LEGO Mindstorms NXT

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LEGO Mindstorms NXT
SOURCES
Carnegie Mellon Gabriel J. Ferrer Dacta Lego Timo
thy Friez Miha Štajdohar Anjum Gupta Group
Roanne Manzano Eric Tsai Jacob Robison
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Introductory programming robotics projects

• Developed for a zero-prerequisite course
• Most students are not ECE or CS majors
• 4 hours per week
• 2 meeting times
• 2 hours each
• Students build robot outside class

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Beginning activities

• Bridge
• Tower
• LEGO Man
• Organizing Pieces
• Naming Pieces
• Programming Robot People
• Robots by instructions

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Teaching Ideas

• Teach mini-lessons as necessary
• Gears- Power vs. Speed
• Transmission of energy/motion
• Using fasteners
• Worm Gears
• Building with bricks vs. building machines

These spin
These dont
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Project 1 Motors and Sensors (1)

• Introduce motors
• Drive with both motors forward for a fixed time
• Drive with one motor to turn
• Drive with opposing motors to spin
• Introduce subroutines
• Low-level motor commands get tiresome
• Simple tasks
• Program a path (using time delays) to drive
  through the doorway

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First Project (2)

• Introduce the touch sensor
• if statements
• Must touch the sensor at exactly the right time
• while loops
• Sensor is constantly monitored
• Interesting problem
• Students try to put code in the loop body
• e.g. set the motor power on each iteration
• Causes confusion rather than harm

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First Project (3)

• Combine infinite loops with conditionals
• Enables programming of alternating behaviors
• Front touch sensor hit gt go backward
• Back touch sensor hit gt go forward
• Braitenberg vehicles and state-machine based
  robots

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Project 2 Mobile robot and rotation sensors (1)

• Physics of rotational motion
• Introduction of the rotation sensors
• Built into the motors
• Balance wheel power
• If left counts lt right counts
• Increase left wheel power
• Race through obstacle course

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Second Project (2)

• if (/ Write a condition to put here /)
• nxtDisplayTextLine(2, "Drifting left")
• else if (/ Write a condition to put here /)
• nxtDisplayTextLine(2, "Drifting right")
• else
• nxtDisplayTextLine(2, "Not drifting")

Complete this code with various conditions and
various motions
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Project 3

• Line Following

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Line Following

• Use light sensors to follow a line in the least
  time
• Design and programming challenge
• Uses looping or repeating programs
• Robots appear to be thinking

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The line following project

• Objectives
• Build a mobile robot and program it to follow a
  line
• Make the robot go as fast as possible
• Challenges
• Different lines (large, thin, continuous, with
  gaps, sharp turns, line crossings, etc)
• Control algorithms for 1, 2 and 3 sensors
• Real time, changing environment
• Learning, adaptation
• Fault tolerance, error recovery

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Different control algorithms for different lines
(large and thin line)
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Different control algorithms for 1 and 3 sensors
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The used techniques and knowledge (1)

• Real time constraints appear when the robot goes
  as fast as possible
• Sensor reading and information processing speed
• Motor-robot inertia, wheel slipping
• Fault tolerant, error recovery techniques are
  used when
• Unreliable sensor values
• Inaccurate surface
• Loosing the line

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The used techniques and knowledge (2)

• Initial calibration and adaptation are used in
  the changing environment
• Changes in the light intensity of the line (room
  lamps, robot shade, )
• Batterys charge
• Learning techniques can be used to determine
• How fast the robot can go (acceleration on long
  straight lines)
• How sharply the robot should turn
• How to avoid endless repetitions

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Educational benefits of the line following
project

• Students confronted, used and learned
• Real time constraints
• Robust, fault tolerant control algorithms
• Error recovery techniques
• Robots learning and adaptation to the changing
  environment

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The Challenges
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Project 4 Drawing robot

• Pen-drawer
• First project with an effector
• Builds upon lessons from previous projects
• Limitations of rotation sensors
• Slippage problematic
• Most helpful with a limit switch
• Shapes (Square, Circle)
• Word (LEGO

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Pen-Drawer Robot
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Pen-Drawer Robot
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Project 5 Finding objects (1)

• Finding objects
• Light sensor
• Find a line
• Sonar sensor
• Find an object
• Find free space

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Fourth Project (2)

• Begin with following a line edge
• Robot follows a circular track
• Always turns right when track lost
• Traversal is one-way
• Alternative strategy
• Robot scans both directions when track lost
• Each pair of scans increases in size

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Fourth Project (3)

• Once scanning works, replace light sensor reading
  with sonar reading
• Scan when distance is short
• Finds freespace
• Scan when distance is long
• Follow a moving object

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Light Sensor/Sonar Robot
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Other Projects with mobile robots

• Theseus
• Store path (from line following) in an array
• Backtrack when array fills
• Robotic forklift
• Finds, retrieves, delivers an object
• Perimeter security robot
• Implemented using RCX
• 2 light sensors, 2 touch sensors
• Wall-following robot
• Build a rotating mount for the sonar
• Quantum Braitenberg Robots of Arushi Raghuvanshi
• Maze Robots of Stefan Gebauer and Fuzzy robots of
  Chris Brawn

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Robot Forklift
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Gearing the motors
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Project 6 Fuzzy Logic

• Implement a fuzzy expert system for the robot to
  perform a task
• Students given code for using fuzzy logic to
  balance wheel encoder counts
• Students write fuzzy experts that
• Avoid an obstacle while wandering
• Maintain a fixed distance from an object

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Fuzzy Rules for Balancing Rotation Counts

• Inference rules
• biasRight gt leftSlow
• biasLeft gt rightSlow
• biasNone gt leftFast
• biasNone gt rightFast
• Inference is trivial for this case
• Fuzzy membership/defuzzification is more
  interesting

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Fuzzy Membership Functions

• Disparity leftCount - rightCount
• biasLeft is
• 1.0 up to -100
• Decreases linearly down to 0.0 at 0
• biasRight is the reverse
• biasNone is
• 0.0 up to -50
• 1.0 at 0
• falls to 0.0 at 50

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Defuzzification

• Use representative values
• Slow 0
• Fast 100
• Left wheel
• (leftSlow repSlow leftFast repFast) /
  (leftSlow leftFast)
• Right wheel is symmetric
• Defuzzified values are motor power levels

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Project 7. Q-Learning

• Discrete sets of states and actions
• States form an N-dimensional array
• Unfolded into one dimension in practice
• Individual actions selected on each time step
• Q-values
• 2D array (indexed by state and action)
• Expected rewards for performing actions

Q-values
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Q-Learning Main Loop

• Select action
• Change motor speeds
• Inspect sensor values
• Calculate updated state
• Calculate reward
• Update Q values
• Set old state to be the updated state

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Calculating the State (Motors)

• For each motor
• 100 power
• 93.75 power
• 87.5 power
• Six motor states

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Calculating the State (Sensors)

• No disparity STRAIGHT
• Left/Right disparity
• 1-5 LEFT_1, RIGHT_1
• 6-12 LEFT_2, RIGHT_2
• 13 LEFT_3, RIGHT_3
• Seven total sensor states
• 63 states overall

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Action Set for Balancing Rotation Counts

• MAINTAIN
• Both motors unchanged
• UP_LEFT, UP_RIGHT
• Accelerate motor by one motor state
• DOWN_LEFT, DOWN_RIGHT
• Decelerate motor by one motor state
• Five total actions

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Action Selection

• Determine whether action is random
• Determined with probability epsilon
• If random
• Select uniformly from action set
• If not random
• Visit each array entry for the current state
• Select action with maximum Q-value from current
  state

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Calculating Reward

• No disparity gt highest value
• Reward decreases with increasing disparity

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Updating Q-values

• QoldStateaction
• QoldStateaction
• learningRate
• (reward discount maxQ(currentState) -
  QoldStateaction)

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Student Exercises

• Assess performance of wheel-balancer
• Experiment with different constants
• Learning rate
• Discount
• Epsilon
• Alternative reward function
• Based on change in disparity

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Learning to Avoid Obstacles

• Robot equipped with sonar and touch sensor
• Hitting the touch sensor is penalized
• Most successful formulation
• Reward increases with speed
• Big penalty for touch sensor

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Other classroom possibilities

• Operating systems
• Inspect, document, and modify firmware
• Programming languages
• Develop interpreters/compilers
• NBC an excellent target language
• Supplementary labs for CS1/CS2

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Project 8. Sumo and similar fighting competitions
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The Tug O War

• Robots pull on opposite ends of a 2 foot string
• There are limits on mass,motors, and certain
  wheels
• Teaches integrity, torque, gearing, friction
• Good challenge for beginners
• Very little programming

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Drag Race

• Least amount of time to cross a set distance
• Straight, light fast designs
• Teaches gearing, efficiency
• Nice contrast to Tug O War
• Little programming

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Sprint Rally

• Cross the table and return, attempting to stay
  within the designated path.
• Challenging programming
• Possibly uses sensors
• Teaches precision, programming logic, prediction

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Sumo-Autonomous

• Robots push each other out of the ring
• A real competition
• Require light sensors
• Encourages efficient, robust designs
• Power isnt everything
• Designs must predict unknown opponents

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Sumo-Remote

• Uses another RCX or tethered sensors to control
• Do not use Mindstorms remote
• Like BattleBots
• Still requires programming
• Driver skill is a factor

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Other Challenge Possibilities

• Weight lifting, obstacle course, tightrope
  walking, soccer, maze navigation, Dancing, golf,
  bipedal locomotion, tractor pull, and many more
• Cooperative Robots
• Component Design
• Time-limited robot design
• See the website, find more on the internet, or
  create your own
• Create Specific rules
• Predict loopholes

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Final Notes

• Slides available on-line
• http//ozark.hendrix.edu/ferrer/presentations/
• Make sure to check back with www.robotc.net for
  updates and support.
• Join the robotc.net forums at www.robotc.net/forum
  s
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  for getting all other FIRST related questions
  answered
• Any questions Post to forums, or e-mail me at
  frc-support_at_robotc.net