FUZZy TimE-critical Spatio-Temporal (FUZZ-TEST): Project #3 Brandon Cook – 3 rd 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 1

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

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

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 4

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 5

Fuzzy Logic Allows classification of variables for more human-like reasoning. Common terms Inputs Rules Outputs Membership Function Fuzzy Inference System (FIS) 6

7 Fuzzy Decision Making

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 8

9 Example of Type 2 Fuzzy Membership Functions

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 10

Fuzzy Paddle 11

Fuzzy Inference System 12

Simulation: Robots vs. Robots 13

Results: Robots vs. Robots 14 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

Simulation: Humans vs. Robots 15

Results: Humans vs. Robots 16 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

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. 17

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 18

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. 19

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

Questions? 21