Sensors Previous Lecture

Sensors CSCI545 Introduction to Robotics Hadi Moradi

Previous Lecture „

DC motors „ „ „ „ „ „ „ „ „

Inefficient Operating voltage Operating current Stall current Stall torque Gearing up and down Gear ratios PWM Servo motors vs. stepper motors

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Sensors „

Perception through sensors „

Contact: bump, switch

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Distance: Ultrasound, radar, infra red

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Light level: photo cells, cameras

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Sound level: microphone

Sensors „

Perception through sensors „

Strain: strain gauge

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Rotation: encoders

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Magnetism: compasses

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Smell: chemical

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Sensors „

Perception through sensors „

Temperature: thermal, infra red

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Inclination: inclinometers, gyroscopes

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Pressure: pressure gauges

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Altitude: altimeters …

Sensors „

Simple „

Contact switch

complex human retina

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The General Question „

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Given the sensory reading what was the world like?

Example: Skin

Levels of Processing „

A switch: „ „

open = 0 volts Closed = 5 volts

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A digital scale:

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Microphone:

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Camera:

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Proprioception „

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Sensing information „

Proprioception:

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Exteroception:

Examples of proprioception

Sensor Fusion „

Combining multiple sensors Difficulties:

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E Example: l Human H brain b i

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Magnetic Field Sensor of Baby Loggerhead Sea Turtles „ „ „

Field Inclination Angle Field Intensity Neuron sensors in the brain?

http://faculty.washington.edu /chudler/magtur.html http://news.nationalgeographic.com/news/ 2001/10/1012_TVanimalnavigation.html

Magnetic Field Sensor of Baby Loggerhead Sea Turtles

http://www.unc.edu/depts/oceanweb/turtles/ Research by Dr. Kenneth Lohmann

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Ohm’s Law „

V= IR „ „ „

V =voltage I =current R = resistance

(volts) (Amps) (Ohms)

Switch Sensors „

Open vs vs. closed

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Light Sensors „

A variable resistor that changes based on the light. Brighter light => low resistance darker light => Higher resistance

The Importance of shielding „

Note: Shielding, position, and directionality of the photocells are important.

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Resistive Position Sensors „

Originally developed for video game control.

Bend Sensor

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Potentiometers „ „

Volume control in your stereo Typically called pots

Example

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Example

Reflective Opto-sensors „ „

Emitter and detector Emitter: „

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LED

Detector: „ „

Photodiode Phototransistor

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Photodiode vs. Photoresistor „ „

Photoresistor: Photodiode/phototransistor: „

Phototransistor vs. Photodiode:

Applications „ „ „

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object presence detection object distance detection surface feature detection (finding/following markers/tape) wall/boundary tracking rotational shaft encoding

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Sensor limitations „

Light g reflectivity: y „

Surface color

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Texture Ambient light: How to overcome the ambient light?

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Sensor calibration

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=> Partially observable

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Break Beam Sensors

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Any pair of compatible emitterdetector devices can be used to make a break-beam sensor Examples:

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Where have you seen these?

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Shaft Encoding „

Measure angular rotation

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Example: „ „

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Speedometer: p speed p of rotation Odometer: number of rotations

Q: What happens if there is only one notch in the disk?

An Example

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Quadrature Shaft Encoder

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Clockwise rotation signal

Output Signal

cw ccw

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Modulation and Demodulation of Light „ „

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Problem: Ambient light Solution: Example: Home remote control Usage: g

Modulation and Demodulation of Light

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Proximity Sensing „

The distance to a nearby object „

Just the return of signal

Distance Sensing

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Infra Red (IR) Sensors „ „

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Infra red part of the spectrum Used like break beam and reflectance sensors Advantage

Time of Flight „

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Emitter: send a chirp Collector: Receives the bounce back Elapsed time „

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1.12 feet/ms

Called

echolocation

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Bats

Man Made Example „

Used to map undersea surface

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Undersea Mapping

Picture from Bluefin Robotics

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Problem 1: Multiple Reflections „

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Which reflection gets back earlier? Which reflection should be used for calculation?

Object 2

Object 1 Sonar

Problem 2: Specular Reflection „

Graze the surface and bounce off

Object 2

Object 1 Sonar

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Problems

Other Usages: NavBelt

http://www.engin.umich.edu/research/mrl/00MoRob_19.html

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Navchair

http://www.engin.umich.edu/research/mrl/00MoRob_19.html

GuideCane

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GuideCane

Machine Vision „

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Problem: determine the objects in the environment (Understand the environment). Example: RoboCup

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The Physics of Vision

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Light goes through the iris Impinges retina

Camera Light Processing

A very simple processing: convert the image to a normal image

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Image Reconstruction „

Reconstruction: what was the world like that produced this image?

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Pixelizing the Image Plane „

pixels: picture cells „

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Each picture divided into small cells

Typical camera: 512 X 512 pixels Human eye: „

120 x 10^6 rods

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6 x 10^6 cones

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Image Brightness „

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Brightness: B i h proportional i l to the h amount off light directed toward the camera Brightness depends on: „

Patch Brightness „

Th brightness The bi h depends d d on: „ „

specular (bounce off the surface) diffuse (re-emitted)

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First Steps of Early Vision „

Example: „ „

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b&w camera 512 x 512 pixel image plane. intensity level between white and black

Question: „ „

Do we know if there is an object? How do we find an object in the image?

An Example

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Edge Detection „

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Edges: curves in the image plane with significant change in the brightness level

A simple approach: to look for sharp brightness changes:

Problem:

Example: Human Body Project

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Smoothing of Noise „ „

Noise: Small picks in differentiated image. image Eliminating noise: „

Finding Objects „

Step 2: Find objects among all those edges. edges Segmentation:

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Q Questions: ti

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How do we know which lines correspond to which objects, What makes an object?

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Finding Objects „

Use clues to detect objects. The math is hard...

Clues for Segmentation (1) „

Use stored models (model (model-based based vision) „

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Clues for Segmentation (1) „

MAKRO 1.1 drives to a T-shaped junction, measures its width, drives back, performs a turn, stops, drives back and performs a turn back into the main pipe. Second run, different point of view

Clues for Segmentation(2) „

Use motion (motion vision) „

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Clues for Segmentation(3) „

Use binocular stereopsis

(stereo vision) „

Clues for Segmentation(4)

Left image

Right image

Image after disparity

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Clues for Segmentation(5) „

Use texture

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Use shading shading, contours, … „

recover shape in a similar way as from texture

Complexity of Vision Sensing „

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Reconstruction:

If no need for reconstruction: „

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Si lif vision Simplify i i processing i

Q: What are some ways of doing that?

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Simplifying Vision „

Use color

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Use a smaller image plane (e.g., a line)

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Use other sensors to complement vision

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Use task-specific information

Question: Determine the object in this image

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Structured Light Vision „

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Project a light on a mirror and scan the area. You may avoid rotating motor and scan with a full surface.

Images courtesy of http://www.caligari.com/

Structured Light Vision „

Any object in the environment cuts the light.

Images courtesy of http://www.caligari.com/

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Structured Light Vision

A) The whole scene, B) The object w/o laser light, C) the difference

Images courtesy of http://www.caligari.com/

Structured Light Vision „

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Y= projection of the laser on the image plane H= height of the camera Question: How do you calculate r?

Images courtesy of http://www.caligari.com/

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