A multiple level MIMO fuzzy logic based intelligence for multiple agent cooperative robot system

• Main Authors: Vicerra, Ryan Rhay P., David, Kanny Krizzy A., Dela Cruz, Angelo R., Roxas, Edison A., Simbulan, Kristan Bryan C., Bandala, Argel A., Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2016
• Subjects: Fuzzy logic; Intelligent agents (Computer software); Artificial intelligence; Robotics; Manufacturing
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1877; https://animorepository.dlsu.edu.ph/context/faculty_research/article/2876/type/native/viewcontent/TENCON.2015.7372985
• Institution: De La Salle University

Fuzzy Logic is a many valued logic and it is very similar to human reasoning which is not binary. It uses approximate measures rather than exact, making it suitable for linguistic variable and analysis. It has been applied to many applications in artificial intelligence, control and robotics. In this paper, the authors develop an artificial intelligence using multiple fuzzy logic for a dynamic multiple agent robot system. The system is made up of multiple robots with multiple identity assignment; which means that each robot will have its distinct behavior. In order to design pure fuzzy logic artificial intelligence, we used fuzzy logic block in different parallel and series configuration making giving it multiple fuzzy logic levels. Furthermore, there is multiple input-multiple output (MIMO) fuzzy logic implementation in one of our several fuzzy logic blocks, this is necessary in order to utilize pure fuzzy logic control in the whole artificial intelligence. The multi agent cooperative robot platform we choose to test our artificial intelligence is a multiple robot system for FIRA Micro-Robot World Soccer Tournament (MiroSot). In our setup, there are three robots to be assigned dynamically with three different identities; the Forward, the Back and the Goal-keeper. Robot identity assignment depends on the position of each robot with respect to the position of the ball. To tune each fuzzy logic block individually isolation is done. Some tuning procedures are performed in a simulator while most of them are tuned in the actual platform. Although tuning procedures are rigorous, the linguistic approach and human reasoning nature of fuzzy logic made it possible to achieve its completion. Overall, the proposed artificial intelligence produced favorable response based on the expected outcome and experimentations.