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Using Lego Mindstorms NXT in the Classroom Gabriel J. Ferrer Hendrix College ferrer@hendrix.edu http://ozark.hendrix.edu/~ferrer/ Outline NXT capabilities Software development options Introductory programming projects Advanced programming projects Purchasing NXT Kits

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using lego mindstorms nxt in the classroom

Using Lego Mindstorms NXT in the Classroom

Gabriel J. Ferrer

Hendrix College

ferrer@hendrix.edu

http://ozark.hendrix.edu/~ferrer/

outline
Outline
  • NXT capabilities
  • Software development options
  • Introductory programming projects
  • Advanced programming projects
purchasing nxt kits
Purchasing NXT Kits
  • Two options (same price; $250/kit)
    • Standard commercial kit
    • Lego Education kit
      • http://www.lego.com/eng/education/mindstorms/
  • Advantages of education kit
    • Includes rechargeable battery ($50 value)
    • Plastic box superior to cardboard
    • Extra touch sensor (2 total)
  • Standard commercial kit
    • Includes NXT-G visual language
nxt brick features
NXT Brick Features
  • 64K RAM, 256K Flash
  • 32-bit ARM7 microcontroller
  • 100 x 64 pixel LCD graphical display
  • Sound channel with 8-bit resolution
  • Bluetooth radio
  • Stores multiple programs
    • Programs selectable using buttons
sensors and motors
Sensors and Motors
  • Four sensor ports
    • Sonar
    • Sound
    • Light
    • Touch
  • Three motor ports
    • Each motor includes rotation counter
touch sensors
Touch Sensors
  • Education kit includes two sensors
  • Much more robust than old RCX touch sensors
light sensor
Light Sensor
  • Reports light intensity as percentage
  • Two modes
    • Active
    • Passive
  • Practical uses
    • Identify intensity on paper
    • Identify lit objects in dark room
    • Detect shadows
sound sensor
Sound Sensor
  • Analogous to light sensor
    • Reports intensity
    • Reputed to identify tones
      • I haven’t experimented with this
  • Practical uses
    • “Clap” to signal robot
ultrasonic sonar sensor
Ultrasonic (Sonar) Sensor
  • Reports distances
    • Range: about 5 cm to 250 cm
    • In practice:
      • Longer distances result in more missed “pings”
  • Mostly reliable
    • Occasionally gets “stuck”
    • Moving to a new location helps in receiving a sonar “ping”
motors
Motors
  • Configured in terms of percentage of available power
  • Built-in rotation sensors
    • 360 counts/rotation
software development options
Software development options
  • Onboard programs
    • RobotC
    • leJOS
    • NXC/NBC
  • Remote control
    • iCommand
    • NXT_Python
robotc
RobotC
  • Commercially supported
    • http://www.robotc.net/
  • Not entirely free of bugs
  • Poor static type checking
  • Nice IDE
  • Custom firmware
  • Costly
    • $50 single license
    • $250/12 classroom computers
example robotc program
Example RobotC Program

void forward() {

motor[motorA] = 100;

motor[motorB] = 100;

}

void spin() {

motor[motorA] = 100;

motor[motorB] = -100;

}

example robotc program14
Example RobotC Program

task main() {

SensorType[S4] = sensorSONAR;

forward();

while(true) {

if (SensorValue[S4] < 25) spin();

else forward();

}

}

lejos
leJOS
  • Implementation of JVM for NXT
  • Reasonably functional
    • Threads
    • Some data structures
    • Garbage collection added (January 2008)
    • Eclipse plug-in just released (March 2008)
  • Custom firmware
  • Freely available
    • http://lejos.sourceforge.net/
example lejos program
Example leJOS Program

sonar = newUltrasonicSensor(SensorPort.S4);

Motor.A.forward();

Motor.B.forward();

while (true) {

if (sonar.getDistance() < 25) {

Motor.A.forward();

Motor.B.backward();

} else {

Motor.A.forward();

Motor.B.forward();

}

}

event driven control in lejos
Event-driven Control in leJOS
  • The Behavior interface
    • boolean takeControl()
    • void action()
    • void suppress()
  • Arbitrator class
    • Constructor gets an array of Behavior objects
      • takeControl() checked for highest index first
    • start() method begins event loop
event driven example
Event-driven example

class Go implements Behavior {

private Ultrasonic sonar =

new Ultrasonic(SensorPort.S4);

public boolean takeControl() {

return sonar.getDistance() > 25;

}

event driven example19
Event-driven example

public void action() {

Motor.A.forward();

Motor.B.forward();

}

public void suppress() {

Motor.A.stop();

Motor.B.stop();

}

}

event driven example20
Event-driven example

class Spin implements Behavior {

private Ultrasonic sonar =

new Ultrasonic(SensorPort.S4);

public boolean takeControl() {

return sonar.getDistance() <= 25;

}

event driven example21
Event-driven example

public void action() {

Motor.A.forward();

Motor.B.backward();

}

public void suppress() {

Motor.A.stop();

Motor.B.stop();

}

}

event driven example22
Event-driven example

public class FindFreespace {

public static void main(String[] a) {

Behavior[] b = new Behavior[]

{new Go(), new Stop()};

Arbitrator arb =

new Arbitrator(b);

arb.start();

}

}

nxc nbc
NXC/NBC
  • NBC (NXT Byte Codes)
    • Assembly-like language with libraries
    • http://bricxcc.sourceforge.net/nbc/
  • NXC (Not eXactly C)
    • Built upon NBC
    • Successor to NQC project for RCX
  • Compatible with standard firmware
    • http://mindstorms.lego.com/Support/Updates/
icommand
iCommand
  • Java program runs on host computer
  • Controls NXT via Bluetooth
  • Same API as leJOS
    • Originally developed as an interim project while leJOS NXT was under development
    • http://lejos.sourceforge.net/
  • Big problems with latency
    • Each Bluetooth transmission: 30 ms
    • Sonar alone requires three transmissions
    • Decent program: 1-2 Hz
nxt python
NXT_Python
  • Remote control via Python
    • http://home.comcast.net/~dplau/nxt_python/
  • Similar pros/cons with iCommand
developing a remote control api
Developing a Remote Control API
  • Bluetooth library for Java
    • http://code.google.com/p/bluecove/
  • Opening a Bluetooth connection
    • Typical address: 00:16:53:02:e5:75
  • Bluetooth URL
    • btspp://00165302e575:1; authenticate=false;encrypt=false
opening the connection
Opening the Connection

import javax.microedition.io.*;

import java.io.*;

StreamConnection con = (StreamConnection) Connector.open(“btspp:…”);

InputStream is = con.openInputStream();

OutputStream os = con.openOutputStream();

nxt protocol
NXT Protocol
  • Key files to read from iCommand:
    • NXTCommand.java
    • NXTProtocol.java
an interesting possibility
An Interesting Possibility
  • Programmable cell phones with cameras are available
  • Camera-equipped cell phone could provide computer vision for the NXT
introductory programming projects
Introductory programming projects
  • Developed for a zero-prerequisite course
  • Most students are not CS majors
  • 4 hours per week
    • 2 meeting times
    • 2 hours each
  • Not much work outside of class
    • Lab reports
    • Essays
first project 1
First Project (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
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
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
second project 1
Second Project (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
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");

}

third project
Third Project
  • 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”)
    • Arguably excessive
fourth project 1
Fourth Project (1)
  • Finding objects
  • Light sensor
    • Find a line
  • Sonar sensor
    • Find an object
    • Find freespace
fourth project 2
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
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
other projects
Other Projects
  • “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
advanced programming projects
Advanced programming projects
  • From a 300-level AI course
  • Fuzzy logic
  • Reinforcement learning
fuzzy logic
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
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
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
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
q learning
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 learning main loop
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
calculating the state motors
Calculating the State (Motors)
  • For each motor:
    • 100% power
    • 93.75% power
    • 87.5% power
  • Six motor states
calculating the state sensors
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
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
Action Selection
  • Determine whether action is random
    • Determined with probability epsilon
  • If random:
    • Select uniformly from action set
  • If not:
    • Visit each array entry for the current state
    • Select action with maximum Q-value from current state
q learning main loop57
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
calculating reward
Calculating Reward
  • No disparity => highest value
  • Reward decreases with increasing disparity
updating q values
Updating Q-values

Q[oldState][action] =

Q[oldState][action] +

learningRate *

(reward + discount * maxQ(currentState) - Q[oldState][action])

student exercises
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
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
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
thanks for attending
Thanks for attending!
  • Slides available on-line:
    • http://ozark.hendrix.edu/~ferrer/presentations/
  • Currently writing lab textbook
    • Introductory and advanced exercises
  • ferrer@hendrix.edu