LEGO Mindstorms NXT
SOURCES:
Carnegie Mellon
Gabriel J. Ferrer
Dacta
Lego
Timothy Friez
Miha Štajdohar
Anjum Gupta
Group: Roanne Manzano
Eric Tsai
Jacob Robison
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
Beginning activities
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Bridge
Tower
LEGO Man
Organizing Pieces
Naming Pieces
Programming Robot People
Robots by instructions
Teaching Ideas
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Teach mini-lessons as necessary
These spin:
Gears- Power vs. Speed
Transmission of energy/motion
Using fasteners
These don’t:
Worm Gears
Building with bricks vs. building machines
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
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
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
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 < right counts
• Increase left wheel power
• Race through obstacle course
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
Project 3
Line
Following
Line Following
1. Use light sensors to follow a line in the least
time
2. Design and programming challenge
3. Uses looping or repeating programs
4. Robots appear to be ‘thinking’
The „line following” project
1. Objectives :
2. Build a mobile robot and program it to follow a
line
3. 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 sensors
7. Real time, changing environment
8. Learning, adaptation
9. Fault tolerance, error recovery
Different control algorithms for different
lines (large and thin line)
Different control algorithms for 1 and 3
sensors…
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…
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
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
The Challenges
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
Pen-Drawer Robot
Pen-Drawer Robot
Project 5: Finding
objects (1)
• Finding objects
• Light sensor
– Find a line
• Sonar sensor
– Find an object
– Find free space
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
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
Light Sensor/Sonar Robot
Other Projects with mobile robots
“Theseus”
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Store path (from line following) in an array
Backtrack when array fills
Robotic forklift
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Finds, retrieves, delivers an object
Perimeter security robot
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Implemented using RCX
2 light sensors, 2 touch sensors
Wall-following robot
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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
Robot Forklift
Gearing the motors
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
Fuzzy Rules for Balancing Rotation
Counts
• Inference rules:
– biasRight => leftSlow
– biasLeft => rightSlow
– biasNone => leftFast
– biasNone => rightFast
• Inference is trivial for this case
– Fuzzy membership/defuzzification is more
interesting
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
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
Project 7. Q• Discrete sets ofLearning
states and
actions
– States form an N-dimensional
array
• Unfolded into one dimension in
practice
– Individual actions selected on
each time step
Action
1=
strike
State
happy
action2
action3
0.3
State
unhap
py
State
angry
State
hungry
• Q-values
– 2D array (indexed by state and
action)
– Expected rewards for performing
actions
State
bored
Q-values
action4
Q-Learning Main Loop
1. Select action
2. Change motor speeds
3. Inspect sensor values
1. Calculate updated state
2. Calculate reward
4. Update Q values
5. Set “old state” to be the updated state
Calculating the State (Motors)
• For each motor:
– 100% power
– 93.75% power
– 87.5% power
• Six motor states
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
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
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
Calculating Reward
• No disparity => highest value
• Reward decreases with increasing
disparity
Updating Q-values
Q[oldState][action] =
Q[oldState][action] +
learningRate *
(reward + discount * maxQ(currentState) Q[oldState][action])
Student Exercises
• Assess performance of wheel-balancer
• Experiment with different constants
– Learning rate
– Discount
– Epsilon
• Alternative reward function
– Based on change in disparity
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
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
Project 8.
Sumo and similar
fighting
competitions
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
Drag Race
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Least amount of time to cross a set distance
Straight, light fast designs
Teaches gearing, efficiency
Nice contrast to Tug O’ War
Little programming
Sprint Rally
• Cross the table and return,
attempting to stay within the
designated path.
• Challenging programming
• Possibly uses sensors
• Teaches precision,
programming logic,
prediction
Sumo-Autonomous
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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
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
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
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]
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