A.Q.U.A.L.U.N.G.
37
Bianca Wood Chris Culver Shane Parker Yousef Al-Khalaf
description
A.Q.U.A.L.U.N.G. Bianca Wood Chris Culver Shane Parker Yousef Al-Khalaf. Motivation. Challenge Our Capabilities Sense of Accomplishment Sheer Fun. Objectives. Build a flying stable quadrotor Agile Real-time, intelligent decision-making Autonomous. Challenges. - PowerPoint PPT Presentation
Transcript of A.Q.U.A.L.U.N.G.
Bianca WoodChris Culver
Shane ParkerYousef Al-Khalaf
Motivation
Challenge Our CapabilitiesSense of AccomplishmentSheer Fun
Objectives
Build a flying stable quadrotorAgileReal-time, intelligent decision-makingAutonomous
Challenges •Waiting for parts to come in •Not having a proper testing environment
•(worked in a ditch literally)•Making something fly, and also be stable• Having our frame break two days before our final presentation
Specifications
Each quadrotor is .91 m diamterHeight of .178 m Weight ~ 5 lbsAble to operate for 15 minutes on a single charge
A.I. Controller
Power System
Image Processor
Flight Control
Current Position
Shut Off
Memory Power Switch
Starting Pos.
Motor ControllerCamera
Block Diagram
Fire Control
A.I. Controller
Power System
Image Processor
Flight Control
Current Position
Shut Off
Memory Power Switch
Starting Pos.
Motor ControllerCamera
Block Diagram
Fire Control
A.I. Controller
Power System
Image Processor
Flight Control
Current Position
Shut Off
Memory Power Switch
Starting Pos.
Motor ControllerCamera
Block Diagram
Fire Control
A.I. Controller
Power System
Image Processor
Flight Control
Current Position
Shut Off
Memory Power Switch
Starting Pos.
Motor ControllerCamera
Block Diagram
Fire Control
A.I. Controller
Power System
Image Processor
Flight Control
Current Position
Shut Off
Memory Power Switch
Starting Pos.
Motor ControllerCamera
Block Diagram
Fire Control
Power
Micro Controller
Motor
Motor
Motor
Motor
Movement
A.I. Controller
Flight Control Sub-System
NavigationControl
Algorithm Coordinates /
Sensors Motors
Navigation•Coordinates come from AI computer
Stabilization•Readings come from sensors
PWM Signal
Rxacc = (ADCRx*Vref/1023 – Vzerog)/ Sensitivity
Ryacc = (ADCRy*Vref/1023 – Vzerog)/ Sensitivity
Rzacc = (ADCRz*Vref/1023 – Vzerog)/ Sensitivity
R2= Rxacc + Ryacc + Rzacc
2 2 2
-ADC = Value coming from accelerometer -Vref = Reference voltage from ADC -1023 = Max value of ADC bus-Vzerog = Acc under 0 g’s of force -Sensitivity = Relationship between changes in acceleration to change in output
Accelerometer
( ) Θxr = cos
-1 Rx
R
( ) Θzr = cos
-1 Rz
R
( ) Θyr = cos
-1 Ry
R
Gyroscope
θxy = Rotation around Z axis Yaw
θyz = Rotation around X axis Roll
θxz = Rotation around Y axis Pitch
Rate θxy = (ADCxy*Vref/1023 – VoltsZeroRate)/Sensitivity
Rate θxz = (ADCxz*Vref/1023 – VoltsZeroRate)/Sensitivity
Rate θyz = (ADCyz*Vref/1023 – VoltsZeroRate)/Sensitivity
-ADC = Value coming from gyro-Vref = Reference voltage from ADC -1023 = Max value of ADC bus-VoltsZeroRate = Output voltage when no rotation-Sensitivity = Change in output voltage with one degree per sec rotation
Combining Accelerometer and Gyroscope Data
• Takes Accelerometer data • Checks it against Gyroscope data and past output data• Corrects itself
• Rout(n) = Current output of Algorithm• Rout(n-1) = Last output of Algorithm • Rate θ = Gyro output • Rgyro = Current gyro & past output combined
Θxz(n-1) = atan2(Rxout(n-1), Rzout(n-1))Θyz(n-1) = atan2(Ryout(n-1), Rzout(n-1))Θxy(n-1) = atan2(Rxout(n-1), Ryout(n-1))
Θxz(n) = Θxz(n-1) + Rate θxz(n)*T
Θyz(n) = Θyz(n-1) + Rate θyz(n)*T
Θxy(n) = Θxy(n-1) + Rate θxy(n)*T
Rxgyro(n) = sin(Θxz(n)) / SQRT{1 + cos (Θxz(n) )*tan (Θyz(n))} 2 2
Rygyro(n) = sin(Θyz(n)) / SQRT{1 + cos (Θyz(n) )*tan (Θxz(n))} 22
Rzgyro(n) = SQRT( 1 – Rxgyro (n) – Rygyro (n))22
(T= sampling period)
• Rout(n) = Current output of Algorithm• Rout(n-1) = Last output of Algorithm • Rate θ = Gyro output • Rgyro = Current gyro & past output combined
Rout(n) = Racc + Rgyro ( ) w2
w1
( ) w2
w11 +
( ) w2
w1
*= How much to trust the gyro over the accelerometer
Computer VisionHaar Wavelets, first real time face detector.
Viola and Jones adapted idea, developed Haar-Like-Features. Considers adjacent rectangular regions at a specific location
in a detection window. Sums pixel intensities. Calculates difference between the sums.
Computer Vision•Integral Image Algorithm
•Single Pass Over the Image
•Evaluating any Rectangle in Constant Time
Overall Software Class Diagram
Artificial Intelligence Subsystem
State Machine
Navigation Mesh
Path With Navigation Mesh
Hardwarei7 Ivy Bridge16GB DDR3 1600 with 9-9-9-24 Timings120GB SSDNVIDIA 8900 GT
Voltage : 2.1 – 3.6
Frequency : 2.4 GHz
Data Rate : 250 Kbps
Range : 200 ft open space
Voltage : 5 v
Current : 500 mA
Power(2) Lithium-ion Polymer batteries11.1 v 2200mAhMounted on the
bottomCamera powered
by 9VPCB to disperse the
power
Power Distribution
Battery 1
Battery 2
PCB &
Voltage Regulator
ESC/Motor
ESC/Motor
ESC/Motor
ESC/Motor
PCB•Power in from left and right• Voltage regulator came with
MCU•5V Regulator•Receiving ~ 22V
• 2 diodes •4 arms to the ESCs/motors
PCB•Power in from left and right• Voltage regulator came with
MCU•5V Regulator•Receiving ~ 22V
• 2 diodes •4 arms to the ESCs/motors
PCB•Power in from left and right• Voltage regulator came with
MCU•5V Regulator•Receiving ~ 22V
• 2 diodes •4 arms to the ESCs/motors
PCB•Power in from left and right• Voltage regulator came with
MCU•5V Regulator•Receiving ~ 22V
• 2 diodes •4 arms to the ESCs/motors
FinancesMotors (2) $500 donationsPCB materials donatedSelf funded
BudgetItem Quantity
Price per Quantity Total Price
Quadcopter Frames 2 $30, $100 $130
Computer 1 $1,000 ($1,000)
Motors 12 $10 ($120)
Slowfly electric prop 1045R 4-piece set 4 $3 $12
Wireless Microprocessor 1 $150 $150
Wireless 2.4 GHz camera 1 $20 $20
Wireless 2.4 GHz USB receiver 1 $40 $40
Zigbee 1 $80 $80
ESCs 4 $7 $28
11.1V 2200mAh 25C LiPo Battery 2 $15 $30
PCB 1 - -
Miscellaneous Unknown UnknownAllocating
$150
Total $640
Thank youSponsors
Jeff MolerTim ParkerJohn Enander
Dr. Samuel Richie
Questions
Bianca WoodChris Culver
Shane ParkerYousef Al-Khalaf
