Multiple Level Fuzzy Logic-based Artificial Intelligence for Multi-Agent Cooperative Robot Platform  

Oleh:

Vicerra, Ryan Rhay P. ; David, Kanny Krizzy A. ; Simbulan, Kristan Bryan C. ; Santos, Cyrus M. Delos ; Bandala, Argel A. ; Fernando, Arvin H. ; Dadios, Elmer P.

Jenis: Article from Proceeding
Dalam koleksi: The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS), 3-6 December 2013 Cebu, Philippines, page 1-10.
Topik: Fuzzy Logic; Robotics; Artificial Intelligence; Product Design & Development
Fulltext: 4013.pdf (354.54KB)

Isi artikel

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 a pure fuzzy logic artificial intelligence for a dynamic robot platform with multiple robots and multiple identity assignment which means that each robot will have its distinct behavior. In order to design pure fuzzy logic artificial intelligence, we applied fuzzy logic multiple times calling each of them as a fuzzy logic block. These fuzzy logic blocks can be seen in different parallel and series configuration making it multilevel in structure. 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). The system requires complex intelligence as its individual agents must perform specific tasks in a dynamic environment, unlike other systems which duplicates a single task for all the agents. In our setup, we use three robots and gave them three different identities; the Forward, the Back and the Goal-keeper. The robot identity assignment is very dynamic and depends on the position of each robot with respect to the position of the object they are pursuing. The developers have to tune each fuzzy logic blocks individually by isolating each one from the other. Some tuning procedures are performed in a simulator while most of them are tuned in real time in the actual platform. Although tuning procedures are rigorous, the linguistic approach and human reasoning nature of fuzzy logic made it easy for the developers to achieve its completion. The multiple trial and error tuning enhanced the developers understanding of how fuzzy logic and the overall system works. Overall, the proposed artificial intelligence produced favorable response based on the expected outcome and experimentations.

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