Lecture 04

Introduction to RoboticsSensors
CSCI 4830/7000
September 20, 2010
NikolausCorrell

Review: Kinematics and Control
Concepts
Forward Kinematics
“Odometry”
Feed-back Control
Inverse Kinematics

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

Proportional Control
N.B.: zero error neq correct position!

More on robot kinematics (arms)
John Craig
Introduction to Robotics
Mark Spong, Seth Hutchinson and M.Vidyasagar
Robot Modeling and Control

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)

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

Today
Perception: Basis for reasoning about the world
Understand how a sensor works before using it
Case studies

iRobotRoomba
4 Bumpers
2 Floor sensors
1 infrared distance (side)
Infrared
Wheel encoders

PrairieDog
Roomba
5.6m, 240 degrees laser scanner
Indoor localization system
Camera
Microphone
5 Position encoders (arm)

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?

PR2 (WillowGarage)

Laser Range Scanner
Measures phase-shift of reflected signal
Example: f=5MHz -> wavelength 60m

Examples
2 D
3D (PR2 sweep)
(after classification)

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

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

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

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

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

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

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

Scratchboard

Homework
Read section 4.1.7 (pages 117 – 145)
Questionnaire on CU Learn
Midterm: October 11 (during class)

Lecture 04

by University of Colorado at Boulder

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Lecture 04

by University of Colorado at Boulder

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Lecture 04 Presentation Transcript

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