topics: introduction to robotics cs 491/691(x) lecture 5 instructor: monica nicolescu
Post on 19-Dec-2015
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TRANSCRIPT
CS 491/691(X) - Lecture 5 2
Review
• Sensors
– Simple, complex
– Proprioceptive, exteroceptive
• Switches
• Light sensors
• Polarized light sensors
• Resistive position sensors
• Potentiometers
• Reflective optosensors
CS 491/691(X) - Lecture 5 3
Reflective Optosensors
• Include a source of light emitter (light emitting diodes LED) and a light detector (photodiode or phototransistor)
• Two arrangements, depending on the positions of the emitter and detector– Reflectance sensors: Emitter and detector
are side by side; Light reflects from the object back into the detector
– Break-beam sensors: The emitter and detector face each other; Object is detected if light between them is interrupted
CS 491/691(X) - Lecture 5 4
Calibration
• Ambient / background light can interfere with the sensor
measurement
• The ambient light level should be subtracted to get only the
emitter light level
• Calibration: the process of adjusting a mechanism so as to
maximize its performance
• Ambient light can change sensors need to be calibrated
repeatedly
• Detecting ambient light is difficult if the emitter has the same
wavelength
– Adjust the wavelength of the emitter
CS 491/691(X) - Lecture 5 5
Infra Red (IR) Light
• IR light works at a frequency different than ambient
light
• 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
CS 491/691(X) - Lecture 5 6
Modulation/Demodulation
• Modulated IR is commonly
used for communication
• Modulation is done by flashing the light source at a
particular frequency
• This signal is detected by a demodulator tuned to
that particular frequency
• Offers great insensitivity to ambient light
– Flashes of light can be detected even if weak
CS 491/691(X) - Lecture 5 7
Infrared Communication• Bit 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 a
0 or 1
– Common in commercial use
– Useful when it is difficult to control the exact shape of the waveform
CS 491/691(X) - Lecture 5 8
Proximity Sensing
• Ideal application for modulated/demodulated
IR light sensing
• Light from the emitter is reflected back into
detector 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
CS 491/691(X) - Lecture 5 9
Break Beam Sensors
• Any pair of compatible emitter-detector devices
can 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
track of shaft rotation
CS 491/691(X) - Lecture 5 10
Shaft Encoding
• Shaft encoders
– Measure the angular rotation of a shaft or an axle
• Provide position and velocity information about the
shaft
• Speedometers: measure how fast the wheels are
turning
• Odometers: measure the number of rotations of the
wheels
CS 491/691(X) - Lecture 5 11
Measuring Rotation
• A perforated disk is mounted on the shaft
• An emitter–detector pair is placed on both
sides of the disk
• As the shaft rotates, the holes in the disk
interrupt 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
CS 491/691(X) - Lecture 5 12
General Encoder Properties
• Encoders are active sensors
• Produce and measure a wave
function of light intensity
• The wave peaks are counted to compute the speed
of the shaft
• Encoders measure rotational velocity and position
CS 491/691(X) - Lecture 5 13
Color-Based Encoders
• Use a reflectance sensors to count the rotations
• Paint the disk wedges in alternating contrasting
colors
• Black wedges absorb light, white reflect it and only
reflections are counted
CS 491/691(X) - Lecture 5 14
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, one
can:
– move in a straight line
– rotate by a fixed angle
• Can be difficult due to wheel and gear slippage and
to backlash in geartrains
CS 491/691(X) - Lecture 5 15
Quadrature Shaft Encoding
• How 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 each
time step with the previous time step
– Only one sensor changes state (on/off) at each
time step, based on the direction of the shaft
rotation this determines the direction of
rotation
– A counter is incremented in the encoder that
was on
CS 491/691(X) - Lecture 5 16
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 is
decremented
State transition table:
• Previous state = current state
no change in position
• Single-bit change incrementing
/ decrementing the count
• Double-bit change illegal
transition
CS 491/691(X) - Lecture 5 17
Uses of QSE in Robotics
• Robot arms with complex joints
– e.g., rotary/ball joints like knees or
shoulders
• Cartesian robots, overhead cranes
– The rotation of a long worm screw
moves an arm/rack back and fort
along an axis
• Copy machines, printers
• Elevators
• Motion of robot wheels
– Dead-reckoning positioning
CS 491/691(X) - Lecture 5 18
Ultrasonic Distance Sensing
• Sonars: so(und) na(vigation) r(anging)
• Based on the time-of-flight principle
• The emitter sends a “chirp” of sound
• If the sound encounters a barrier it reflects back to the sensor
• The reflection is detected by a receiver circuit, tuned to the frequency of the emitter
• Distance to objects can be computed by measuring the elapsed time between the chirp and the echo
• Sound travels about 0.89 milliseconds per foot
CS 491/691(X) - Lecture 5 19
Sonar Sensors
• Emitter is a membrane that transforms mechanical energy into a “ping” (inaudible sound wave)
• The receiver is a microphone tuned to the frequency 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
CS 491/691(X) - Lecture 5 20
Echolocation
• Echolocation = 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 than
artificial ones
CS 491/691(X) - Lecture 5 21
Specular Reflection
• Sound does not reflect directly and come right back
• Specular 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
CS 491/691(X) - Lecture 5 22
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
CS 491/691(X) - Lecture 5 23
Laser Sensing• High accuracy sensor
• Lasers use light time-of-flight
• Light is emitted in a beam (3mm) rather than a cone
• Provide higher resolution
• For small distances light travels faster than it can be measured use phase-shift measurement
• SICK LMS200 – 360 readings over an 180-degrees, 10Hz
• Disadvantages: – cost, weight, power, price
– mostly 2D
CS 491/691(X) - Lecture 5 24
Visual Sensing
• Cameras try to model biological eyes
• Machine vision is a highly difficult research area
– Reconstruction
– What is that? Who is that? Where is that?
• Robotics requires answers related to achieving
goals
– Not usually necessary to reconstruct the entire world
• Applications
– Security, robotics (mapping, navigation)
CS 491/691(X) - Lecture 5 25
Principles of Cameras
• Cameras have 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),
120x106 rods and 6x106 cones (eye)
– The brightness is proportional to the
amount of light reflected from the objects
CS 491/691(X) - Lecture 5 26
Image Brightness
• Brightness depends on– reflectance of the surface patch
– position and distribution of the light sources in the environment
– amount of light reflected from other objects in the scene onto the surface patch
• Two types of reflection– Specular (smooth surfaces)
– Diffuse (rough sourfaces)
• Necessary to account for these properties for correct object reconstruction complex computation
CS 491/691(X) - Lecture 5 27
Early Vision
• The retina is attached to numerous rods and cones which,
in turn, are attached to nerve cells (neurons)
• The nerves process the information; they perform "early vision", and pass information on throughout the brain to do
"higher-level" vision processing
• The typical first step ("early vision") is edge detection, i.e., find
all 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 if there is one?
CS 491/691(X) - Lecture 5 28
Edge Detection• Edge = a curve in the image across which
there is a change in brightness
• Finding edges– Differentiate the image and look for areas where
the magnitude of the derivative is large
• Difficulties– 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
CS 491/691(X) - Lecture 5 29
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
CS 491/691(X) - Lecture 5 30
Vision from Motion
• Take advantage of motion to facilitate vision
• Static 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
CS 491/691(X) - Lecture 5 31
Stereo Vision
• 3D information can be
computed from two
images
• Compute relative
positions of cameras
• Compute disparity
– displacement of a point in
3D between the two images
• Disparity is inverse proportional with actual distance
in 3D
CS 491/691(X) - Lecture 5 32
Biological Vision
• Similar visual strategies are used in nature
• Model-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 recognizing
shadows, textures, contours, other shapes
CS 491/691(X) - Lecture 5 33
Vision for Robots
• If complete scene reconstruction is not needed we
can simplify the problem based on the task
requirements
• Use color
• Use a combination of color and movement
• Use small images
• Combine other sensors with vision
• Use knowledge about the environment
CS 491/691(X) - Lecture 5 34
Examples of Vision-Based Navigation
Running QRIO Sony Aibo – obstacle avoidance