Semi Autonomous Hand Launched Rotary Wing Unmanned Air Vehicles


Published on

Semi Autonomous Hand Launched Rotary Wing Unmanned Air Vehicles

Published in: Education, Technology, Business
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Semi Autonomous Hand Launched Rotary Wing Unmanned Air Vehicles

  1. 1. PI’s : Prof. Lyle Long and Prof. Joseph F. Horn Tel: (814) 865-1172 and (814) 865 6434 Email: and [email_address] Graduate Students: Wei Guo, Ph.D. Candidate Scott Hanford, M.S. Candidate Project PS 8 Semi-Autonomous Hand-Launched Rotary-Wing Unmanned Air Vehicles 2004 RCOE Program Review May 4, 2004
  2. 2. Background / Problem Statement Technical Barriers Small rotary-wing unmanned air vehicles (RUAV) would be useful for many military and civilian applications, especially if they were semi-autonomous and very small. (e.g. “hover and stare” mission) <ul><li>Technical barriers for a hand-held semi-autonomous UAV’s: </li></ul><ul><ul><li>Control, Reliability, Ease of operation, Portability, Low cost, Weight, Power, … </li></ul></ul><ul><li>R/C RUAV’s too difficult to fly, require extensive training </li></ul><ul><li>Semi-autonomous control to minimize training and to allow the operator to divide their attention </li></ul><ul><li>Small, commercially available, low-cost sensors are desired but tend to have lower performance and reliability </li></ul><ul><li>Solution: Apply advanced control design and sensor fusion methods to achieve reliable semi-autonomous operation </li></ul>
  3. 3. Task Objectives: Approaches: Expected Results: <ul><li>Design and build avionics systems for small electric powered quad-rotor RUAV’s </li></ul><ul><li>Incorporate newest low cost / lightweight processors and sensors </li></ul><ul><li>Investigate the use of both commercially packaged avionics systems and custom-designed avionics </li></ul><ul><li>Investigate several types of microprocessors and sensor systems </li></ul><ul><li>Apply advanced controls laws / sensor fusion methods for reliable semi-autonomous control </li></ul><ul><li>Also investigating avionics systems for gas powered RC Helicopters </li></ul>Investigate feasibility of hand launched RUAV that are portable, inexpensive, easy to operate, and capable of performing useful tasks <ul><li>Small COTS-based inexpensive autopilots </li></ul><ul><li>Flight test results and evaluation of the different systems </li></ul>
  4. 4. Motors and Gear Reduction Power, Electronic Controller, Payload Rotors <ul><li>Four variable-speed motors control each rotor individually </li></ul><ul><li>Front and aft rotors rotate opposite direction of left and right </li></ul><ul><li>Four-axis Control: </li></ul><ul><ul><li>Roll – vary relative speed of left and right rotors </li></ul></ul><ul><ul><li>Pitch – vary relative speed of fore and aft rotors </li></ul></ul><ul><ul><li>Collective – vary speed of all rotors simultaneously </li></ul></ul><ul><ul><li>Yaw – vary relative speed of clockwise and counter-clockwise rotors </li></ul></ul><ul><li>Highly maneuverable, minimal cross-coupling, simple control mechanisms </li></ul><ul><li>Low rate damping, requires electronic stability augmentation </li></ul>Control of a Quad-Rotor UAV
  5. 5. Processors and Software We are currently evaluating all of these Others: Motorola, HandyBoard, Atmel,… Very fast, can use c, light weight, inexpensive, … 1000 Basic, C, Java 1000 Yes Yes 128 MB 266 MHz JumpTec ( ) (Intel PC Processor) PC-104 200 Basic, C, Java 1000 Yes Yes 1 GB 1000 MHz VIA MicroATX ( ) VIA 140 Basic 80? No Yes 34 KB 100 KIPS Basic Micro ( (Hitachi 3664) BASIC Atom Pro 89 Java (subset) 60 No No 64 KB 8 KIPS Parallax ( (Ubicom SX48AC) Javelin Stamp Yes Yes Floating Point Math? 100 Java 250 Yes 1 MB 40 MHz Dallas Semiconductor ( TINI Board 100 Basic or C 80 No 1800 KB 40 MIPS MicroChip ( ( PIC18F452) PIC Cost with Board ($) Software Power (mA) Ether-net Memory Speed Manufacturer (website) Processor
  6. 6. Processors & Boards PIC Development Board PC-104 Micro ATX 7 inches 3.5 in. 4 in. (includes breadboard for sensors) Too heavy, complicated & power hungry
  7. 7. Sensors Analog Devices MEMS sensors ADXL 202/210 2-Axis Accelerometers Range: ±2g or ±10g RMS Noise : < 0.002 g for 10 Hz Bandwidth Option Power : 0.6 mA @ 3-5 VDC Size: Less than 0.5 gram, 0.05 cm 3 ADXRS 150/300 Rate Gyros Range: ±150 or ±300 °/sec Noise : 0.05 °/sec/Hz 1/2 Power: 6.0 mA @ 5 VDC Size: Less than 0.5 gram, 0.15 cm 3 Crista IMU Uses Analog Devices MEMS sensors, with 3 gyros and 3 accelerometers integrated into a single unit with serial interface. Unit weighs 37 grams with enclosure. This is a relatively expensive item. Very easy to use and integrate. Honeywell HMR 3100 Digital Compass One of the smallest and cheapest units available. Size: < 1.5 grams Power: 0.2mA @ 3 VDC RMS Accuracy : < 5 deg Novatel SuperStar II GPS Receiver At 22 grams, one of the smallest and lightest units available. Size: 22 grams Power: < 0.5 W Accuracy: < 5 m CEP (< 1 m CEP DGPS) Currently looking into SONAR altimeters All of these devices are currently being tested. Can survive 1000 g shock
  8. 8. Summary of Quad-Rotor Systems Currently Under Development 1. PC/104 Based System PC system is easy to program and to integrate with other hardware. Also uses Crista IMU, and wireless network adapter for communication. Too heavy and power consuming but will be use for rapid prototyping of control laws. 3. BasicAtom System 2. PIC Microcontroller System Very good performance relative to weight, power consumption, and cost. Programmable in C. Requires more electronics expertise to hardware. Lightweight, low power consumption. Program in BASIC. Can perform floating point math. Already integrated with MEMS sensors and motors. 4. Javelin Stamp System Lightweight, low power consumption. Program in JAVA. Limited to integer math. May not be feasible to implement control laws. 5. Custom-Designed System Custom-designed board integrates processor and all sensors and servos. Optimal in terms of weight and power. Requires EE expertise.
  9. 9. Draganflyer III Photo from SuperStar II GPS Receiver (22 grams) Remove existing electronics Crista IMU (37 grams) Intel 166 MHz Pentium MMX PC/104+ (110 grams) HMR3100 Digital Compass (1.5 grams) NetGear Wireless Network Adapter (20 grams) PC/104 is relatively heavy and power consuming PC Based Avionics
  10. 10. <ul><li>Testing PC-104 system on a DraganFlyer III airframe </li></ul><ul><li>System is heavy will need external power source (using tether) </li></ul><ul><li>Using Linux operating system with 256 MB ChipDisk storage </li></ul><ul><li>Wireless network adapter communication with ground station </li></ul><ul><li>Crista IMU from Cloud Cap Technology (plug and play) </li></ul><ul><li>SSC board generates PWM signals </li></ul><ul><li>Hobby ESC units control motors </li></ul><ul><li>System is easier to program and integrate but is too heavy and power consuming for practical application. </li></ul><ul><li>Can be used for initial testing of control laws before transitioning to lighter microprocessor </li></ul>Bench Test Integration of PC Based Avionics for Quad-Rotor Initial bench test integration with PC-104 on the development board.
  11. 11. Quad Rotor Draganflyer III Photo from Remove Existing Electronics PIC Microcontroller or other lightweight microprocessor 4-channel receiver Wireless Video Camera MEMS-based ADXRS150 Gyro CMC GPS Receiver MEMS-based ADXL202E Accelerometer PIC Microcontroller Avionics HMR3100 Digital Compass
  12. 12. BasicAtom Test Platform Batteries Electronic Speed Control 280 size Motor Accelerometer Gyroscope BasicAtom Processor Serial PWM Controller 9 inch prop
  13. 13. Close-up of Processor and Sensor Boards Accelerometer Gyroscope Hitachi 3664 Processor 1 inch
  14. 14. Accelerometer Output X accel (in g's) = 0.000000 X accel (in g's) = 0.035693 X accel (in g's) = 0.014277 X accel (in g's) = 0.963735 X accel (in g's) = -0.513993 X accel (in g's) = 0.064248 X accel (in g's) = 0.021416 Acceleration in the positive x-direction
  15. 15. Gyroscope Output Z ang velocity (degrees / sec) = 0.000000 Z ang velocity (degrees / sec) = 0.000000 Z ang velocity (degrees / sec) = 14.062715 Z ang velocity (degrees / sec) = 47.266346 Z ang velocity (degrees / sec) = 28.516060 Z ang velocity (degrees / sec) = 4.296940 Z ang velocity (degrees / sec) = -1.171893 Z ang velocity (degrees / sec) = -18.359653 Z ang velocity (degrees / sec) = -26.953535 Z ang velocity (degrees / sec) = -37.109940 Z ang velocity (degrees / sec) = -37.109940 Z ang velocity (degrees / sec) = -36.328678 Clockwise Rotation Counter-Clockwise Rotation
  16. 16. PSU Custom Designed Autopilot 3 EE PhD students from Long’s UAV course developed a custom-designed avionics board using the MEMS-based sensors. Board was designed, manufactured, and is now undergoing testing and programming. Should be completed Summer 2004. Quad-rotor helicopter Gyros Atmel Processor Accelerometer Motor controllers
  17. 17. Control Design Incremental Approach <ul><li>Stability augmentation using classical control laws (PID Control) </li></ul><ul><ul><li>Initially use rate gyros only </li></ul></ul><ul><ul><li>Rate command response type </li></ul></ul><ul><ul><li>Goal: Improve handling qualities over existing Draganflyer III </li></ul></ul><ul><li>Enhanced stability augmentation </li></ul><ul><ul><li>Incorporate 3 linear accelerometers </li></ul></ul><ul><ul><li>Achieve attitude command response type </li></ul></ul><ul><ul><li>Goal: Inexperienced pilot can learn to fly in a couple of days </li></ul></ul><ul><li>Semi-autonomous mode </li></ul><ul><ul><li>Add GPS receiver, digital compass, altimeter </li></ul></ul><ul><ul><li>Translational rate command response type </li></ul></ul><ul><ul><li>State estimation algorithm needed (Extended Kalman Filter) </li></ul></ul><ul><ul><li>Goal: Anyone can fly with very limited training </li></ul></ul><ul><li>Reliable autonomous flight </li></ul><ul><ul><li>Way point navigation </li></ul></ul><ul><ul><li>Additional sensors for fault-tolerant control </li></ul></ul><ul><ul><li>Goal: Reliable autonomous operation </li></ul></ul>
  18. 18. <ul><li>Simplified non-linear model of quad-rotor dynamics near hover (ref. Huzeman et al) </li></ul><ul><li>Can identify empirical damping constants from flight test data </li></ul><ul><li>Inertia parameters identified from swing test </li></ul><ul><li>Dynamics are simulated in SIMULINK </li></ul><ul><li>Developing preliminary design of simple PID controllers </li></ul><ul><li>Autonomous control will require accurate state estimator </li></ul>Quad-Rotor Dynamics
  19. 19. Control Law Design SIMULINK Diagram of Simple PID Controller
  20. 20. Preliminary Observations on Control Law Design <ul><li>Small microcontrollers should have sufficient processing power to implement PID control </li></ul><ul><li>Linear dynamics of quad-rotor in hover are very simple </li></ul><ul><ul><li>Minimal damping, behaves like inertial system </li></ul></ul><ul><ul><li>Minimal cross-coupling </li></ul></ul><ul><ul><li>Can use classical control design methods </li></ul></ul><ul><li>Main issue with MEMS inertial sensors is bias and drift </li></ul><ul><ul><li>Cannot use pure integral action, need to wash out integral signal </li></ul></ul><ul><ul><li>Accurate Attitude estimation is not feasible with inertial measurements alone </li></ul></ul><ul><ul><li>Not a major issue with pilot-in-the-loop control </li></ul></ul><ul><li>Autonomous flight will require state estimation with assistance from GPS, Digital Compass, and possibly altimeter. Plan on using Extended Kalman Filter. </li></ul><ul><li>Can small microcontrollers perform accurate state estimation in real-time? </li></ul><ul><li>Can we achieve sufficiently accurate state estimation with these sensors to achieve autonomous flight? </li></ul>
  21. 21. Autonomous UAV’s are vulnerable to the failure or degradation of sensors Incorporate multiple inexpensive accelerometers and take advantage of kinematical coupling to derive rate and attitude information Use an Extended Kalman Filter (EKF) to fuse sensor data Fault-Tolerant UAV Flight Control Long term objective FCC Accelerometers Flight Control Computer
  22. 22. New UAV Course PSU Funded, Taught by Prof. Long, Fall 2003. 31 students Every student built and flew airplanes. Guest lectures on UAVs. 1 credit. Spring 2004. 28 students Students worked in teams of 5 to build large 80 in. span aircraft. Installed wireless video cameras, onboard flight data recorders, performed flight tests. 2 credits.
  23. 23. Onboard Wireless Video Camera View of area around flying field. At approximately 400 feet altitude. Cars Us Landing strip
  24. 24. Onboard Flight Data Recorder (Crash in UAV course, April, 2004) Altitude (ft) Speed (MPH) Onboard “blackbox” used for flight testing, but also contained crash data. (in high-speed dive the elevator failed and pilot lost control) Pilot Input Elevator lost, but pilot still trying… time, seconds
  25. 25. Commercial R/C Autopilot Piccolo Autopilot by Cloud Cap Technology Manufacturer of this autopilot system ($15,000) very interested in our RCOE center and our UAV course. (gave us a free system for the course for next year) We hope to work with them to make this suitable for rotary wing UAV’s Moving map displays from ground station Can be coupled to flight simulator <ul><li>Weighs 212 grams, consumes about 300 mA at 12V </li></ul><ul><li>Sensors include IMU, GPS, Pitot and static pressure ports </li></ul><ul><li>Can control all primary servos plus additional servos </li></ul><ul><li>UHF communication link with ground station </li></ul><ul><li>Some user programmability, set up for PID control </li></ul><ul><li>We have tested units in Hardware-in-the-loop simulation </li></ul><ul><li>Test initially on fixed-wing then transition to helicopter </li></ul>
  26. 26. Accomplishments 2003 Accomplishments 2003 Accomplishments <ul><li>Program awarded in Fall of 2002 </li></ul><ul><li>Evaluated wide range of processors and sensors </li></ul><ul><li>Coupled and programmed MEMS-based gyros, accelerometers to microcontroller </li></ul><ul><li>Demonstrate feasibility and document limitations of systems </li></ul><ul><li>Acquired commercial autopilot system and began evaluation </li></ul><ul><li>Developed and taught popular new UAV course </li></ul>Planned 2004 Accomplishments <ul><li>Will concentrate on incorporating PIC processors </li></ul><ul><li>Will couple all components (rotors, sensors, processors, …) </li></ul><ul><li>Will develop PID control software using C language </li></ul><ul><li>Will demonstrate feasibility and document limitations of system and </li></ul><ul><li>sensors </li></ul><ul><li>Will also attempt to couple systems to traditional R/C helicopter </li></ul>
  27. 27. Technology Transfer Activities: Leveraging or Attracting Other Resources or Programs: Recommendations at the 2003 Review: None. Received $ 50K to develop R/C aircraft course. ( ) 2002 DURIP grant includes $20K for UAV research <ul><li>Began interacting with CloudCap Technology. </li></ul><ul><li>Prof. Long is on organizing committee for 1 st AIAA Intelligent Systems Conference (Chicago, Sept., 2004) </li></ul><ul><li>Prof. Long is Editor-in-Chief of Journal of Aerospace Computing, Information, and Communication (JACIC) ( ) </li></ul>