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EN1002 - Engenharia Unificada I Utilizando o LEGO Mindstorms Centro de Engenharia, Modelagem e Ciências Sociais Aplicadas.

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Presentation on theme: "EN1002 - Engenharia Unificada I Utilizando o LEGO Mindstorms Centro de Engenharia, Modelagem e Ciências Sociais Aplicadas."— Presentation transcript:

1 EN1002 - Engenharia Unificada I Utilizando o LEGO Mindstorms Centro de Engenharia, Modelagem e Ciências Sociais Aplicadas

2 Outline NXT capabilities Software development options Introductory programming projects Advanced programming projects

3 The LEGO Mindstorms® is a robotic building system consisting of: The NXT Intelligent Brick: the brain of the system Sensors and servo motors LEGO TECHNIC Elements Programming software Lego Mindstorms NXT

4 The system: PC with USB Bluetooth adapter, running control LabVIEW software and an NXT sensor- equipped robotic vehicle Project Implementation

5 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

6 Sensors and Motors Four sensor ports Sonar Sound Light Touch Three motor ports Each motor includes rotation counter

7 Touch Sensors Education kit includes two sensors Much more robust than old RCX touch sensors

8 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

9 Sound Sensor Analogous to light sensor Reports intensity Reputed to identify tones I haven’t experimented with this Practical uses “Clap” to signal robot

10 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”

11 Motors Configured in terms of percentage of available power Built-in rotation sensors 360 counts/rotation

12 Software development options Onboard programs RobotC leJOS NXC/NBC Remote control iCommand NXT_Python

13 RobotC Commercially supported Not entirely free of bugs Poor static type checking Nice IDE Custom firmware Costly $50 single license $250/12 classroom computers

14 Example RobotC Program void forward() { motor[motorA] = 100; motor[motorB] = 100; } void spin() { motor[motorA] = 100; motor[motorB] = -100; }

15 Example RobotC Program task main() { SensorType[S4] = sensorSONAR; forward(); while(true) { if (SensorValue[S4] < 25) spin(); else forward(); }

16 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

17 Example leJOS Program sonar = new UltrasonicSensor(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(); }

18 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

19 Event-driven example class Go implements Behavior { private Ultrasonic sonar = new Ultrasonic(SensorPort.S4); public boolean takeControl() { return sonar.getDistance() > 25; }

20 Event-driven example public void action() { Motor.A.forward(); Motor.B.forward(); } public void suppress() { Motor.A.stop(); Motor.B.stop(); }

21 Event-driven example class Spin implements Behavior { private Ultrasonic sonar = new Ultrasonic(SensorPort.S4); public boolean takeControl() { return sonar.getDistance() <= 25; }

22 Event-driven example public void action() { Motor.A.forward(); Motor.B.backward(); } public void suppress() { Motor.A.stop(); Motor.B.stop(); }

23 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(); }

24 NXC/NBC NBC (NXT Byte Codes) Assembly-like language with libraries NXC (Not eXactly C) Built upon NBC Successor to NQC project for RCX Compatible with standard firmware es/

25 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 Big problems with latency Each Bluetooth transmission: 30 ms Sonar alone requires three transmissions Decent program: 1-2 Hz

26 NXT_Python Remote control via Python on/ on/ Similar pros/cons with iCommand

27 Developing a Remote Control API Bluetooth library for Java Opening a Bluetooth connection Typical address: 00:16:53:02:e5:75 Bluetooth URL btspp://00165302e575:1; authenticate=false;encrypt=false

28 Opening the Connection import*; import*; StreamConnection con = (StreamConnection)“btspp:…”); InputStream is = con.openInputStream(); OutputStream os = con.openOutputStream();

29 NXT Protocol Key files to read from iCommand:

30 An Interesting Possibility Programmable cell phones with cameras are available Camera-equipped cell phone could provide computer vision for the NXT

31 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

32 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

33 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

34 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

35 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

36 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"); }

37 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

38 Pen-Drawer Robot


40 Fourth Project (1) Finding objects Light sensor Find a line Sonar sensor Find an object Find freespace

41 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

42 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

43 Light Sensor/Sonar Robot

44 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

45 Robot Forklift

46 Gearing the motors

47 Advanced programming projects From a 300-level AI course Fuzzy logic Reinforcement learning

48 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

49 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

50 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

51 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

52 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

53 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

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

55 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

56 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

57 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

58 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

59 Calculating Reward No disparity => highest value Reward decreases with increasing disparity

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

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

62 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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