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Achievements• Added degree of freedom to PONG• New bounce capabilities (angel of incidence)• Improved Boundary Conditions• Rotate/Translate Simultaneously • Humans vs. Robots Capable• Robots vs. Robots Capable

University of Cincinnati, Department of Aerospace Engineering

Brandon CookFaculty Mentor: Kelly Cohen

FUZZy TimeE-critical Spatio-Temporal Pong (FUZZ-TEST Pong)

Future Work• Adding varying degrees of difficulty selections • Easy, Medium, Hard

• Add trickery components to Intelligent Team• Additional inputs and modified outputs to take

opponents current rotation in to account• Implement collaborative robotics into real world, 3-

dimensional, simulation (e.g. disaster relief situations)

(Fuzzy Logic Inferencing Pong)

Objectives

IntroductionFuzzy Logic• Allows classification of variables for more human-like

reasoning• Common terms:• Inputs• Rules• Outputs• Membership Function• Fuzzy Inference System (FIS)

Figure 1: The light spectrum is fuzzy, as is nature.

• Create Simulation• Observe Human Collaboration: Tennis Matches• Create Fuzzy Inference System• Models Human-Like Behavior

• Beta Testing• Compile Results

Results

Figure 4: Fuzzy Inference System (FIS) File

Conclusion• Fuzzy logic is an effective tool for:• Emulating human-like reasoning• Collaborative linguistic reasoning between

autonomous robots• Adapting to situations where linear model

controllers are not feasible

References• Kosko, Bart. Fuzzy Thinking: The New Science of Fuzzy Logic. New York: Hyperion, 1993. Print.• Mendel, Jerry M., and Dongrui Wu. "Interval Type-2 Fuzzy Sets." Perceptual Computing: Aiding People in Making Subjective Judgments. Piscataway, NJ:

IEEE, 2010. 35-64. Print. • 2010 Australian Open – Men’s Doubles Final Bob & Mike Bryan vs. Nestor & Zimonjic [video]. Retrieved June, 2011, from http://www. youtube.

com/watch?v=0C-pEt8d9ts• Barker, S. , Sabo, C. , and Cohen, K. , "Intelligent Algorithms for MAZE Exploration and Exploitation", AIAA Infotech@Aerospace Conference, St. Louis,

MO, March 29-31, 2011, AIAA Paper 2011-1510. • D. Buckingham, Dave’s MATLAB Pong, University of Vermont, Matlab Central, 2011• Federer & Mirka vs. Hewitt & Molik – part 3 [video]. Retrieved June, 2011, from http://www. youtube. com/watch?v=b0BAh_pRRTo• Sng H. L. , Sen Gupta and C. H. Messom, Strategy for Collaboration in Robot Soccer, IEEE International Workshop on Electronic Design, Test and

Applications (DELTA), 2002• B. Innocenti, B. Lopez and J. Salvi, A Multi-Agent Architecture with Cooperative Fuzzy Control for a Mobile Robot, Robotics and Autonomous Systems,

vol. 55, pp. 881-891, 2007• D. Matko, G. Klancar and M. Lepetic, A Tool for the Analysis of Robot Soccer Game, International Journal of Control, Automation and Systems, vol. 1,

pp. 222 – 228, 2003

Figure 7: Added Rotational Degree of Freedom

• Create a doubles PONG game with:• Advanced ball control• Rotation of paddles• 2-on-2 gameplay• Fuzzy Logic based opponents

Methods

Figure 2: Doubles Pong Setup

PONG• Classic arcade game created by Atari• Gameplay Objective: Score by hitting the

ball past opponent• First team to 21 points wins• Creates spatio-temporal environment

Figure 5: Breakdown of Fuzzy Paddle (Offensive vs. Defensive)

Figure 3: Fuzzy Logic Reasoning

Strategy• Larger paddle rotations: more offensive

• Back up partner: more defensive

Figure 6: Fuzzy Team Defensive Strategy (Red Team)

Robots Humans Winner

Robots vs. Humans

42 2 ROBOTS

Red Team Blue Team Winner

Robots vs. Robots

15 15 DRAW

Table 1: Doubles Robot Team vs. Robot Team Results

Table 2: Doubles Human Team vs. Robot Team Results

Beta Testing

Acknowledgements• Sponsored by: The National Science Foundation• Grand ID No.: DUE-0756921

• Academic Year – Research Experience for Undergraudates (AY-REU) Program

• Sophia Mitchell• Original FLIP Simulation Creator (only translation)

Fuzzy Reasoning• Example of how Fuzzy Paddles assign

discrete outputs

Fuzzy Inference System (FIS)• Example of strategy FIS

• Simulation proved Fuzzy teams are evenly matched• Each volley lasted nearly 5 minutes• Logic proved effective at :• Intercepting ball trajectory• Hitting ball towards open court positions

Figure 5. Real-World Collaborative Robots (i.e. Naos)

• Match #1 (first to 21 points)• Robots defeated Humans 21 to 2• Only scores on Robots due to small gameplay

glitches (e.g. ball traveling through paddle)• Match #2 (first to 21 points)• Robots defeated Humans 21 to 0

• Proved effectiveness of Fuzzy Logic Paddles

• Depending on Inputs:• Unoccupied regions of the court (Open)• Game Strategy: offensive/defensive (Game)• Current fuzzy paddle location

• Assign discrete output strategy: Where to hit the ball (Strategy)