Session U-08 AN AUTONOMOUS TRACKING …aseegsw.com/past Proceedings/U-08-Tucker.pdf · Within the...

Proceedings of the 2010 ASEE Gulf-Southwest Annual Conference, McNeese State University Copyright © 2010, American Society for Engineering Education

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Session U-08

AN AUTONOMOUS TRACKING SYSTEM INTEGRATING UAV AND UGVS

Christopher Tucker1, Juan Tafoyacabrera1, Christopher Montanez1, Remus Avram1, Matt

Bauer2, Yufang Jin1 1Department of Electrical and Computer Engineering

University of Texas at San Antonio

2Department of Computer Science Arcadia University

Abstract The purpose of our research is to integrate a system composed of an Unmanned Aerial vehicle (UAV) and an unmanned ground vehicle (UGV). The integrated heterogeneous system containing UAV and UGV could be used to scout areas which are not suitable for people to explore, and thus minimizing human risk. In our system, both the UAV and the UGV have a GPS unit to accurately localize the vehicle. The vehicles and the central control computer communicated with each other using a wireless transceiver. Through this wireless communication network and the GPS units, we have successfully coordinated the UAV and the UGV team and guided the UGV to follow the trajectory of the UAV. The novelty of this research lies in the fact that it has achieved the control, communication, and computation of the UAV and UGV, and further, integrated these heterogeneous systems into a real platform. Introduction Over the past decades, a great deal of research has been done in the field of unmanned vehicles that include ground air and sea. The first successful UAV in recorded history dates back to February of 1863 when a young man by the name of Charles Perley patented an unmanned aerial bomber 11. This first UAV was simply a hot-air balloon which could carry a load of explosives and a timing mechanism for detonating. The invention of winged aircraft brought with it a more stable way of experimenting and deploying UAVs. UGVs have also greatly improved since the first major mobile robot was developed in the late 1960s by Stanford Research Institute 10. There is some research currently being done in the area of UAV and UGV hybrid systems such as the “Griffon: a man portable UAV/UGV system”, however this is only proof of concept8. This research is motivated by the lacking of research on the integration of UAVs and UGVs. The aim of this research was to guide a UGV follow a UAV in a dynamic environment. Our previous research focused on trajectory planning of a UGV in a dynamic environment14, 15, cooperation of a UGV team in a dynamic environment16, modeling and control of UAV12, 13, 17, communication network of UGVs and UAVs 15. These researches have set a solid foundation for us to further integrate a heterogeneous platform containing both UAV and UGV. In this research, we have selected a 10 feet blimp as our UAV, Arduino Mega as our UAV controller,

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center, the greater the fan’s speed. All three were controlled with PWM (pulse-width modulation) signals. Sensors:

1) Heading Sensor:The Compass Module HMC6352 was used as the heading sensor for the UAV1. This item will be used in the final control system to determine proper guidance vectors. When placed within the gondola, the magnetic fields already present within the gondola affected the sensor and caused it to produce a different value. This was remedied with different placement on the UAV.

2) Sonar: The MaxSonar-EZ1 was used to provide altitude readings from the gondola to the control program9. By placing the sonar on the bottom of the gondola, the control system can retrieve its current altitude in inches ranging from 6 inches to 254 inches. A noted problem was that if anything obstructed or moved into the sonar’s area at an elevated height, it would produce erroneous altitude values.

3) GPS Module: The GPS module used on the UAV is a U-Blox5 GS4077. Its small form factor and light weight made it an ideal candidate to take part in our project. The module runs on 3.3 V @ 75mA and has a refresh rate of up to 4Hz with 50 satellite tracking channels. Initial testing of the module yielded satisfactory results with a time to fix (TTF) of less than one minute. The module’s main function is to relay positioning information from the UAV to the UGV. Xbee: Wireless Communication The Vehicle was equipped with an XBee6 transceiver module for wireless communication with the UGV. This wireless transceiver served as the principal link between the UAV and UGV. All positioning information coming from the UAV is sent through this link. Major Control Unit of UAV Within the prototype, the Arduino Mega board was chosen as the controller of the UAV system2. This board was chosen due to its ease of programming, multitude of documentation, and memory space. The characteristics of this board can be seen in Table 1. This board will also be sufficient for future progression into this research.

Table 1. Summary of Arduino Mega’s specifications Microcontroller ATmega1280 Operating Voltage 5V Input Voltage (recommended) 7‐12V Input Voltage (limits) 6‐20V Digital I/O Pins 54(of which 14 provide PWM output) Analog Input Pins 16 DC Current per I/O Pin 40 mA DC Current for 3.3V Pin 50 mA Flash Memory 128 KB of which 4 KB used by bootloader SRAM 8 KB EEPROM 4 KB Clock Speed 16 MHz


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Navigation software flowchart of UAV

The navigation system is broken down into several stages. The first stage initializes the settings within the microcontroller. Flowchart of the navigation system is shown in Figure 5. The code was written in a manner that the microcontroller is always listening for incoming commands. Once it detects am incoming command, it sorts it and determines which part of the control system the command belongs to. For example, if the incoming data is the ASCII character T, the code in the microcontroller runs the proper function referring to the thrust. Due to the sensitivity of the heading sensor, a calibration program has been written to minimize affects from external magnetic fields. When this process is initiated the commands are sent to sensor to calibrate it based on its documentation. After this has been completed a reading is taken at the four points: North, East, South, and West and stored in an array labeled headingval[]. From these stored values, the final value is interpolated using this equation:

slope = (float)(90/(headingval[m] ‐ headingval[m‐1])); newval = slope*(current‐headingval[m‐1])+((m‐1)*90);.

The slope of the equation is set by dividing the desired degree span by the actual degree span of one of the four quadrants. The m variable is the number of the quadrant based on a clockwise path, where North is 0 degrees, East is 90 degrees, South is 180 degrees, and West is 270 degrees. The current reading from the heading sensor is denoted as the variable current. The newval variable is the new degree value obtained after using a linear equation based on the previously obtained slope variable and the constant is based on 90 degree increments using the m value. One complication is the location of the transition from 359 degrees to 0 degrees. Whichever quadrant this is located in uses a slightly different equation. Seen below is used if this falls within the first quadrant.

difference = 360 ‐ headingval[0]; denominator = difference + headingval[1]; slope = (float)(90.00/denominator); if(temp<180){ newval = slope*(current)+ slope*(difference);} if(temp>270){ newval = slope*(current ‐ headingval[0]);}

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UGV

The role of our UGV was played by a very resourceful and complex robot, P3-AT, provided by MobileRobots, Inc. P3-AT is a highly versatile all-terrain robotic platform, software-compatible with all MobileRobots robots providing high-performance robotic actions with plenty of real estate for customization. Powerful, yet easy to use; reliable, yet flexible, P3-AT is a popular team performer for outdoor or rough-terrain projects. Four 100-tick encoder motors provide the P3-AT with a generous flexibility, while a 32-bit RISC microprocessor handles all the low-level commands that control the robotic behavior. UGV Hardware MCU The SH7144 series CPU has a RISC-type instruction set. Most instructions can be executed in one state (one system clock cycle), which greatly improves instruction execution speed. In addition, the 32-bit internal-bus architecture enhances data processing power. All high-level commands and governing software was compiled in C++ and made extensive use of the provided APIs and predefined classes. Commands to the P3-AT are sent in packets, with every packet sent every 100ms. Communication Communication with the P3-AT can be realized through a RS323 serial port onboard of the platform. The C++ object-oriented application programming interface, Advanced Robotics Interface for Applications (ARIA), is primarily used to program robots provided by the manufacturer. In particular, ARIA is the robust API which was used to program the Pioneer3-All Terrain (P3-AT) and synchronizes communication between the host computer and UGV. Lantronix Wi-Box(wireless link between PC and P3-AT) The wireless box is connected via a serial port to the P3-AT MCU; it provides a means of connecting the UGV through an Ad-Hoc 802.11b network connection. By connecting to the robot wirelessly, it allows the researcher with a lot more flexibility in programming and

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9

maneuvering the P3-AT. The Wi-Box allows the UGV to be controlled by any computer or laptop configured with a wireless network device. Navigation For navigation the UGV is equipped with a 3-axis compass, or magnetometer, that has the ability to produce the UGV’s current absolute heading. In addition, the compass can also output other useful information such as pitch, roll, and temperature. The main component for UGV navigation is the robots onboard Novatel GPS Unit. The particular Novatel model being used has as an accuracy of less than one meter. The GPS and compass will be used in outdoor situations where the desired objective is for the UGV to be able to follow the UAV. Sonar Sensor In order to enhance the robots behavior a few external devices were added to the platform: Sonar sensors (16 sensors to offer an immediate preview of the surrounding environment) These sensors are radially distributed around the robot and they confer a decent initial scan of the perimeter. However, because of their position on the robot and the different possible refraction angles and the divergence of the sonar reading vectors the sonar sensor array is not best fitted for our purpose. Figure 7 shows the sonar setup at the front of the P3-AT:

Using the Law of sines we can determine the maximal error between any two adjacent readings. i.e.: The front of the robot two readings hold a 1,73 m error between their readings at a distance of 5 meters ahead of the robot.

sin 20 5

sin 80 1.73

LASER sensor(offers a better scanning accuracy, 360 readings / 180 degree span) Unlike the Sonar Sensor Array, the Laser system offers a 180 degree vision, with a reading every 0.5 degrees and a maximal length of vision of 35 meters. Using the same law of sines at 35m ahead of the robot our error is now only 0.3m and 0.043m at 5 meters ahead of the robot, thus providing us with much higher accuracy.

Figure 7 Displaying the front sonar array.


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Various experiments have shown and proved the accuracy of the Laser. In Figure 8 you can observe, how the robot detects the two static objects an their physical values:

Figure 8

UGV Software

As stated, in addition to the hardware by MobileRobots, Inc. application programming interfaces (API) were also provided. In particular the API of interest is Advanced Robotics Interface for Applications (ARIA). This API allows the programmer to easily interface with the P3-AT and program desired responses. The programmed responses can be simple programs, such as programs that send a series of direct movement commands. On the other hand, ARIA has the capability to create fairly complex programs that invoke many actions, or behaviors. A mixture of actions can result in high-level and intelligent behaviors. Due to the two types of environment (indoors vs. outdoors) two different following procedures have been approached. While indoors, a camera mounted on the UGV will keep track of the UAV and by the means of image processing, it would be able to determine the UAV movement and compute its new coordinates. Indoor Navigation As stated in the introduction the goal of the project is for the UGV to follow a specific path, predetermined by a freely moving unmanned aerial vehicle. In order to achieve our goal several navigational steps are required:

− provide P3-AT with enough knowledge about its surrounding environment; − establish communication with UAV; − develop obstacle avoidance and path planning algorithm. Add previous research result

references

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about ±1 meter. Finally, if the conditions were met, the robot was given the command to turn towards the calculated direction of the UAV . In order to make the robot turn towards the desired heading the angle between the current heading and desired heading had to be calculated. The robot's current heading was known by reading the UGV's onboard compass. The change of heading was equivalent to the desired heading less the current heading. The UGV was then given a command to change its heading by the previously calculated value. After this rotational command was complete, then the command was sent to travel the previously calculated distance. This results in continuous following of the UAV if the entire process is repeatedly looped. For this particular application the refresh rates of both the UAV and UGV GPS systems was about one second. For this reason, the entire previously discussed following scheme is updated approximately every second.

System Integration Integration of the UAV and UGV systems as one hybrid platform was made possible by combining all components of the two systems. The MCUs were in charge of controlling the individual parts in each vehicle. The Arduino Mega microcontroller served as the central processing unit for the UAV. Its tasks were to control the servos, get sensor readings and relay information to the UGV. Communication was the link that tied the two systems together. The XBee wireless transceiver modules were used to send and receive data between the two vehicles. During testing, we found out the transceivers were able to communicate at a distance of about 100 meters and provided reliable data delivery. The UAV could be controlled using a computer to send serial ASCII characters through the wireless network. Navigation was one of the crucial elements for the success of the system. The use of GPS allowed for accurate positioning of down to less than two meters for the UAV. The UAV was able to send positioning data down to the UGV. The UGV received the data and, by applying the previously discussed UGV GPS following program, the UGV was successfully able to travel to the UAV’s current position.

Conclusion

The studies of UAV and UGV have been carried for a long time, but their integration is a fairly new idea. This project progresses through the integration of these two heterogeneous systems into one overall system which will have multiple possibilities from communication radius expansion to search and rescue. Found through this project are various methods of accomplishing this task with the use of a certain aircraft and mobile robot along with various components. We set out to create a hybrid system composed of two distinct vehicles. Two very promising tracking methods were presented to us at the beginning of our research. GPS tracking was chosen over object tracking. GPS tracking gave us the advantage of covering greater distances and was easier to interface.


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References

1. Compass Module - HMC6352. Sparkfun Electronics. [Online] http://www.sparkfun.com/commerce/product_info.php?products_id=7915.

2. Arduino Mega. Arduino. [Online] http://arduino.cc/en/Main/ArduinoBoardMega. 3. ActivMedia Robotics, LLC. CMPE 300 ARIA Lab Manual. 2002. 4. MobileRobots, Inc. MobileRobots Advanced Robotics Interface for Applications (ARIA) Developer's

Reference Manual. Amherst : s.n., 2009. 5. Pioneer 3 Operations Manual. Amhurst : s.n., July 3007. 6. XBee/XBee-PRO OEM RF Modules. Lindon : maxtream, 2006. 7. Yen, Chris. GPS module Spec. s.l. : S.P.K. Electronics, 2009. 8. Griffon: A Man-Prtable Hybrid UGV/UAV. Yamauchi, Brian. Burlington : s.n., 2004. 9. LV-MaxSonar-EZ1 High Performance Sonar Range Finder. s.l. : MaxBotix inc., 2007. 10. UGV HISTORY 101: A Brief History of UGV Development Efforts. Gage, Douglas W. 3, s.l. : Unmanned

Systems Magazine, 1995, Vol. 13. 11. SPIES THAT FLY. PBS. [Online] PBS, November 2002. [Cited: January 1, 2009.]

http://www.pbs.org/wgbh/nova/spiesfly/uavs.html 12. Sylvester Meighan, Nick Grady, Chunjiang Qian, "Inertial Measurement Unit of System Identification of

an Unmanned Aerial Vehicle", Research Poster in Society of Mexican American Engineers and Scientists International Symposium, Albuquerque, New Mexico on October 24-27, 2007.

13. R. Jia, M. Frye, and C. Qian, "Flight Control for an Airship System Based on Particle Swarm Optimization Technique", Proceeding of IEEE Multi-conference on Systems and Control, Sep 2008, San Antonio, US.

14. Marcos Bird, Carlos Quiroz, and Yufang Jin, "Lead-follower Control Scheme for Unmanned Ground Vehicles in an Unknown Environmen", Abstracts of IEEE Multi-conference on Systems and Control, Sep 2008, San Antonio, US.

15. Carlos Quioz, Marcos Birs, Chuong Khuc, Yufang Jin, "Integration of Heterogeneous Unmanned Ground Vehicles with Synchronized Communication", Undergraduate Student Technical Paper Competition at ASEE-GSW conference, March 18-20, 2009

16. Siyao Gu, Marcos Bird, Scott Timme, Yufang Jin, "Formation Control of Unmanned Ground Vehicles Using Visual Feedback", Proceeding of ASEE-GSW conference, March 18-20, 2009.

17. Zana Coulibaly, Luis Alonso, Benito Garcia,Hervie Martin, Yufang Jin, "Controller Design and Hardware implementation of airship", Abstract of IEEE Multi-conference on Systems and Control, Sep 2008, San Antonio, US.

CHRISTOPHER TUCKER Chris will receive his Bachelors of Science in Electrical Engineering from the University of Texas at San Antonio at the accumulation of the Spring 2010 semester focusing on control systems. His research interests include design and development of embedded systems and robotics. JUAN TAFOYACABRERA Mr. Tafoyacabrera is currently a senior Electrical Engineering student at the University of Texas in An Antonio. His research interests include control systems for Automated Unmanned Aerial Vehicles. CHRISTOPHER MONTANEZ Christopher received his Bachelors of Science in Electrical Engineering from the University of Texas at San Antonio with a concentration in Computer Engineering. He is currently pursuing his Master of Science Degree in Electrical Engineering at the University of Texas at San Antonio. His research interests include digital system design, embedded systems design and programming, and C++ programming. REMUS AVRAM Remus received a Bachelors of Science in Electrical Engineering from the University of Texas at San Antonio with concentration in Computer Engineering. His interest is in embedded software solutions, digital system design and various electrical design procedures.


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Matt Bauer Matt received a Bachelors of Science in Computer Science from Arcadia University. He is applying for graduate schools to continue this research now. YUFANG JIN Dr. Jin currently serves as an Assistant Professor of Electrical Engineering at the University of Texas at San Antonio. Her research interests include robust adaptive control design for nonlinear systems, vision based control for mobile robots, synchronization and parameter estimation of chaotic systems, and observer design for nonlinear systems.

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