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Quadcopters. A CEV Talk. Agenda. Flight Preliminaries. 4 movements. Altitude Up Down Roll Left Right Pitch Front Back Yaw Heading. Altitude. Hover. Altitude. Up. Altitude. Down. Roll. Pitch. Quick question. Correct. Wrong. Yaw. Why Quadcopters ?. Flight is fun!.

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Presentation Transcript

  1. Quadcopters A CEV Talk

  2. Agenda

  3. Flight Preliminaries

  4. 4 movements • Altitude • Up • Down • Roll • Left • Right • Pitch • Front • Back • Yaw • Heading

  5. Altitude • Hover

  6. Altitude • Up

  7. Altitude • Down

  8. Roll

  9. Pitch

  10. Quick question Correct Wrong

  11. Yaw

  12. Why Quadcopters?

  13. Flight is fun!

  14. Simplicity

  15. Simplicity

  16. However • Scaling up,

  17. 4 rotors • 4 rotors harder to control than one

  18. Nevertheless • Mechanical Simplicity + Electronic Stabilization win

  19. Perks • Less stable = learn more control theory. • Less kinetic energy per motor(rotor). You wont lose your fingers.

  20. Agenda

  21. The Quadcopter System [Q]

  22. Open Loop

  23. Stability: The Notion Mind Sense Take action

  24. Agenda

  25. Inertial Measurement Unit (IMU)

  26. Angle calculation: Accelerometer • Inclination from an axis can be calculated using the component of gravity along that particular axis.

  27. Angle calculation: Gyroscope • Gyroscopes provide angular rate in degrees per second. • The angle with a certain axis can be calculated by integrating the angular velocity with respect to that axis over the sampling period.

  28. IMUs are not perfect! • Accelerometers: When in motion, the acceleration of the robot affects the acceleration measured by the accelerometer. • Gyroscopes : Due to manufacturing limitations, signal drift often accompanies MEMS gyros. When integrated over time, this drift leads to considerable error.

  29. Complementary filter • Simplest filter for IMUs • Corrects Gyro drift by including a certain component of angle measured by the accelerometer in angle measurement Angle= 0.98*(Gyroscope Angle) + 0.02*(Accelerometer angle)

  30. Kalman filter

  31. Agenda

  32. Control Algorithms

  33. Proportional-Integral-Derivative : An Intuition • Proportional term generates output based on error • Integral term generates output based on bias in error • Derivative term generates output based on speed of error variation Mathematical procedures to tune PID constants (very hard work): -- Root locus method -- Bode plots -- Nyquist Criterion -- Zeigler Nicols Algorithm Method which usually works: -- Trial and Error (video)

  34. Inside The Controller Control signals for ESC, which will in turn command motors Set point PID error Computed Angles Measured Angular rates And acceleration Filtering and Data fusion

  35. The Closed Loop Controller Quadcopter dynamics Sensors

  36. Agenda

  37. Onward we fly…

  38. Onward we fly…

  39. Onward we fly… • Quads in aerial photography, delivery systems, search and rescue…

  40. Onward we fly… • Better (Nonlinear) Control • Accurate estimators • SLAM • Motion Planning

  41. Better Control • Non linear control (V) • Robust control • Adaptive control • Stochastic control

  42. Better Control

  43. Accurate Estimators • Implementing Extended Kalman filter • Third order stochastic filter • Multi state constraint Kalman filter for vision aided navigation

  44. So Far So Good • Hardware • Stability • Movement • Interaction?

  45. Quad’s eye view • What does the world look like? [MAPPING] • Where am I? [LOCALIZATION] • A chicken and Egg problem

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