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Transcript of LEGO Mindstorms NXT Carnegie Mellon Gabriel J. Ferrer Dacta Lego Timothy Friez Anjum Gupta Miha...
![Page 1: LEGO Mindstorms NXT Carnegie Mellon Gabriel J. Ferrer Dacta Lego Timothy Friez Anjum Gupta Miha Štajdohar Anjum Gupta Group: Roanne Manzano Eric Tsai Jacob.](https://reader036.fdocuments.net/reader036/viewer/2022062515/56649d205503460f949f589a/html5/thumbnails/1.jpg)
LEGO Mindstorms NXTLEGO Mindstorms NXT
Carnegie Mellon Gabriel J. FerrerDactaLegoTimothy FriezMiha Štajdohar Anjum GuptaAnjum GuptaGroup: Roanne ManzanoGroup: Roanne ManzanoEric TsaiEric TsaiJacob RobisonJacob Robison
SOURCES:SOURCES:
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Introductory Introductory programming programming
robotics projectsrobotics 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 don’t:
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Project 1: Motors and Project 1: Motors and Sensors (1)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– ifif statements statements
• Must touch the sensor at exactly the right time
– whilewhile loops 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 => go backward– Back touch sensor hit => go forward
• Braitenberg vehicles and state-machine based robots
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Project 2: Mobile robot and Project 2: Mobile robot and rotation sensors (1)rotation sensors (1)
• Physics of rotational motion
• Introduction of the rotation sensors– Built into the motors
• Balance wheel power– If left counts < 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 3Project 3
Line Line FollowingFollowing
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Line Following1. Use light sensors to follow a line in the least
time2. Design and programming challenge3. Uses looping or repeating programs4. Robots appear to be ‘thinking’
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The „line following” project
1. Objectives :2. Build a mobile robot and program it to follow a
line3. Make the robot go „as fast as possible”
4. Challenges :5. Different lines (large, thin, continuous, with gaps,
sharp turns, line crossings, etc…)6. Control algorithms for 1, 2 and 3 sensors7. Real time, changing environment8. Learning, adaptation9. Fault tolerance, error recovery
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Different control algorithms for different lines (large and thin line)
RC
X
RC
X
RC
X
RC
X
RC
X
RC
X
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Different control algorithms for 1 and 3 sensors…
RC
X
RC
X
RC
X
RC
X
RC
X RC
X
RC
X
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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, …)
• Battery’s 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
• Robot’s learning and adaptation to the changing environment
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The ChallengesThe Challenges
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Project 4: Drawing Project 4: Drawing robotrobot
• 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 Project 5: Finding objects (1)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 robotsOther Projects with mobile robots1. “Theseus”
– Store path (from line following) in an array– Backtrack when array fills
2. Robotic forklift– Finds, retrieves, delivers an object
3. Perimeter security robot– Implemented using RCX– 2 light sensors, 2 touch sensors
4. Wall-following robot– Build a rotating mount for the sonar
5. Quantum Braitenberg Robots of Arushi Raghuvanshi
6. 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 LogicProject 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 => leftSlowbiasRight => leftSlow– biasLeft => rightSlowbiasLeft => rightSlow– biasNone => leftFastbiasNone => leftFast– biasNone => rightFastbiasNone => 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:Left wheel:– (leftSlow * repSlow + leftFast * repFast) / (leftSlow +
leftFast)
• Right wheelRight wheel is symmetric
• Defuzzified values are motor power levels
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Project 7. Q-LearningProject 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-valuesQ-values– 2D array (indexed by state and
action)– Expected rewards for performing
actions
Action1= strike
action2 action3 action4
State happy 0.3
State unhappy
State angry
State hungry
State bored
Q-valuesQ-values
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Q-Learning Main Loop
1. Select action2. Change motor speeds3. Inspect sensor values
1. Calculate updated state2. Calculate reward
4. Update Q values5. 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 => highest value
• Reward decreases with increasing disparity
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Updating Q-values
Q[oldState][action] =
Q[oldState][action] +
learningRate *
(reward + discount * maxQ(currentState) - Q[oldState][action])
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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. Project 8. Sumo and similar Sumo and similar
fighting fighting competitionscompetitions
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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-AutonomousSumo-Autonomous
• Robots push each other out of the ring• A ‘real’ competition• Require light sensors• Encourages efficient, robust designs• Power isn’t 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 Other Challenge PossibilitiesPossibilities
• 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
![Page 51: LEGO Mindstorms NXT Carnegie Mellon Gabriel J. Ferrer Dacta Lego Timothy Friez Anjum Gupta Miha Štajdohar Anjum Gupta Group: Roanne Manzano Eric Tsai Jacob.](https://reader036.fdocuments.net/reader036/viewer/2022062515/56649d205503460f949f589a/html5/thumbnails/51.jpg)
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/forums
• www.chiefdelphi.com – useful community website for getting all other FIRST related questions answered
• Any questions: Post to forums, or e-mail me at [email protected]