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Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
Lecture 04
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Lecture 04

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  • 1. Introduction to RoboticsSensors<br />CSCI 4830/7000<br />September 20, 2010<br />NikolausCorrell<br />
  • 2. Review: Kinematics and Control<br />Concepts<br />Forward Kinematics<br />“Odometry”<br />Feed-back Control<br />Inverse Kinematics<br />
  • 3. Forward Kinematics<br />How does the robot move in world space given its actuator speed and geometry?<br />“Odometry”: forward kinematics for mobile platform<br />Example: from exercise 3<br />
  • 4. Proportional Control<br />N.B.: zero error neq correct position!<br />
  • 5. More on robot kinematics (arms)<br />John Craig<br />Introduction to Robotics<br />Mark Spong, Seth Hutchinson and M.Vidyasagar<br />Robot Modeling and Control<br />
  • 6. Inverse Kinematics<br />How do we need to control the actuators to reach a certain position?<br />Inversion of forward kinematics<br />Examples: Differential wheel drive (Exercise 3)<br />
  • 7. Feedback control<br />Use error between reference and actual state to calculate next control input<br />Change in speed proportional to error<br />Error zero -&gt; speed zero<br />Problem: find stable controllers<br />Example: from exercise<br />K. Ogata<br />Modern Control Engineering<br />
  • 8. Today <br />Perception: Basis for reasoning about the world<br />Understand how a sensor works before using it<br />Case studies<br />
  • 9. iRobotRoomba<br />4 Bumpers<br />2 Floor sensors<br />1 infrared distance (side)<br />Infrared<br />Wheel encoders<br />
  • 10. PrairieDog<br />Roomba<br />5.6m, 240 degrees laser scanner<br />Indoor localization system<br />Camera<br />Microphone<br />5 Position encoders (arm)<br />
  • 11. Nao<br />2 VGA cameras<br />4 Microphones<br />2-axis gyroscope<br />3-axis accelerometer<br />2 bumpers (feet)<br />Tactile sensors (hands + feets)<br />Hall-effect encoders<br />2 Sonar<br />2 Infrared<br />Proprioceptive or Exteroceptive?<br />
  • 12. PR2 (WillowGarage)<br />
  • 13. Laser Range Scanner<br />Measures phase-shift of reflected signal<br />Example: f=5MHz -&gt; wavelength 60m<br />
  • 14. Examples<br />2 D<br />3D (PR2 sweep)<br />(after classification)<br />
  • 15. Sensor performance<br />Dynamic range: lowest and highest reading<br />Resolution: minimum difference between values<br />Linearity: variation of output as function of input<br />Bandwidth: speed with which measurements are delivered<br />Sensitivity: variation of output change as function of input change<br />Cross-Sensitivity: sensitivity to environment<br />Accuracy: difference between measured and true value<br />Precision: reproducibility of results<br />Hokuyo URG<br />
  • 16. Relation between sensor physics and performance (solutions)<br />Dynamic range: <br />Range: limited by power of light and modulated frequency, smallest wave-length difference measurable<br />Angle: limited by physical setup / trade-off between bandwidth and angular resolution<br />Resolution:<br />Range: Precision of phase-shift measurement<br />Angle: limited by bandwidth / encoder<br />Linearity:<br />Range: phase shift is linear -&gt; signal is linear, but: weak reception makes determination of phase harder<br />Angle: depends on motor implementation<br />Bandwidth<br />Range: speed of light, calculating phase shift<br />Angle: motor speed<br />Sensitivity:<br />Range: Doppler effect -&gt; not relevant in robotics, Confidence in the range (phase/time estimate) is inversely proportional to the square of the received signal amplitude<br />Angle: n.a.<br />Cross-Sensitivity:<br />Range: Glass / reflection properties, 785nm light <br />Accuracy:<br />Range: Precision of phase-shift measurement, strength of reflected light<br />Angle: motor quality<br />Precision: range / variance<br />
  • 17. Infra-red distance sensors<br />Principle: measure amount of reflected light<br />The closer you get, the more light gets reflected<br />Digitized with analog-digital converter<br />Sharp IR Distance Sensor GP2Y0A02YK<br />20-150cm<br />Miniature IR transceiver<br />0-3cm<br />
  • 18. Sensor performance<br />Dynamic range: lowest and highest reading<br />Resolution: minimum difference between values<br />Linearity: variation of output as function of input<br />Bandwidth: speed with which measurements are delivered<br />Sensitivity: variation of output change as function of input change<br />Cross-Sensitivity: sensitivity to environment<br />Accuracy: difference between measured and true value<br />Precision: reproducibility of results<br />Sharp IR Distance Sensor<br />
  • 19. Relation between sensor physics and performance (solutions)<br />Dynamic range: limited by power of light<br />Resolution: limited by ADC, e.g. 10bit -&gt; 1024 steps<br />Linearity: highly non-linear (intensity decays quadratically)<br />Bandwidth: limited by ADC bandwidth (sample&amp;hold)<br />Sensitivity: varies over range due to resolution<br />Cross-Sensitivity: sun-light, surface properties<br />Accuracy: limited by ADC, varies over range<br />Precision: varies over range<br />
  • 20. Infra-red distance sensors in Webots (Exercise 1)<br />Color of the bounding object affects sensor<br />Non-linear relation between distance and signal strength<br />Distance-dependent resolution and noise<br />Software linearization<br />Noise<br />
  • 21. Exercise<br />Design a robot that can<br />Vacuum a room<br />Mow a lawn<br />Collect golf-balls on a range<br />Collect tennis balls on a court<br />Address<br />Sensors<br />Algorithm<br />Mechanism<br />
  • 22. Scratchboard<br />
  • 23. Homework<br />Read section 4.1.7 (pages 117 – 145)<br />Questionnaire on CU Learn<br />Midterm: October 11 (during class)<br />

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