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-0756921

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)

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

Fuzzy Inference System12

13

Simulation: Robots vs. Robots

14

Results: Robots vs. Robots

Methods:• Ran simulation for 30 servesConclusions:• 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 logicConclusions:• 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

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 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

20

Acknowledgements

UC Academic Year – Research Experience for Undergraduates (AY-REU) ProgramSophia Mitchell (Base Pong Script)

Dr. Kelly Cohen

21

Questions?