Topics: Introduction to Robotics CS 491/691(X)

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Topics Introduction to RoboticsCS 491/691(X)

• Lecture 5
• Instructor Monica Nicolescu

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Review
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Ambient light

• Ambient / background light can interfere with the
  sensor measurement
• To correct it we need to subtract the ambient
  light level from the sensor measurement
• This is how
• take two (or more, for increased accuracy)
  readings of the detector, one with the emitter
  on, one with it off,
• then subtract them
• The result is the ambient light level

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Calibration

• 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

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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
• Detect presence of objects and distance to
  objects
• 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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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

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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

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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

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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?
• Laser beams and clever burglars in movies
• In robotics they are mostly used for keeping
  track of shaft rotation

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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

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Measuring Rotation

• A 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 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

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Color-Based Encoders

• Paint the disk wedges in alternating contrasting
  colors
• Use a reflectance sensors to count the rotations
• Black wedges absorb light, white reflect it and
  only reflections are counted

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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
• Where can encoders be used?

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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

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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

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Which Direction is the Shaft Moving?

• Encoder A 1 and Encoder B 0
• If moving to position AB00, the position count
  is incremented
• If moving to the position AB11, 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

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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
• Motion of robot wheels
• Dead-reckoning positioning
• Copy machines, printers
• Elevators

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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

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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

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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

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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

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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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Laser Sensing

• High accuracy sensors
• 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
• SIC LMS200
• 360 readings over an 180-degrees, 10Hz
• Disadvantages cost, weight, power, price, mostly
  2D

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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

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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

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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

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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
• Suppose we have a bw 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?
• The typical first step ("early vision") is edge
  detection, i.e., find all the edges in the image

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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

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Model-Based Vision

• Compare the current image with images of similar
  objects (models) stored in memory
• Models provide prior information about the
  objects
• Difficulties
• Translation, orientation and scale
• Not known what is the object in the image
• Occlusion
• Storing models
• Line drawings
• Several views of the same object
• Repeatable features (two eyes, a nose, a mouth)

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Vision from Motion

• Take advantage of motion to facilitate vision
• Moving system can detect static objects
• At consecutive time steps continuous objects move
  as one
• Subtract two consecutive images from each other ?
  the movement between frames
• 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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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

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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

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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

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Feedback Control

• Feedback control having a system achieve and
  maintain a desired state by continuously
  comparing its current and desired states, then
  adjusting the current state to minimize the
  difference
• Also called closed loop control

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Goal State

• Goal driven behavior is used in both control
  theory and in AI
• Goals in AI
• Achievement goals states that the system is
  trying to reach
• Maintenance goals states that need to be
  maintained
• Control theory mostly focused on maintenance
  goals
• Goal states can be
• Internal monitor battery power level
• External get to a particular location in the
  environment
• Combinations of both balance a pole

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Error

• Error the difference in the current state and
  desired state of the system
• The controller has to minimize the error at all
  times
• Zero/non-zero error
• Tells whether there is an error or not
• The least information we could have
• Magnitude of error
• The distance to the goal state
• Direction of error
• Which way to go to minimize the error
• Control is much easier if we know both magnitude
  and direction

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A Robotic Example

• Use feedback to design a wall following robot
• What sensors to use, what info will they provide?
• Contact the least information
• IR information about a possible wall, but not
  distance
• Sonar, laser would provide distance
• Bend sensor would provide distance
• Control
• If distance-to-wall is right, then keep going
• If distance-to-wall is larger
• then turn (by 45 degrees) toward the wall
• else turn (by 45 degrees) away from the wall

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Oscillations

• What is the behavior of the robot?
• The robot will oscillate around the optimal
  distance from the wall, getting either too close
  or too far
• In general, the behavior of a feedback system
  oscillates around the desired state
• Decreasing oscillations
• Adjust the turning angle
• Use a range instead of a fixed distance as the
  goal state

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Open Loop Control

• Also called feed-forward control
• Does not use sensory feedback, and state is not
  fed back into the system
• The system executes a command that is fed into
  it, based on what was predicted in advance
• The controller determines set points (sub-goals)
  ahead of time
• Feed-forward systems are effective only if
• They are well calibrated
• The environment is predictable does not change
  such as to affect their performance

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Uses of Open Loop Control

• Repetitive, state independent tasks
• Conveyor belt machines use open loop control
• Biological systems use it as well, in various
  movements
• These are called ballistic movements (like a
  flying bullet)
• Ballistic movements cannot be corrected while
  they are executed
• E.g. pouncing, reflex reaching withdrawal, etc.

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Types of Feedback Control

• There are three types of basic feedback
  controllers
• P proportional control
• PD proportional derivative control
• PID proportional integral derivative control

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Proportional Control

• The response of the system is proportional to the
  amount of the error
• Use both direction and magnitude of error to
  compensate for the error effectively
• The output o is proportional to the input i
• o Kp ? i
• Kp is a proportionality constant (gain)
• Turn sharply toward the wall sharply if far from
  it,
• Turn gently toward the wall if slightly farther
  from it

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Gains

• The gains (Kp) are specific to the particular
  control system
• The physical properties of the system directly
  affect the gain values
• E.g. the velocity profile of a motor (how fast
  it can accelerate and decelerate), the backlash
  and friction in the gears, the friction on the
  surface, in the air, etc.
• All of these influence what the system actually
  does in response to a command

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Determining Gains

• How do we determine the gains?
• Analytically (mathematics)
• require that the system be well understood and
  characterized mathematically
• Empirically (trial and error)
• require that the system be tested extensively
• Automatically
• by trying different values at run-time

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Gains and Oscillations

• Proportional gain value proportional to that of
  the error
• Incorrect gains will cause the system to
  undershoot or overshoot the desired state ?
  oscillations
• Gain values determine if
• The system will keep oscillating (possibly
  increasing oscillations)
• The system will stabilize
• Damping process of systematically decreasing
  oscillations
• A system is properly damped if it does not
  oscillate out of control (decreasing
  oscillations, or no oscillations at all)

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Proportional Control

• Pgain10
• System overshot the zero point, and had to turn
  around
• Offset Error System did not stabilize at the
  goal. Power command too small to activate the
  motor
• Pgain20 should ameliorate the offset problem
• Offset error is solved
• Oscillation the system overshoots three
  timestwice beyond the setpoint and once before it

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Proportional Control

• Pgain30
• Oscillation problem is more pronounced there are
  a total of five oscillatory swings
• Pgain50 Oscillation behavior has taken over
• System cannot stabilize at the setpoint
• A small error generates a power command that
  moves the system across the setpoint

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Derivative Control

• Setting gains is difficult, and simply increasing
  the gains does not remove oscillations
• The system needs to be controlled differently
  when it is close to the desired state and when it
  is far from it
• Otherwise, the momentum of the correction carries
  the system beyond the desired state, and causes
  oscillations
• Solution correct the momentum as the system
  approaches the desired state
• Momentum mass ? velocity

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Controlling Velocity

• Momentum and velocity are directly proportional ?
  we can control the momentum by controlling
  velocity
• As the system nears the desired state, we
  subtract an amount proportional to the velocity
  
• - (gain ? velocity)
• This is called the derivative term, because
  velocity is the derivative of position
• A controller that has a derivative term is called
  a derivative (D) controller

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Derivative Control

• A derivative controller has an output o
  proportional to the derivative of its input i
• o Kd di/dt
• Kd is a proportionality constant
• The intuition behind derivative control
• Controller corrects for the momentum as it
  approaches the desired state
• Slow down a robot and decrease the turning angle
  while getting closer to the desired state

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Readings

• F. Martin
• M. Mataric