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A.Q.U.A.L.U.N.G.

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.

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A.Q.U.A.L.U.N.G.

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  1. A.Q.U.A.L.U.N.G. Bianca Wood Chris Culver Shane Parker Yousef Al-Khalaf

  2. Motivation • Challenge Our Capabilities • Sense of Accomplishment • Sheer Fun

  3. Objectives • Build a flying stable quadrotor • Agile • Real-time, intelligent decision-making • Autonomous

  4. 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

  5. Specifications • Each quadrotor is .91 m diamter • Height of .178 m • Weight ~ 5 lbs • Able to operate for 15 minutes on a single charge

  6. Block Diagram Memory Power Switch Starting Pos. Motor Controller Camera Shut Off Power System A.I. Controller Current Position Image Processor Fire Control Flight Control

  7. Block Diagram Memory Power Switch Starting Pos. Motor Controller Camera Shut Off Power System A.I. Controller Current Position Image Processor Fire Control Flight Control

  8. Block Diagram Memory Power Switch Starting Pos. Motor Controller Camera Shut Off Power System A.I. Controller Current Position Image Processor Fire Control Flight Control

  9. Block Diagram Memory Power Switch Starting Pos. Motor Controller Camera Shut Off Power System A.I. Controller Current Position Image Processor Fire Control Flight Control

  10. Block Diagram Memory Power Switch Starting Pos. Motor Controller Camera Shut Off Power System A.I. Controller Current Position Image Processor Fire Control Flight Control

  11. Flight Control Sub-System Power Movement Motor Motor Motor Motor Micro Controller A.I. Controller

  12. Navigation Control Algorithm Coordinates / Sensors Motors Navigation • Coordinates come from AI computer PWM Signal Stabilization • Readings come from sensors

  13. Accelerometer Rxacc = (ADCRx*Vref/1023 – Vzerog)/ Sensitivity Ryacc = (ADCRy*Vref/1023 – Vzerog)/ Sensitivity Rzacc = (ADCRz*Vref/1023 – Vzerog)/ Sensitivity • 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 2 R = Rxacc + Ryacc + Rzacc 2 2 2 Rx ( ) -1 Θxr = cos R Ry ( ) -1 Θyr = cos R Rz ( ) -1 Θzr = cos R

  14. 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

  15. 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

  16. 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 (T= sampling period) 2 2 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))} 2 2 Rzgyro(n) = SQRT( 1 – Rxgyro (n) – Rygyro (n))

  17. w2 w2 w2 ( ) ( ) ( ) Rout(n) = Racc + Rgyro w1 w1 w1 1 + * = How much to trust the gyro over the accelerometer

  18. Computer Vision • Haar 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.

  19. Computer Vision Integral Image Algorithm Single Pass Over the Image Evaluating any Rectangle in Constant Time

  20. Overall Software Class Diagram

  21. Artificial Intelligence Subsystem

  22. State Machine

  23. Navigation Mesh

  24. Path With Navigation Mesh

  25. Hardware i7 Ivy Bridge 16GB DDR3 1600 with 9-9-9-24 Timings 120GB SSD NVIDIA 8900 GT

  26. Voltage : 2.1 – 3.6 Frequency : 2.4 GHz Data Rate : 250 Kbps Range : 200 ft open space Voltage : 5 v Current : 500 mA

  27. Power • (2) Lithium-ion Polymer batteries • 11.1 v • 2200mAh • Mounted on the bottom • Camera powered by 9V • PCB to disperse the power

  28. Power Distribution ESC/Motor Battery 1 PCB & Voltage Regulator ESC/Motor Battery 2 ESC/Motor ESC/Motor

  29. PCB • Power in from left and right • Voltage regulator came with • MCU • 5V Regulator • Receiving ~ 22V • 2 diodes • 4 arms to the ESCs/motors

  30. PCB • Power in from left and right • Voltage regulator came with • MCU • 5V Regulator • Receiving ~ 22V • 2 diodes • 4 arms to the ESCs/motors

  31. PCB • Power in from left and right • Voltage regulator came with • MCU • 5V Regulator • Receiving ~ 22V • 2 diodes • 4 arms to the ESCs/motors

  32. PCB • Power in from left and right • Voltage regulator came with • MCU • 5V Regulator • Receiving ~ 22V • 2 diodes • 4 arms to the ESCs/motors

  33. Finances • Motors • (2) $500 donations • PCB materials donated • Self funded

  34. Budget

  35. Thank you • Sponsors • Jeff Moler • Tim Parker • John Enander • Dr. Samuel Richie

  36. Questions

  37. A.Q.U.A.L.U.N.G. Bianca Wood Chris Culver Shane Parker Yousef Al-Khalaf

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