Skynet An Autonomous Quatrocopter Designed by Andrew Malone And Bryan Absher
Mar 26, 2015
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