Skynet An Autonomous Quatrocopter

1. SkynetAn Autonomous Quatrocopter Designed by Andrew Malone And Bryan Absher

2. Introduction • Flying robot • Self stabilizing • Able to fly in preprogrammed patterns • Autonomous • Low cost

3. Outline • Block Diagram • PWM Control • Motor Driver Circuit • Wireless Communications • Sensors • Control System • Results • Applications • Future Improvements

5. Power Consumption • Logic Power • 2 x PICLF877A Microprocessor • 0.6mA at 3 V and 4Mhz • 3 x LY530ALH 1 Axis Gryroscope • 5mA at 3 V • ADXL335 3 axis Accelerometer • 3 uA at 3 V • Use 2 button batteries at 150mAh each

6. Motor Driving System • Control of High-Current Motors with a Microprocessor • Microprocessor Output • PIC16F877A • 2V to 5V max ~25mA • Motor • GWS EDF50 • ~4 Amps at 10.8 V

7. PWM Characteristics • Output Voltage is Simulated • Device is Switched On and Off • PIC PWM max 25mA • Magnifies Motor Driving Concerns • Inductance • Generation • Noise • Power on Ground

8. System Requirements • Extremely High Current Gain • ~1000A/A • 10V Maximum Output from 11V Supply • High Current Output • ~5A per Motor • Fast Switching Time • < 20µs • Complete Electrical Isolation • No Common Ground

9. Optical Isolation • Anode and Cathode Voltages drive infrared LED • Light Modulates Phototransistor Base Voltage • Complete electric isolation • Cheap ($0.60 EE store) • Fast (5 – 10 µs) • TIL111 • Perfect for PWM

10. Darlington Transistor TIP 122 • 5A Max Current • β >1000 at 5A • ~1V VCE • < 20µs Switching Time

11. Delivery to Motor • AC Output Interacts with Inductance • Motors Prefer DC inputs • Low-pass Filter http://www.zen22142.zen.co.uk/Design/dcpsu.htm

12. Final Circuit Design

13. Wireless Communication • IEEE 802.15.1 (Bluetooth) • Low power (100mA Tx, 20mA Rx) • Complex Protocol Stack • Small Network Size • Fast Data Rates (1.5 Mbit/s, or 3 Mbit/s) • IEEE 802.15.4 Zigbee • Low power • Low overhead • Slower data rates • Large network size

14. Our Implementation • Simple configuration • UART communications • 115 kBaud (Limited by PIC16LF877A) • 3.3 V • RN-41-SM • Light weight • Low power • High data speed • Good for tuning PID

15. Sensor Theory • Accelerometer • Charged cantilever • Change in acceleration changes the capacitance of the cantilever

16. Sensor Theory • Gyroscopes • MEMS gyroscopes consist of a vibrating structure • Angular velocity changes the vibration

17. Sensor Implementation • Ideal implementation: • Initial angles = arctan(x/z) and arctan(y/z) • ω from gyroscope reading • Subsequent angles = initial angles + ∫ω*dt • Accelerations relative to ground derived from accelerometer combined with gyroscope angle readings • Velocity = ∫a*dt • Position = ∫v*dt

18. Sensor Implementation • Accelerometers and Gyroscopes vary widely from specification • Accelerometer bias must be calibrated • Gyroscope bias varies over time • Inaccurate over long periods • Readings can be corroborated using a Kalman Filter • Integrals rely on fast sampling rate

19. Sensor Implementation Angle • Assume gravity is greatest acceleration • Angle = arctan(r/z) • Extremely accurate • Change in Altitude • Integrate Z-axis acceleration • Accurate for very small accelerations

20. System Control • PID control • Proven method • Standard Tuning methods • Ziegler–Nichols • Effective at controlling high order systems

21. Our Control System

22. Results • PID-controlled power output • Accurate angular orientation measurement • Sufficient lift, battery life • Wireless feedback

23. Applications • Aerial Displays • MIT Flyfire • Flying sensor network • Autonomous surveillance

24. Improvements • 32 or 16bit ARM processor at 100 Mhz • Horizontal motion measurements • Local or Global GPS • SONAR • Environment sensors • CO2 • Visual • Wind Speed • ZigBee mesh network • Create a flying sensor network • Distributed intelligence • Kalman Filter • Reduce noise in angle measurements