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Collective Decision Making
with Heterogeneous Agents
Swarm Intelligence
Fiscarelli Antonio Maria
Scenario
- Central room
- 4 rooms(colored leds)
- Light source
- Ground color
- Objects(green leds)
Robots
G-Robot :
- Ground sensor
L-Robot :
- Light sensor
All robots :
- Omnidirectional Camera
- Range and Bearing system
- Led
Goal
Collectively select the best room:
- VG: ground color quality
- VL: light intensity quality
- VO: number of objects quality
Main Idea
- Action state diagram
- Walking state diagram
Communication
- G-robots estmates color ground and send it to
L-robots
- L-robots estimates light intensity and send it
to L-robots
- All robots estimates number of objects in the
room
Tests
Tests have been performed using different
settings:
- Swarm size N in the range {20,25,30,35,40}
- Ratio p in the range {0.25, 0.5, 0.75}
where TG is the subpopulation of G-robots and
TL is the subpopulation of L-robots
Conclusion
Self organization:
- defining only the single robot's behavior, a
swarm behavior emerges.
Local interaction:
- in-room interaction
Partial knowledge:
- no global information shared
- no environment a priori information
- in-room memory
Robustness:
the swarm is still capable to reach its goal if
some robots stop working.
Flexibility:
the swarm is capable to work in a changing
environment.
With very similar rooms it tends to
interchange between the two rooms.
Scalability:
- using different numbers of G-robots and L-
robots, doesn't degrade in performance
- increasing in size, still able to agree in a
common choice but performance degrades.

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PRESENTATION-decision-making

  • 1. Collective Decision Making with Heterogeneous Agents Swarm Intelligence Fiscarelli Antonio Maria
  • 2. Scenario - Central room - 4 rooms(colored leds) - Light source - Ground color - Objects(green leds)
  • 3. Robots G-Robot : - Ground sensor L-Robot : - Light sensor All robots : - Omnidirectional Camera - Range and Bearing system - Led
  • 4. Goal Collectively select the best room: - VG: ground color quality - VL: light intensity quality - VO: number of objects quality
  • 5. Main Idea - Action state diagram
  • 6. - Walking state diagram
  • 7. Communication - G-robots estmates color ground and send it to L-robots - L-robots estimates light intensity and send it to L-robots - All robots estimates number of objects in the room
  • 8. Tests Tests have been performed using different settings: - Swarm size N in the range {20,25,30,35,40} - Ratio p in the range {0.25, 0.5, 0.75} where TG is the subpopulation of G-robots and TL is the subpopulation of L-robots
  • 9.
  • 10.
  • 11.
  • 12. Conclusion Self organization: - defining only the single robot's behavior, a swarm behavior emerges. Local interaction: - in-room interaction Partial knowledge: - no global information shared - no environment a priori information - in-room memory
  • 13. Robustness: the swarm is still capable to reach its goal if some robots stop working. Flexibility: the swarm is capable to work in a changing environment. With very similar rooms it tends to interchange between the two rooms. Scalability: - using different numbers of G-robots and L- robots, doesn't degrade in performance - increasing in size, still able to agree in a common choice but performance degrades.