1. Lego Robots We built many robots from Lego kits.
We experimented with drives
We experimented with sensors
We experimented with “architectures’

2. You can buy old Lego for 10-40 dollars in USA

6. DRIVE SYSTEMS ‘differential steering’ contrast to steering wheel
balance
other options: tanks, bicycles, etc

7. Drive System ratios and reduction—why? gears:
pro: efficient, accurate, strong
con: need precise alignment, backlash
pulleys:
pro: simplicity, flexibility
con: slippage, poor durability
motor control: speed, direction, braking
note: Connector direction matters .

8. Example Vehicle Design
Differentially-steered vehicle with pulley drive
See handout for construction details

9. More advanced controls in microprocessors and FPGAs

10. A Brief History of Robotics

11. A Brief History of Robotics
Robotics grew out of the fields of control theory, cybernetics and AI
Robotics, in the modern sense, can be considered to have started around the time of cybernetics (1940s)
Early AI had a strong impact on how it evolved (1950s-1970s), emphasizing reasoning and abstraction, removal from direct situatedness and embodiment
In the 1980s a new set of methods was introduced and robots were put back into the physical world

12. Why we apply Control Theory in robotics?
The mathematical study of the properties of automated control systems
Helps understand the fundamental concepts governing all mechanical systems (steam engines, aeroplanes, etc.)
Feedback: measure state and take an action based on it
Idea: continuously feeding back the current state and comparing it to the desired state, then adjusting the current state to minimize the difference (negative feedback).
The system is said to be self-regulating
E.g.: thermostats
if too hot, turn down, if too cold, turn up

13. Control Theory through History
Thought to have originated with the ancient Greeks
Time measuring devices (water clocks), water systems
Forgotten and rediscovered in Renaissance Europe
Heat-regulated furnaces (Drebbel, Reaumur, Bonnemain)
Windmills
James Watt’s steam engine (the governor)

14. Cybernetics Pioneered by Norbert Wiener in the 1940s
Comes from the Greek word “kibernts” – governor, steersman
Combines principles of control theory, information science and biology
Sought principles common to animals and machines, especially with regards to control and communication
Studied the coupling between an organism and its environment

15. Hammond and Miessner automaton: Seleno
1515
WHO WAS REALLY FIRST?
Cybernetic Robotics
Hammond and Miessner automaton: Seleno
(1914)
A phototropic dog
Roberto Cordeschi, The Discovery of the Artificial, Kluwer Academic Pub. 2002

16. W. Grey Walter’s Tortoise
“Machina Speculatrix” (1953)
1 photocell, 1 bump sensor, 2 motor, 3 wheels, 1 battery
Behaviors:
seek light
head toward moderate light
back from bright light
turn and push
recharge battery
Uses reactive control, with behavior prioritization

17. W. Grey Walter’s Tortoise

18. Robot is able to exhibit a food-searching instinct
Grey W. Walter makes use of psychological terms when he describes his machine as:
"spontaneous,"
"purposeful,"
"independent"
Robot is able to "recognize" itself and other individuals of the same species.
Robot is able to exhibit a food-searching instinct
(it automatically went towards its 'nest' to recharge its batteries when they were about to run out).
Walter admitted that all this could be seen as nothing but a series of tricks.
Nonetheless, an outside observer, let us say a biologist, would have used just this mentalistic terminology had he witnessed this behavior in real animals.

19. Grey W. Walter: Machina speculatrix
Cybernetic Robotics
A photosensible tortoise
Grey W. Walter: Machina speculatrix
(1950)
It is nice to be a pioneer?
Do you want to be a technology pioneer?

20. Principles of Walter’s Tortoise
Parsimony
Simple is better
Exploration or speculation
Never stay still, except when feeding (i.e., recharging)
Attraction (positive tropism)
Motivation to move toward some object (light source)
Aversion (negative tropism)
Avoidance of negative stimuli (heavy obstacles, slopes)
Discernment
Distinguish between productive/unproductive behavior (adaptation)

21. Artificial Intelligence Robots

22. Artificial Intelligence
Officially born in 1955 at Dartmouth University
Marvin Minsky, John McCarthy, Herbert Simon
Intelligence in machines
Internal models of the world
Search through possible solutions
Plan to solve problems
Symbolic representation of information
Hierarchical system organization
Sequential program execution

23. AI and Robotics AI influence to robotics:
Knowledge and knowledge representation are central to intelligence
Perception and action are more central to robotics
New solutions developed: behavior-based systems
“Planning is just a way of avoiding figuring out what to do next” (Rodney Brooks, 1987)
Distributed AI (DAI)
Society of Mind (Marvin Minsky, 1986): simple, multiple agents can generate highly complex intelligence
First robots were mostly influenced by AI (deliberative)

24. Shakey and others At Stanford Research Institute (late 1960s)
A deliberative system
Visual navigation in a very special world
STRIPS planner
Vision and contact sensors

25. Early AI Robots: HILARE
Late 1970s
At LAAS in Toulouse
Video, ultrasound, laser rangefinder
Was in use for almost 2 decades
One of the earliest hybrid architectures
Multi-level spatial representations

26. Early Robots: CART/Rover
Hans Moravec’s early robots
Stanford Cart (1977) followed by CMU rover (1983)
Sonar and vision

27. Basic idea of Braitenberg Vehicles reminder and new concepts

28. Background
In Vehicles, Valentino Braitenberg describes a set of thought experiments in which increasingly complex vehicles are built from simple mechanical and electronic components.
Braitenberg uses these thought experiments to explore psychological ideas and the nature of intelligence.
Throughout the book, we see more intricate behaviors emerge from the interaction of simple component parts and simple behaviors

29. Vehicles: Experiments in synthetic psychology (1984)
V. Braitenberg
Vehicles: Experiments in synthetic psychology (1984)
“…We will tempted, then, to use psychological language in describing their behavior.
And yet we know very well there is nothing in these vehicles that we have not put in ourselves.
This will be an interesting educational game”
V. Braitenberg - Vehicles: Experiments in synthetic psychology, Cambridge, MA; MIT Press, 1984

30. Braitenberg Vehicles Valentino Braitenberg (1980) Thought experiments
Use direct coupling between sensors and motors
Simple robots (“vehicles”) produce complex behaviors that appear very animal, life-like
Excitatory connection
The stronger the sensory input, the stronger the motor output
Light sensor  wheel: photophilic robot (loves the light)
Inhibitory connection
The stronger the sensory input, the weaker the motor output
Light sensor  wheel: photophobic robot (afraid of the light)

31. Example Vehicles
Wide range of vehicles can be designed, by changing the connections and their strength
Vehicle 1:
One motor, one sensor
Vehicle 2:
Two motors, two sensors
Excitatory connections
Vehicle 3:
Inhibitory connections
Vehicle 1
Being “ALIVE”
“FEAR” and “AGGRESSION”
Vehicle 2
“LOVE”

32. V. Braitenberg -
Vehicles: Experiments in synthetic psycology, Cambridge, MA; MIT Press, 1984
The vehicle's sensors exert an inhibitory influence on the motors.
Vehicle A, turns toward the light source and stops, when it is dose enough to the light source. i.e. as soon as the light stimulation is large enough to exert sufficient inhibitory activation.
Vehicle B is similarly inhibited, but it moves away from the source.
inhibitory

33. + + vehicle 2B: coward vehicle 3A: love excitatory
straight excitatory connections
vehicle 3A: love
straight inhibitory connections + +
off on
vehicle 2A: aggresive
crossed excitatory connections
excitatory

34. MotorTest.java Modes of operation Single motor vehicle
import josx.platform.rcx.*;
class MotorTest {
static final int STOP = 0;
static final int RUN = 1;
static final int FLOAT = 2;
static int mode = STOP;
static int power = 0;
public static void main(String [] args) {
setupButtonListeners();
while (true) {
if (mode == RUN) {
Motor.A.setPower( power ); // power in range [0, 7]. incremented with each press of View button.
Motor.A.forward();
} else if (mode == STOP) {
Motor.A.stop();
} else if (mode == FLOAT) {
Motor.A.flt(); }
... (button listener code not shown)
Modes of operation
Single motor vehicle

35. Mindstorms Light Sensor
why the LED?
sensor mode: raw vs. percent
I/0 graph, compared to mammal eyes
spectral response
sunlight vs incandescent vs fluorescent

36. LightTest.java button LCD import josx.platform.rcx.*;
class LightTest implements SensorConstants {
public static void main(String [] args) throws InterruptedException {
Sensor.S1.setTypeAndMode (SENSOR_TYPE_LIGHT, SENSOR_MODE_PCT);
Sensor.S1.activate();
while (true) {
int lightReading;
if (Button.VIEW.isPressed()) {
lightReading = Sensor.S1.readRawValue();
} else {
lightReading = Sensor.S1.readValue(); }
LCD.showNumber( lightReading );
button
LCD

37. Complete Example: “Aggressive.java”
import josx.platform.rcx.*;
class aggressive implements SensorConstants {
public static void main(String [] args) {
int minBrightness = 100;
final int gain = 12;
Sensor.S1.setTypeAndMode (SENSOR_TYPE_LIGHT, SENSOR_MODE_PCT);
Sensor.S1.activate();
Sensor.S3.setTypeAndMode (SENSOR_TYPE_LIGHT, SENSOR_MODE_PCT);
Sensor.S3.activate();
for (int i = 0; i < 100; i++) {
if (Sensor.S1.readValue() < minBrightness) {
minBrightness = Sensor.S1.readValue();
} else if (Sensor.S3.readValue() < minBrightness) {
minBrightness = Sensor.S3.readValue(); }
Thread.sleep(20);
Motor.A.forward();
Motor.C.forward();
while (true) {
int motorASpeed = (Sensor.S3.readValue() - minBrightness) / gain;
int motorCSpeed = (Sensor.S1.readValue() - minBrightness) / gain;
setMotorSpeed(Motor.A, motorASpeed);
setMotorSpeed(Motor.C, motorCSpeed);
aggressive
Sensors S1 and S3

38. Aggressive.java (continued)
protected static void setMotorSpeed(Motor m, int motorSpeed) {
if (motorSpeed < 1) {
m.flt(); // important
LCD.showNumber(-1);
} else {
if (motorSpeed > 7) {
motorSpeed = 7; }
m.forward();
m.setPower(motorSpeed);
LCD.showNumber(motorSpeed);
motor floats
Limit motor speed
motor forward

39. Observations
Closed loop control; lessens importance of mechanical imperfections (e.g. pulley slip).
The map is not the territory.
Make your own—robots and observations!

40. Intelligent, Emotional behavior and the origins of Robot’s Control
Each of these imaginary vehicles in some way mimics intelligent behavior.
Each of them is given a name that corresponds to the behavior that it imitates;
– ”Fear”, ”Love”, ”Logic”, etc.
Our robots will have names such as « Quantum John » or « Fearful Qubit » or « Fuzzy Mary » to emphasize their brains and not only emotions.

41. Simple Creatures

42. Simple Creatures Timid
the shadow seeker
one motor
one threshold light sensor, pointing up, on when in light and off in shadow.
Timid will run when it can ”see” the room lights, and stop when it cannot.
When the lights are turned on, Timid drives until it gets into shadow, at which point it stops

43. Simple Creatures Indecisive
the shadow edge finder
one motor
one threshold light sensor, pointing up, on when in light and off in shadow.
Indecisive drives forward until it gets to a shadow.
At this point, the threshold light sensor no longer sees the overhead lights and its output switches of.
The motor reverses and the creature runs back into the light.
Indecisive oscillates back and forth at shadow edges.

44. Simple Creatures Paranoid
the shadow-fearing robot
two motors
one threshold light sensor, pointing up, mounted on an arm sticking out forward, on when in light and off in shadow.
Paranoid drives straight forward until its threshold light sensor enters a shadow.
Then, triggered by the sensor, its left wheel reverses.
It swings around to the left until the protruding sensor has swung back out of the shadow.
At this point the left wheel returns to forward motion.

45. Simple Creatures Dogged
– the obstacle avoider
one motor
two touch sensors, one facing front and one back, each connected to a bumper. or
flip-flop
When Dogged is started it runs either forward or backward.
When either the front or back bumper is pressed, the creature reverses direction.
Dogged changes direction every time either bumper gets pressed.
It will fall into a pattern of running quickly back and forth between two objects.

46. Insecure Simple Creatures
– the wall follower
two motors
whisker sensor, on when sufficiently bent and off otherwise (implement using a touch sensor and a strip).
inverter
Insecure slowly edges its way along walls and around the base of pillars.

47. Driven Simple Creatures
– the light seeker
two motors
differential light sensor, facing forward (implent it with two light sensors)
Driven moves towards a bright light by successive right and left turns.
It slowly wiggles its way towards light sources.

48. More Complex Creatures

49. MULTISENSORY
T:Temperature
O:Oxygen
F:Food L R T O F
L:Light

50. More Complex Creatures
Multi-sensory
Persistent
– the light seeker with a collision algorithm
Attractive and Repulsive
– the leading and following pair
Consistent
– the four state turtle
Inhumane
– the mouse trap

51. Philosophical Creatures
Frantic
– the negative feedback loop
Observant
– the creature sensitive to the direction of a sound

52. Idea of “Generalized Braitenberg Vehicles”

53. Generalized Braitenberg Vehicles
Other designs of the vehicle itself.
Various types of drives.
Braitenberg Head, Braitenberg Mister Potatao, Braitenberg Snake. All kinds of “BraitenbergSomething”.
Very many sensors
Very many effectors
Complex brains
Combinational logic replaces with state machines.
State machines replaced with Petri Nets and Regular Expressions

54. Generalized Braitenberg Vehicles
State machines replaced with Harel State Charts
Adding probability
Adding Fuzzy Control
Adding Multiple-Valued Control
Adding Modal Logic Controls
Programming in LISP
Reconfigurable change shape of body

55. ENVIRONMENT ENVIRONMENT sensors Combinational Block
actuators
Generalized Braitenberg Robot
ENVIRONMENT
sensors
Combinational
Block
actuators
memory
Braitenberg Automaton Robot
ENVIRONMENT

56. ENVIRONMENT actuators Fuzzy Combinational Block Fuzzy Memory sensors
F/Q
Quantum
Combinational
Block
Q/F
Quantum memory
ENVIRONMENT

57. Original “Braitenberg Vehicles” are Wheeled Mobile Robots
Wheeled Mobile Robot (WMR)
A robot capable of locomotion on a surface solely through the actuation of wheel assemblies mounted on the robot and in contact with the surface.
A wheel assembly is a device which provides or allows motion between its mount and surface on which it is intended to have a single point of rolling contact.
Muir and Newman, 1986
What is the best drive system and mechanical design of a mobile robot?

58. Drives of “Generalized Braitenberg Vehicles”

59. Assumptions 1. The robot is built from rigid mechanisms.
2. The robot contains at most one steering link per wheel.
3. All steering axes are perpendicular to the floor.
4. No slip occurs in the orthogonal direction of rolling (non-slipping).
5. No translational slip occurs between the wheel and the floor (pure rolling).
Non-slipping and pure rolling

60. Examples of mobile robots
Suitable to make the model
Smooth motion
Risk of slipping
Bi- wheel type robot
Exact straight motion
Robust to slipping
Inexact modeling of turning
Caterpillar type robot
Free motion
Complex structure
Weakness of the frame
Omnidirectional robot

61. Subsumption Architecture of Brooks implemented in BV

62. A simplified version of an elementary Brooks architecture
PATHPLAN
Level 2
SUPPRESS
WANDER
Level 1
SENSORS
FORWARD
TURN
MOTOR
AVOID
Level 0
subsumption

63. In this case, take the direction pointed at by Light. F
ENVIRONMENT F j O w L l M T t
If both Temperature sensors are active, then go forward; otherwise go away from the direction pointed at by either one of them unless there is Light.
In this case, take the direction pointed at by Light.
You do this, unless the Oxygen sensors are active.
In such a case, always go their direction unless Food is present.
If this is the case, always go in that direction.

64. IMPLY(X,Y) if X occurs then apply Y
UNLESS(X, Z, Y) if X occurs then apply Y unless Z occurs M O w F j L l T t
ENVIRONMENT
IMPLY(F, )
UNLESS(O,  , )
UNLESS(L,    , )
UNLESS(T,      , )

65. Readings
Vehicles: Experiments in Synthetic Psychology, Valentino Braitenberg, MIT Press; Reprint edition (1986), ISBN:
Braitenberg Creatures, D. W. Hogg, F. Martin, M. Resnick. E&L Memo No 13, MIT Media Lab. Cambridge, MA,

66. Slides and Ideas Used Ernesto Burattini Jong Hwan Kim Edmond Schonberg
Davi Geiger
Krister Wolf
Roberto Cordeschi
Guglielmo Tamburrini
Arushi Raghuvanshi