1. LEGO Mindstorms NXT SOURCES: Carnegie Mellon Gabriel J. Ferrer Dacta
Timothy Friez
Miha Štajdohar Anjum Gupta
Group: Roanne Manzano
Eric Tsai
Jacob Robison

2. 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

3. Beginning activities Bridge Tower LEGO Man Organizing Pieces
Naming Pieces
Programming Robot People
Robots by instructions

4. 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:

5. 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

6. First Project (2) Introduce the touch sensor Interesting problem
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

7. 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

8. 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

9. 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

10. Project 3
Line Following

11. 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’

12. 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
The “line following” project
start stop 2x for 1 sec <> stat stop for 2 sec
In the simple “line following” project, a mobile robot has to follow a line (a thin “guiding line” or large like a road), as fast as possible. The line has different color than the background which can be detected by 1, 2 or 3 light insensity sensors.
Student find easilly several control algorithm for the robot but they have to consider the real time constraints too, a fast moving robot may not have enough time to read the sensors, process the data and control the robot.
Trying to go fast result the line is lost sometimes (in some experiment we voluntary set up a situation where the line is not continuos, has small gaps, or the line turns wery sharply). Robot (controlling program) has to deal with these situations. have to use robust, fault tolerant, “error recovering” techniques, or even have to learn.
Stopping and going back where line was last seen is the very first recovering solution. A more complex control program don't stop immediately (when the line is lost) but try to continue to the “estimated line direction” (the program memorize which side the line was last seen and try to go that direction) “hoping” the the line will be found. If the line is not found in a certain amount of time the robot control program may switch to an “active search” mode where the robot try to find a line looking for both directions, going to bigger and bigger circles.
Lightning conditions changes, line color (shade of the robot on the line) changing...
robot reduce speed, turn sharper...
Real time limitations/problems in communication

13. Different control algorithms for different lines (large and thin line)
RCX
RCX
RCX
Egyszerű nyelvezettel fejtse ki mondanivalóját!
A bizonyítékokat feltárhatja mind szóbeli, mind képi eszközökkel
Erősítse meg az elmondottakat egy konkrét eset ismertetésével!
Alkalmazzon átvezetést vagy átmenetet a napirendi pontok között!
RCX
RCX
RCX

14. Different control algorithms for 1 and 3 sensors…
RCX
RCX
RCX
RCX
Egyszerű nyelvezettel fejtse ki mondanivalóját!
A bizonyítékokat feltárhatja mind szóbeli, mind képi eszközökkel
Erősítse meg az elmondottakat egy konkrét eset ismertetésével!
Alkalmazzon átvezetést vagy átmenetet a napirendi pontok között!
RCX
RCX
RCX

15. 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…

16. 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
Stop, go back where line last was seen (and try again…)
Continue (and hope) to find the line
Continue and search for the line
Time limits…
Adaptive threshold and variable speed turning based on the amount of turning needed
Acceleration
…memorize the track on a closed circuit…

17. 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
Programming languages
State machine
Multi tasking

18. The Challenges

19. 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

20. Pen-Drawer Robot

21. Pen-Drawer Robot

22. Project 5: Finding objects (1)
Light sensor
Find a line
Sonar sensor
Find an object
Find free space

23. 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

24. 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

25. Light Sensor/Sonar Robot

26. 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

27. Robot Forklift

28. Gearing the motors

29. 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

30. 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

31. 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

32. Defuzzification Use representative values: Left wheel:
Slow = 0
Fast = 100
Left wheel:
(leftSlow * repSlow + leftFast * repFast) / (leftSlow + leftFast)
Right wheel is symmetric
Defuzzified values are motor power levels

33. Project 7. Q-Learning Discrete sets of states and actions Q-values
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
Action1= strike
action2
action3
action4
State happy
0.3
State unhappy
State angry
State hungry
State bored
Q-values

34. 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

35. Calculating the State (Motors)
For each motor:
100% power
93.75% power
87.5% power
Six motor states

36. 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

37. 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

38. 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

39. Calculating Reward No disparity => highest value
Reward decreases with increasing disparity

40. Updating Q-values Q[oldState][action] = Q[oldState][action] +
learningRate *
(reward + discount * maxQ(currentState) - Q[oldState][action])

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

42. 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

43. 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

44. Project 8. Sumo and similar fighting competitions

45. 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

46. 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

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

48. Sumo-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

49. 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

50. 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

51. Final Notes Slides available on-line:
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