2.4 TWOG Cost(k USD) 5 N/A 5 0.125 Size(cm) 10x4x1.5 13x5.9x1.9 5.1x3.5x1.2 4x3x0.95 Weight(g) 28 110 17 8 CPU 3MIPS 40MHz 29MHz 32-Bit ARM7 Vin(volts) 4.2-26 5-30 -0.3-16.5 6.1-18 Power 140mA 4w 500mA N/A (6.5V) (3.3 or 5V) Memory N/A 448KB 512KB 32KB Autopilot

data then generating commands to the actuators for correct guidance of the UAV. In order to choose a suitable autopilot that satisfies our requirements, several available commercial-off-the-shelf (COTS) autopilots [6], [7], [8], [9] are surveyed and compared in Table 3. Table 3 indicates that Procerus Kestrel and Micropilot MP2028 are small, light-weight and powerful autopilot choices. However, they are both closed-source and their prices exceed our cost range. Closed-source means we are only able to manipulate the standard functions and the internal software is inaccessible, which will prevent us from implementing new flight control algorithms and integrating other hardware. The Paparazzi TWOG is an open source autopilot including complete software support. The open source settings make it a flexible, effective and inexpensive solution for low-cost UAV platform development [10]. They also reserve options for other hardware to be integrated into the system so that more functions can be activated. The same autopilot has been used on many other platforms and long hours of successful autonomous flights have proven its robustness [11]. Moreover, an advanced flight controller has been designed and implemented based on the same software and hardware [12]. The accomplished test results also demonstrate the capability and po-

Low-Cost Navigation Unit In order to achieve reasonable navigation performance, attitude estimation with high fidelity is indispensable. Accurate orientation measurement is crucial for the flight controller to stabilize the whole UAV system and ensure smooth autonomous flight performance. Inertial measurement units (IMUs) are popular electronic devices on UAVs and they play an important role in attitude estimation because of their high accuracy. GPS is another important navigation sensor because it can measure position, altitude, velocity and course angles of an UAV. By combining IMU and GPS, it can provide most essential data for UAV autonomous navigation. However, most commercial IMUs are quite expensive due to the high quality hardware and sophisticated algorithms. For the purpose of balancing performance and cost, we decided to research on low-cost sensor solutions. With the development of low-cost inertial and GPS sensors, there have been several inexpensive IMUs and GPS available in the current market. Relying on less sophisticated algorithms, they work similarly to the expensive commercial sensing systems 4

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while the price is less than 200 US dollars [13]. Even though their accuracy is incomparable to the commercial ones, they are sufficient for low-cost UAV development. One of the low-cost navigation units combines Ardu IMU and uBlox GPS. Ardu IMU was originally introduced by DIYDRONE and it prices at only 100 US dollars [13]. It consists of a 3-axis accelerometer which is used to measure linear accelerations and a 3-axis gyroscope that is used to measure the angular velocity. The processor is Arduino-compatible that runs the filtering and parsing code. Figure 5 shows a sample Ardu IMU. In order to estimate the orientation angles, a direction cosine matrix (DCM) complementary filter is implemented [14] and it can output attitude estimates with a frequency of around 50Hz. The uBlox GPS receiver is a popular solution for navigation because it is inexpensive and powerful. It can update up to 4Hz and it has been integrated into the Ardu IMU.

FIGURE 6. System Block Diagram.

(a) Ardu IMU Parsing through Gumstix.

(b) Ardu IMU Direct Parsing.

FIGURE 7. Two Configurations of Ardu IMU.

System Integration and Control Tunings The integration of the current platform has been completed by combining the open-source Paparazzi autopilot and the Ardu IMU. The current communication units include one 72MHz RC receiver for the safety link and a 900MHz wireless modem for the datalink. The modem is able to handle up to 40 miles [15] and we usually limit the flight area within 1 mile due to legal reasons. If the datalink has been lost for 30 seconds, the UAV will return to the base station and circle around it. Then the safety pilot can take over the control. Besides, a differential air pressure sensor that can measure airspeed and provide feedback for closed-loop speed control has also been designed and implemented for the same system using the ADC port. A system block diagram is shown in Figure 6. The Ardu IMU is designed to accept GPS information for yaw drift correction and direct parsing all the navigation data to the TWOG autopilot through its UART port. In order to quantify the performance of Ardu IMU, a logging system based on Gumstix Verdex microcomputer has also been designed so that the entire inertial sensor and GPS data can be saved into a SD card on the microcomputer for further data analysis. Gumstix Verdex can be also used to parse the navigation data to the autopilot while that is an optional setting. The two configurations for Ardu IMU are shown in Figure 7. In order to utilize the sensor data from Ardu IMU, both airborne code and IMU parsing code follow the same Ugear format, which was cre-

ated for the AggieAir platform [16]. Using this protocol, there is no need to modify the Paparazzi airborne code, so any IMU that wants to communicate with the autopilot just needs to convert its sensor outputs into a standard Ugear format, and then the UAV can perform autonomous navigation based on that IMU. By doing this way, the software is fully compatible with other commercial IMUs owned by CSOIS. In order to evaluate the performance of the Ardu IMU, we also compared it with Microstrain GX2, a commercial IMU priced around 1700 USD and it is stated that the dynamic accuracy is about ±2o [17]. After several flight tests by purely using the Gumstix logging function, the raw sensor data of both IMUs was compared and it could be observed that the low-cost sensors, especially the accelerators did have deficiency under vibration conditions, which adversely affected the IMU accuracy. While working on the software developing and modification, it was also noticed that the Arduino software timer was not completely punctual although most of the time it stayed at 50Hz, which led to occasional incorrect sensor outputs. Some thorough ground testings have also been finished and find that the estimation accuracy of the Ardu IMU is close to GX2. In order to resolve the motor noise issue, a specially designed mounting 5

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Some typical low-cost autopilots and IMUs available in the market are selected and introduced to people who are interested. ArduPilot is an autopilot developed by DIYDRONE. It prices at 150 USD and is fully compatible with Ardu IMU [19]. It is also open-source for both hardware and software. People have achieved autonomous flight experience using this autopilot. Moreover, DIYDRONE provides many other sensor packages with software ready, so they can be easily integrated with the Ardupilot to fulfill more functions. The ground station software of this autopilot is not as good as Paparazzi, and its on-board processor is insufficient for future airborne code development. Paparazzi also has other autopilot options, such as the Tiny series for fixed-wing UAVs, Lisa and Booz for quadrotor UAVs [20]. They are all developed by the community and serve different usages. Open-source and low-cost are common features of all the autopilots. Sparkfun has made several different low-cost IMUs similar to the Ardu IMU. Razor IMU is a typical one. It prices at 125 USD and consists of gyros, accelerators and magnetometers [21]. It has an ATmega328 processor and shares similar open-source software as the Ardu IMU. Unlike its counterpart, it does not provide a GPS port. Therefore, it is more beneficial to purely utilize its attitude sensor data. CHIMU is another typical low-cost IMU. It is made by a company called Ryan Mechatronics and prices at 299 USD for the basic version and 349 USD for a temperature calibrated version [22]. Unlike the two IMUs introduced before, it is actually an attitude heading reference system (AHRS) because of its powerful on-board processing system. The company has also designed a delicate GUI to manipulate the IMU with many additional options. However, this IMU is closed-source so it causes more difficulties with integrating it with other autopilots. Due to the price and closed-source aspects, there have not been many feedbacks from users using CHIMU. VectorNav VN-100 is probably the most expensive IMU that still can be considered as a low-cost IMU. It prices at 500 USD and has additional costs for the development kit. Similar to CHIMU, it is also an AHRS combining a 3-axis accelerometer, 3-axis gyroscope, 3-axis magnetometer, and a 32-bit processor which runs an extend Kalman filter (EKF) with 200Hz update rate [23]. So far it has been quantified regarding the accuracies, which is statically < 1o for heading and < 0.5o for roll and pitch angles.

which not only tightly holds the IMU but also absorbs the vibration is installed on the 48” UAVs so that the Ardu IMU can send steady navigation senor data to the autopilot. The Paparazzi autopilot integrates two sections for the high level flight controls, which are the navigation loop and altitude loop [10]. The navigation loop consists of the roll channel control and the altitude loop consists of the pitch channel control and the throttle control. It uses simple PID controllers for the low level controls, and specifically, a proportional controller is designed for the roll channel while a proportional and differential controller is adopted for the pitch channel. The current speed loop is using an open-loop controller and a closed loop design is undergoing. In order to tune a newly built aircraft for autonomous navigation, we follow a simple basic procedure: (1) Launch the UAV manually using our bungee. During the RC control, the safety pilot needs to trim the elevons so the UAV can fly steadily under manual mode. (2) Switch the control mode to semi-autonomous (Auto1), so the control is under augmented stability. With Auto1, the safety pilot can set the throttle to the cruising value defined in the airframe file and check if the UAV is able to stay at the same altitude. (3) Depending on the performance under Auto1, the GCS operator needs to tune the roll and pitch loop neutrals so the UAV can fly straight and flat when the safety pilot has no control on the UAV except the throttle. (4) After the semi-autonomous tuning, switch the control mode to fully autonomous (Auto2). We can first increase the roll P gain until oscillation on the roll channel happens, and then reduce it so we can find the closest gain value for the best roll control performance. Afterwards, we can follow the same method to tune the pitch channel. (5) When the UAV is flying in Auto2, we have designed several flight routines such as circling left and right, flying straight to refine the roll and pitch neutral value. Meanwhile, we also slightly tune the course control P gain so we can obtain the near optimal heading performance. Once this standard procedure is finished, we change all the relevant parameters in the airframe file and load the modified files into the autopilot. Then UAV is ready to perform satisfactory autonomous flight. More details regarding the tuning and calibrations can be found in [18]. After explicit studies on the characteristics of the Ardu IMU and successfully integrating it with the Paparazzi TWOG autopilot, we have had extensive flight tests and test results have proved the steadiness of the new system.

EXPERIMENTAL RESULTS Many flight tests have been performed and here we show a series of flight test results collected in the Cache Junction research farm belonging to Utah State University. Both IMU and GPS sensor data were saved through Paparazzi’s logging function. From Figure 8 to Figure 12, we show the roll angle tracking errors, pitch angle tracking errors, altitude tracking, course angle

LOW-COST NAVIGATION SOLUTION SURVEY Paparazzi TWOG autopilot and Ardu IMU represent the most popular navigation solution for low-cost UAV development. 6

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20 10 15

Pitch Tracking Error(m)

Roll Tracking Error(m)

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FIGURE 8. Roll Angle Tracking.



1000 Time(s)




FIGURE 9. Pitch Angle Tracking.

tracking and flight path, respectively. The results are all highlighted for the autonomous flight mode so that they can show the comprehensive performance of this system. From Figure 8 and 9, it can be observed that the roll channel tracks pretty well so that the UAV has consistent circling performance. The pitch channel is not as well as the roll channel due to the flying-wing design, but most of the time the UAV has sufficient ascending and descending performance. Shown from Figure 10, its altitude maintains close to the reference with small oscillation due to wind disturbance. Figure 11 shows its actual course tracking given the reference course from GPS and they are pretty close to each other. The last figure shows the smooth autonomous flight path, which includes standby circling, line tracking and circling, and autonomous landing. The autonomous landing is achieved through several functions in the flight plan. Basically the UAV first circles down to an altitude of 50m based on the GPS estimation, then it flies towards a touchdown waypoint at the ground altitude with attitude controls and zero throttle. We have also successfully tested autonomous takeoff and it is achieved using a similar concept as the landing. We first find the exact GPS coordinate where the bungee is located, and then we extend the bungee to launch the UAV. When the UAV passes the bungee waypoint, its throttle will be turned and climb to a certain altitude. During this process, its attitude control is also activated so it can confront small cross wind.

Altitude desired

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FIGURE 10. Altitude Tracking.

is also presented for UAV researchers and practitioners that are interested in this area. Comprehensive autonomous flight rests are shown at the end to demonstrate the stability of the proposed platform. Future work includes implementing new filtering algorithms such as EKF for the current IMU, comparing other low-cost IMUs during flight tests and improving the speed control of Paparazzi autopilot by using the air pressure sensor.

ACKNOWLEDGMENT This work is supported in part by the Utah Water Research Laboratory (UWRL) MLF Seed Grant (2006-2011) on ”Development of Inexpensive UAV Capability for High- Resolution Remote Sensing of Land Surface Hydrologic Processes: Evapotranspiration and Soil Moisture.” The authors would like to thank Dr Haiyang Chao, Austin Jensen and Daniel Morgan for the early stage development on this project. The authors also want to thank the people of DIYDRONE for their efforts on developing opensource IMU projects and the Paparazzi UAV community for pro-

CONCLUSION AND FUTURE WORK In this paper, we report our work by designing and integrating a low-cost autonomous navigation system on a flying wing RC aircraft to achieve satisfactory autonomous flight performance. The current UAV platform is first introduced regarding cost and advantages, then we focus on major avionic components and explain the architecture for the integrated system. An evaluation on various RC aircrafts for UAV platform development is provided and a low-cost navigation solution survey 7

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Development of a Low-Cost Test-Bed for Undergraduate Education in UAVs”. In Proc. of 44th IEEE Conference on Decision and Control, and the European Control Conference 2005. Seville, Spain. [5] Bolsunovsky, A., Buzoverya, N., Gurevich, B., Denisov, V., Dunaevsky, A., Shkadov, L., Sonin, O., Udzhuhu, A., and Zhurihin, J., 2001. “Flying wing - problems and decisions”. Aircraft Design, 4, pp. 193–219. [6] MicroPilot MP2028 Autopilot. http://www.

Course desired

350 300


250 200 150 100 [7] Cloud Cap Technology The Piccolo SL autopilot. http://\_sl.shtm. [8] Procerus Technology Kestrel V2.4 autopilot. http://www. php. [9] Paparazzi ”Tiny WithOut Gps” TWOG autopilot. http://\_v1. [10] Brisset, P., Drouin, A., Gorraz, M., Huard, P. S., and Tyler, J., 2006. “The Paparazzi solution”. In Proc. of MAV. Sandestin, Florida. [11] Brisset, P., March, 2010. The Paparazzi UAV System. Quadrotor/PascalBrisset\_InfoIndustrielle\ _Paparazzi.pdf. [12] Chao, H., Luo, Y., Di, L., and Chen, Y. Q., 2010. “Roll-Channel Fractional Order Controller Design for a Small Fixed-Wing Unmanned Aerial Vehicle”. Control Engineering Practice, 18(7), pp. 761–772. [13] DIYDRONE ArduIMU . profiles/blogs/arduimu-now-available. [14] Premerlani, W., and Bizard, P. Direction Cosine Matrix IMU: Theory. DCMDraft2.pdf. [15] Digi Xtend Manual. xtend.pdf. [16] Chao, H., Jensen, A. M., Han, Y., and Chen, Y. Q., 2009. Geoscience and Remote Sensing. IN-TECH, ch. AggieAir: Towards Low-cost Cooperative Multispectral Remote Sensing Using Small Unmanned Aircraft Systems, pp. 467–490. [17] Microstrain 3DM-GX2. 3dm-gx2.aspx. [18] Di, L., Chao, H., and Chen, Y., 2010. “A Two-stage Calibration Method for Low-cost UAV Attitude Estimation Using Infrared Sensor”. In Proc. of 2010 IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, pp. 137–142. Qingdao, China. [19] DIYDRONE Ardupilot. product\_p/br-0013-01.htm. [20] Paparazzi Autopilots. wiki/Autopilots. [21] Sparkfun Razor IMU. products/9623. [22] CHIMU Micro AHRS. http://www.ryanmechatronics. com/index.htm. [23] VectorNav VN-100. index.php.

50 0 400



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FIGURE 11. Course Angle Tracking. 6

x 10 4.63 4.6299



4.6299 utm north(m)

Straight line 4.6298 4.6297 4.6297 Autonomous landing 4.6296 4.6296 Circling 4.6296 4.181



4.184 utm east(m)


4.186 5

x 10

FIGURE 12. Flight Path.

viding help during the project development.

REFERENCES [1] Chao, H., Cao, Y., and Q.Chen, Y., August 2007. “Autopilots for Small Fixed Wing Small Unmanned Air Vehicles: A Survey”. In Proc. of the 2007 IEEE International Conference on Mechatronics and Automation, pp. 3144–3149. Harbin, China. [2] McLain, T. W., and Beard, R. W., June 30-July 2, 2004. “Unmanned Air Vehicle Testbed for Cooperative Control Experiments”. In Proc. of the 2004 American Control Conference. Boston, Massachusetts. [3] Clothier, R., Harrison, A., Dusha, D., McManus, I., Greer, D., and Walker, R., March 13-17, 2005. “Development of a Low-cost UAV System for Civilian Airspace Integration Trials”. In AIAC-11 Eleventh Australian International Aerospace Congress. Melbourne, Australia. [4] Jung, D., Levy, E. J., Zhou, D., Fink, R., Moshe, J., Earl, A., and Tsiotras, P., December 12-15, 2005. “Design and 8

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Aug 29, 2011 - calculating the central gravity point and balancing all the acces- sories, we can find the ..... relevant parameters in the airframe file and load the modified files into the autopilot. ....\_v1. [10] Brisset, P.

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