SensorActuator Networks Braitenburg Vehicles

1
Sensor-Actuator Networks(Braitenburg Vehicles)

• Experiments in Synthetic Psychology
• OR
• Steps toward really artifical life
• Norm Badler
• Steve Lane
• CSE 377

2
General Structure
Actuators Decision_function(Sensors)
Decision_function
Sensors
Actuators
Environment
3
A Simple Example
V k(S) Velocity is a linear function of sensor
value
kS
S
VkS
Environment
4
Basic Braitenberg Vehicle Design

• Sensor/Actuator Pairs
• Light or other environment feature sensor(s)
• Motor(s) (wheels)
• Wiring

Vehicle 1
5
Two Motors Make it a Little More Interesting
(Left-Right Actuators)
Vleft kl(Sl) Vright kr(Sr) Velocity of
(left, right) actuators are linear functions of
two sensor values
kl(Sl) kr(Sr)
SlSr
Vleft kl(Sl) Vright kr(Sr)
Environment
6
A Little More Complex
Vleft kl(Sl) Vright kr(Sr) Velocity of
(left, right) actuators are linear functions of
two sensor values (but crossed)
kl(Sr) kr(Sl)
SlSr
Vleft kl(Sr) Vright kr(Sl)
Environment
7
Excitatory and Inhibitory Functions
Vleft kl(Sl) Vright kr(Sr) Functions may be
excitatory () or inhibitory (-) (essentially
reflects the slope of the function)
kl(Sl) kr(Sr)
SlSr
Vleft kl(Sl) Vright kr(Sr)
Environment
8
Fear Aggression
Vehicle 2
9
Exploring Love
Vehicle 3
10
Values Special Tastes Cont

• The outer 2 sensors are uncrossed excitatory
• The next pair in are crossed and excitatory
• The third pair are uncrossed and inhibitory like
  Sensor/Actuator Pairs
• The fourth pair are crossed and excitatory.

Vehicle 4
11
Values Special Tastes

• Dislikes high temperature (turns away from hot
  places.
• Dislikes light sources (turns toward them and
  destroys them.
• Prefers oxygenated environment containing organic
  matter
• Can move elsewhere when O2 food scarce.

Vehicle 4
12
VALUES, KNOWLEDGE INFERENCE

• From the outside you might conclude that Vehicle
  4 has
• a system of VALUES
• Dislikes high temperatures
• Dislikes light sources
• Prefers environments with food sources
• KNOWLEDGE of its environment and
• an INFERENCE ability
• Light bulbs are a source of heat
• If I destroy them then I will be cooler
• Oxygen organic matter make energy

13
But Whats Really Going On?

• Intelligence implies the ability to acquire,
  represent and process information
• There was no such acquisition or representation
  of information here.
• In constructing Vehicle 4 we were just playing
  with the wiring between sensors and actuators
• The behavioral properties and responses that
  emerge may look intelligent but they really are
  not.
• When we analyze a system we tend to overestimate
  its complexity
• Anyone have pets?

14
Taking this Further
Vleft Fl(params,Sl,Sr) Vright
Fr(params,Sl,Sr) Velocity of (left, right)
actuators are non-linear, parametric functions of
two sensor values
Fl(params,Sl,Sr) Fr(params,Sl,Sr)
SlSr
Vleft Fl() Vright Fr()
Environment
15
Non-linear sensory responses
V speed of motor I intensity of stimulation
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Whats the Point?
The decision functions relate actuator
behaviors to the sensed environment. Can
generalize any component.
Hard-wired function Learned function
Eyes Ears Hunger
Wheels Legs Color Other internal state
Environment
17
Decisions ?
18
Sensor Scope ?
19
What (Who) is Being Sensed?

• Environment
• Check for obstacles, food sources, lights, etc.
• Beware zig-zag wall following
• Other nearby vehicles/creatures
• Check local environment for motion of neighbors,
  gives rise to flocking and herding behaviors.
• Boids

20
Some other links

• Vehicles Experiments in Synthetic Psychology, by
  Valentino Braitenberg (1984), Bradford Books, MIT
  Press, ISBN 0-262-02208-7
• Braitenberg demos
• Braitenberg Vehicles
• BEAST
• POPBUGS
• Gerken (Dewdwey article)
• Some implementation notes
• You can find a lot more if you Google

21
Conclusions

• The interaction of simple devices and systems can
  give rise to a variety of complex emergent
  behavior
• Many of these individual behaviors can be readily
  seen in animals such as insects, bees, ants, etc.
• love, fear, aggression, foraging, exploring, etc,
• Group behaviors also can be created in a similar
  manner
• Flocking, herding, schooling, etc.
• You can implement this for particular cases in
  your worlds.
• The computational model is scalable to multiple
  individuals (code re-use, parameterized).
• Lesson Learned Graphical Synthesis is a lot
  easier than Analysis!