Robotics 10

8/8/2019 Robotics 10

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CS 491/691(X) - Lecture 5 1

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Topics: Introduction toRobotics

CS 491/691(X)Lecture 5

Instructor: Monica Nicolescu


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CS 491/691(X) - Lecture 5 4

Reflective OptosensorsInclude a source of light emitter (lightemitting diodes LED) and a lightdetector (photodiode or phototransistor)

Two arrangements, depending on thepositions of the emitter and detector Reflectance sensors: Emitter and detector

are side by side; Light reflects from the objectback into the detector

Break-beam sensors: The emitter anddetector face each other; Object is detected if light between them is interrupted


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CS 491/691(X) - Lecture 5 5

CalibrationAmbient / background light can interfere with the sensor measurement

The ambient light level should be subtracted to get only theemitter light level

Calibration : the process of adjusting a mechanism so as tomaximize its performance

Ambient light can change sensors need to be calibratedrepeatedly

Detecting ambient light is difficult if the emitter has the samewavelength

Adjust the wavelength of the emitter


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CS 491/691(X) - Lecture 5 6

Infra Red (IR) LightIR light works at a frequency different than ambientlight

IR sensors are used in the same ways as the visible

light sensors, but more robustly Reflectance sensors, break beams

Sensor reports the amount of overall illumination, ambient lighting and the light from light source

More powerful way to use infrared sensing Modulation/demodulation : rapidly turn on and off the source

of light


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CS 491/691(X) - Lecture 5 7

Modulation/DemodulationModulated IR is commonly

used for communication

Modulationis done by flashing the light source at aparticular frequency

This signal is detected by a demodulator tuned to

that particular frequencyOffers great insensitivity to ambient light Flashes of light can be detected even if weak


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CS 491/691(X) - Lecture 5 8

Infrared CommunicationBit frames All bits take the same amount of

time to transmit

Sample the signal in the middle of the bit frame

Used for standard computer/modem communication Useful when the waveform can be reliably transmitted

Bit intervals

Sampled at the falling edge

Duration of interval between sampling determines whether it is a0 or 1

Common in commercial use

Useful when it is difficult to control the exact shape of the

waveform


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CS 491/691(X) - Lecture 5 9

Proximity SensingIdeal application for modulated/demodulatedIR light sensing

Light from the emitter is reflected back intodetector by a nearby object, indicating

whether an object is present LED emitter and detector are pointed in the

same direction

Modulated light is far less susceptible to

environmental variables amount of ambient light and the reflectivity of

different objects


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CS 491/691(X) - Lecture 5 10

Break Beam SensorsAny pair of compatible emitter-detector devicescan be used to make a break-beam sensor

Examples:

Incadescent flashlight bulb and photocell

Red LEDs and visible-light-sensitive photo-transistors

IR emitters and detectors

Where have you seen these?

Break beams and clever burglars in movies

In robotics they are mostly used for keeping trackof shaft rotation


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CS 491/691(X) - Lecture 5 11

Shaft Encoding

Shaft encoders Measure the angular rotation of a shaft or an axle

Provide position and velocity information about the

shaftSpeedometers: measure how fast the wheels areturning

Odometers: measure the number of rotations of thewheels


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CS 491/691(X) - Lecture 5 12

Measuring RotationA perforated disk is mounted on the shaft

An emitterdetector pair is placed on both

sides of the disk

As the shaft rotates, the holes in the diskinterrupt the light beam

These light pulses are counted thus monitoring the rotation of the

shaft

The more notches, the higher the resolution of the encoder

One notch, only complete rotations can be counted


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CS 491/691(X) - Lecture 5 13

General Encoder Properties

Encoders are active sensors

Produce and measure a wave

function of light intensityThe wave peaks are counted to compute the speed

of the shaft

Encoders measure rotational velocity and position


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CS 491/691(X) - Lecture 5 14

Color-Based Encoders

Use a reflectance sensors to count the rotationsPaint the disk wedges in alternating contrastingcolors

Black wedges absorb light, white reflect it and onlyreflections are counted


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CS 491/691(X) - Lecture 5 15

Uses of Encoders

Velocity can be measured at a driven (active) wheel

at a passive wheel (e.g., dragged behind a legged robot)

By combining position and velocity information, onecan: move in a straight line

rotate by a fixed angle

Can be difficult due to wheel and gear slippage andto backlash in geartrains


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CS 491/691(X) - Lecture 5 16

Q uadrature Shaft EncodingHow can we measure direction of rotation?

Idea: Use two encoders instead of one

Align sensors to be 90 degrees out of phase

Compare the outputs of both sensors at eachtime step with the previous time step

Only one sensor changes state (on/off) at eachtime step, based on the direction of the shaftrotation this determines the direction of

rotation A counter is incremented in the encoder that

was on


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CS 491/691(X) - Lecture 5 17

Which Direction is the Shaft Moving?

Encoder A = 1 and Encoder B = 0

If moving to position AB=00,the position count is

incremented If moving to the position

AB=11, the position count isdecremented

State transition table:

Previous state = current stateno change in position

Single-bit changeincrementing / decrementing thecount

Double-bit change illegaltransition


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CS 491/691(X) - Lecture 5 18

Uses of Q SE in RoboticsRobot arms with complex joints e.g., rotary/ball joints like knees or

shoulders

Cartesian robots, overhead cranes

The rotation of a long worm screwmoves an arm/rack back and fortalong an axis

Copy machines, printers

ElevatorsMotion of robot wheels

Dead-reckoning positioning


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CS 491/691(X) - Lecture 5 19

Ultrasonic Distance SensingS onars: so(und) na(vigation) r (anging)Based on the time-of-flight principleThe emitter sends a chirp of sound

If the sound encounters a barrier it reflects back tothe sensor

The reflection is detected by a receiver circuit,tuned to the frequency of the emitter D istance to objects can be computed by measuringthe elapsed time between the chirp and the echoSound travels about 0.89 milliseconds per foot


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CS 491/691(X) - Lecture 5 20

Sonar SensorsE mitter is a membrane that transforms mechanicalenergy into a ping (inaudible sound wave)

The receiver is a microphone tuned to thefrequency of the emitted sound

Polaroid Ultrasound Sensor Used in a camera to measure the

distance from the camera to the subject

for auto-focus system Emits in a 30 degree sound cone

Has a range of 32 feet

Operates at 50 KHz


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CS 491/691(X) - Lecture 5 21

EcholocationE cholocation = finding location based on sonar Numerous animals use echolocation

Bats use sound for:

finding pray, avoid obstacles, find mates,communication with other bats

Dolphins/Whales:find small fish, swim through mazes

Natural sensors are much more complex thanartificial ones


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CS 491/691(X) - Lecture 5 22

Specular Reflection

Sound does not reflect directly and come right backSpecular reflection The sound wave bounces off multiple sources before

returning to the detector

Smoothness The smoother the surface the more likely is that the sound

would bounce off

Incident angle The smaller the incident angle of the sound wave the

higher the probability that the sound will bounce off


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CS 491/691(X) - Lecture 5 23

Improving Accuracy

Use rough surfaces in lab environments

Multiple sensors covering the same area

Multiple readings over time to detect discontinuities

Active sensing

In spite of these problems sonars are used

successfully in robotics applications Navigation

Mapping


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CS 491/691(X) - Lecture 5 24

Laser Sensing

High accuracy sensor Lasers use light time-of-flightLight is emitted in a beam (3mm) rather than a cone

Provide higher resolution

For small distances light travels faster than it can bemeasured use phase-shift measurement

SICK LMS200

360 readings over an 180-degrees, 10HzDisadvantages: cost, weight, power, price

mostly 2D


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CS 491/691(X) - Lecture 5 25

Visual Sensing

Cameras try to model biological eyesMachine visionis a highly difficult research area Reconstruction

What is that? Who is that? Where is that?Robotics requires answers related to achievinggoals Not usually necessary to reconstruct the entire world

Applications Security, robotics (mapping, navigation)


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CS 491/691(X) - Lecture 5 26

Principles of Cameras

Camerashave many similarities with the human eye The light goes through an opening ( iris - lens) and hits the

image plane (retina ) The retina is attached to light-sensitive elements ( rods,

cones silicon circuits ) Only objects at a particular range are

in focus ( fovea ) depth of field 512x512 pixels ( cameras ),120x10 6 rods and 6x10 6 cones ( eye )

The brightness is proportional to the

amount of light reflected from the objects


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CS 491/691(X) - Lecture 5 27

Image Brightness

Brightness depends on reflectance of the surface patch

position and distribution of the light sourcesin the environment

amount of light reflected from other objectsin the scene onto the surface patch

Two types of reflection Specular (smooth surfaces)

Diffuse (rough sourfaces)

Necessary to account for theseproperties for correct objectreconstruction complex computation


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CS 491/691(X) - Lecture 5 28

Early VisionThe retina is attached to numerous rods and cones which, inturn, are attached to nerve cells (neurons )The nerves process the information; they perform "earlyvision", and pass information on throughout the brain to do

"higher-level" vision processingThe typical first step ("early vision") is edge detection, i.e., findall the edges in the image

Suppose we have a b&w camera with a 512 x 512 pixel image

Each pixel has an intensity level between white and black

How do we find an object in the image? Do we know ifthere is one?


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CS 491/691(X) - Lecture 5 29

Edge Detection

Edge= a curve in the image across whichthere is a change in brightnessFinding edges Differentiate the image and look for areas

where the magnitude of the derivative is largeDifficulties Not only edges produce changes in brightness:

shadows, noise

Smoothing Filter the image using convolution Use filters of various orientations

Segmentation: get objects out of the lines


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CS 491/691(X) - Lecture 5 30

Model-Based Vision

Compare the current image with images of similar objects(models ) stored in memory

Models provide prior information about the objects

Storing models

Line drawings

Several views of the same object

Repeatable features (two eyes, a nose, a mouth)

Difficulties

Translation, orientation and scale

Not known what is the object in the image

Occlusion


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CS 491/691(X) - Lecture 5 31

Vision from Motion

Take advantage of motion to facilitate visionStatic system can detect moving objects Subtract two consecutive images from each other the

movement between frames

Moving system can detect static objects At consecutive time steps continuous objects move as one

Exact movement of the camera should be known

Robots are typically moving themselves Need to consider the movement of the robot


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CS 491/691(X) - Lecture 5 32

Stereo Vision

3D information can becomputed from two

images

Compute relativepositions of cameras

Compute disparity

displacement of a point in3D between the two images

Disparity is inverse proportional with actual distancein 3D


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CS 491/691(X) - Lecture 5 33

Biological Vision

Similar visual strategies are used in natureModel-based vision is essential for object/people

recognition

Vestibular occular reflex Eyes stay fixed while the head/body is moving to stabilize

the image

Stereo vision Typical in carnivores

Human vision is particularly good at recognizingshadows, textures, contours, other shapes


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CS 491/691(X) - Lecture 5 34

Vision for Robots

If complete scene reconstruction is not needed wecan simplify the problem based on the taskrequirements

Use colorUse a combination of color and movementUse small images

Combine other sensors with visionUse knowledge about the environment


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CS 491/691(X) - Lecture 5 35

Examples of Vision-Based Navigation

Running QRIO Sony Aibo obstacle avoidance


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CS 491/691(X) - Lecture 5 36

Readings

F. Martin: Chapter 6

M. Matari : 9

Robotics 10

Documents

Transcript of Robotics 10