1. Pedro Nunes1 Sensor Fusion Applied to the RoboCup Simulation League

2. Pedro Nunes2 Introduction Environment reconstruction using multiple sources of information. Environment reconstruction using multiple sources of information. Provide more accurate estimations of the location of dynamic objects that are visible to multiple robots. Provide more accurate estimations of the location of dynamic objects that are visible to multiple robots. Each agent shares it’s Worldview with the teammates. Each agent shares it’s Worldview with the teammates. Re-parameterization of Gaussian Distributions. Re-parameterization of Gaussian Distributions.

3. Pedro Nunes3 SF in the Soccer Server Visual Sensor: Approximate the Soccer Server’s noise model with a Gaussian Distribution. Aural Sensor: An agent can issue a message with 512 bytes that is broadcasted to all agents within 50m. An agent can only hear one message every second simulation cycle.

4. Pedro Nunes4 Merging Gaussians Transform to global frame: Transform to global frame: C = R(-theta) T C L R(-theta) Merge: Merge: C‘ = C 1 - C 1 [C 1 + C 2 ] -1 C 1 Calculate new Mean: Calculate new Mean: X’ = X 1 + C 1 [C 1 + C 2 ] -1 (X 2 - X 1 )

5. Pedro Nunes5 Experiment

6. 6 Results

7. 7 Conclusions Generally sensor fusion improves object localization. Generally sensor fusion improves object localization. Create rules in order to give preference to some sensors. Create rules in order to give preference to some sensors.

8. Pedro Nunes8 Learning Cooperation In the RoboCup Simulation League

9. Pedro Nunes9 Introduction Usually agents share their Worldview. Usually agents share their Worldview. Very efficient but not realistic. Very efficient but not realistic. Communication between agents was reduced. Communication between agents was reduced.

10. Pedro Nunes10 Objective Develop a cooperation method where the information exchange is low. Develop a cooperation method where the information exchange is low. Replace the information obtained trough communication. Replace the information obtained trough communication. Solution: Solution: Determine the location of the players based on the position of the ball. Determine the location of the players based on the position of the ball. Why the ball position? Why the ball position?

11. Pedro Nunes11 Implementation Several games were recorded and used in the learning process. Several games were recorded and used in the learning process. In each cicle we recorded: In each cicle we recorded: Position and Velocity of the Ball. Position and Velocity of the Ball. Position of all Players. Position of all Players. Over 60000 patterns. Over 60000 patterns. Neural Net. Neural Net.

12. Pedro Nunes12 Results

13. Pedro Nunes13 Results (2) TeamWDLGAP Vision + Comm. 33030-1612 Vision + Neural 32122-1711 Neural Neural31218-1810 Vision0068-270

14. Pedro Nunes14 Conclusions Complement information of the World. Complement information of the World. Consequences: Consequences: Higher pass accuracy. Higher pass accuracy. More with ball possesion. More with ball possesion. Higher number of scored goals. Higher number of scored goals. Better global performance. Better global performance.