SkynetAn Autonomous Quatrocopter

Designed byAndrew Malone

AndBryan Absher

Introduction

• Flying robot

• Self stabilizing

• Able to fly in preprogrammed patterns

• Autonomous

• Low cost

Outline

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

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

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

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

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

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

Darlington Transistor

TIP 122– 5A Max Current– β >1000 at 5A– ~1V VCE– < 20µs Switching Time

Delivery to Motor

• AC Output Interacts with Inductance

• Motors Prefer DC inputs

• Low-pass Filter

http://www.zen22142.zen.co.uk/Design/dcpsu.htm

Final Circuit Design

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

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

Sensor Theory

• Accelerometer– Charged cantilever– Change in acceleration

changes the capacitance of the cantilever

Sensor Theory

• Gyroscopes– MEMS gyroscopes consist of a vibrating

structure– Angular velocity changes the vibration

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

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

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

System Control

• PID control– Proven method– Standard Tuning methods

• Ziegler–Nichols – Effective at controlling high order systems

dt

deKteKteKtePID dIp )()())((

Our Control System

Results

• PID-controlled power output

• Accurate angular orientation measurement

• Sufficient lift, battery life

• Wireless feedback

Applications

• Aerial Displays– MIT Flyfire

• Flying sensor network• Autonomous

surveillance

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