1. FUZZy TimE-critical Spatio-Temporal (FUZZ-TEST): Project #3
Brandon Cook – 3rd Year Aerospace Engineering ACCEND Student
Dr. Kelly Cohen – Faculty Mentor
Sponsored by: NSF Type 1 STEP Grant, Grant ID No.: DUE
Continuation of: Fuzzy Logic Inference for Pong (FLIP) Research

2. Outline Project Goal & Objectives Project Research Tasks
Project Time Schedule
Introduction to Fuzzy Logic
Current Progress
Future Work

3. Project Goal
Grasp understanding of intelligent robots and systems while expanding on abilities for real world applications.

4. Overall Objectives Create a Doubles-PONG game simulation in MATLAB
Learn about Type II Fuzzy Logic
Create an AIAA conference paper
Work with Sophia Mitchell to create a joint journal publication

5. Project Research Tasks
Conduct Literature survey
Create an “Intelligent Team”
of two autonomous agents
Successfully implement additional rotation DOF (Degree of Freedom)
Test the effectiveness of the “Intelligent Team”
Summarize results into a Technical Report

6. Fuzzy Logic
Allows classification of variables for more human-like reasoning.
Common terms
Inputs
Rules
Outputs
Membership Function
Fuzzy Inference System (FIS)
Fuzzy logic allows for classification of variables for more human-like reasoning. Normally programming uses binary logic, where things are ones, and zeros, red or yellow. As humans, using this type of reasoning in everyday life wouldn’t make much sense because we know there are numbers between zero and one, or colors between red and yellow. To us, things are in a continuum. The beauty of fuzzy logic is if I were to ask everyone in this room to point to orange on this spectrum, “orange” would suddenly become several different points with varying degree of belonging to red and yellow, and we would all be correct. *pause* Now let’s just reflect upon what you see here… the color spectrum. This is life. In all it’s richness and color. Life is fuzzy, and we need to embrace that fact.

7. Fuzzy Decision Making

8. Type II Fuzzy Logic
Brings uncertainty into the membership functions of a fuzzy set
Linguistic uncertainties can be modeled that were not visible in Type 1 fuzzy sets
Allows for more noisy measurements to be quantified

9. Example of Type 2 Fuzzy Membership Functions

10. Accomplishments Knowledge on Type II Fuzzy Logic
Rotational DOF added to human and Fuzzy paddles
Enhanced bounce characteristic
Optimized boundary conditions
Ability to rotate and translate simultaneously
Less delay between commands and response
Allows Fuzzy paddle to rotate fully to desired output
Implemented new design into FLIP (singles game)
New Fuzzy Rules

11. Fuzzy Paddle

12. Fuzzy Inference System

13. Simulation: Robots vs. Robots

14. Results: Robots vs. Robots
Methods:
Ran simulation for 30 serves
Conclusions:
Each “volley” lasted nearly 3 minutes
Intelligent teams are equally matched
Highly effective at calculating the ball trajectory to find intersection point
Very challenging to score a point on the fuzzy team

15. Simulation: Humans vs. Robots

16. Results: Humans vs. Robots
Methods:
Two veteran “gamers” selected to test the effectiveness of the fuzzy logic
Conclusions:
Fuzzy team is superb at collaborating effectively to defeat the opposing team
Highly effective at communicating in a time-critical manner
Excellent at making adjustments to the infinite gameplay scenarios to defeat opponent

17. Conclusions
Fuzzy logic is an effective tool for collaboration between autonomous agents in a time-critical spacio-temporal environment
A completely autonomous, robotic, intelligent team (or swarm) would be very useful in applications including:
Space robotics
Celestial body exploration and colonization
Unmanned Aerial Vehicles (UAVs)
Homeland security
Disaster relief programs.

18. Future Work Expand on game to incorporate more advanced features:
Modify FIS for extreme offensive moves capability (limits reaction time)
Passing from back to front paddle
Speeding up ball between paddles
Both go for ball with different paddle angles
Add additional inputs/outputs to take opponents current rotation in to account
Present work at International AIAA conference in January (Dallas, TX)
Create a joint AIAA publication with Sophia Mitchell

19. Future Plans Continue research in intelligent systems19
Future Plans
Continue research in intelligent systems
Complete my Bachelors and Masters degrees through the ACCEND program
Pursue a PhD
NASA Jet Propulsion Laboratory
Upcoming Coop: NASA Marshall (U.S. Space and Rocket Center
Go to space.

20. Acknowledgements
UC Academic Year – Research Experience for Undergraduates (AY-REU) Program Sophia Mitchell (Base Pong Script) Dr. Kelly Cohen

21. Questions?