Your SlideShare is downloading. ×
  • Like
Lecture 04: Sensors
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Now you can save presentations on your phone or tablet

Available for both IPhone and Android

Text the download link to your phone

Standard text messaging rates apply
Published

 

Published in Education
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
1,411
On SlideShare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
44
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

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