Fuzzy logic decision making for autonomous robotic applications

Abstract

There is growing in interest in the effectiveness of emulating human decision making and learning in modern aerospace applications. The following is an examination of several applications in which type 1 and 2 fuzzy logic has been utilized in artificial intelligence and machine learning problems to demonstrate their capabilities. In Fuzzy Logic Inferencing for PONG (FLIP), the effectiveness of type 1 logic is examined as an optimal controller for players in the game of PONG. Robotic collaboration is also developed as the PONG game was expanded into a multiplayer option. Collaborative Learning using Fuzzy Inferencing (CLIFF) is an extension of this PONG game, however type-2 logic is used to create a robotic coach that optimizes its players to beat its opponent in a development of layered fuzzy learning. Precision Route Optimization using Fuzzy Intelligence (PROFIT) examines the use of fuzzy logic as an optimizer in an algorithmic solution to a modified Travelling Salesman Problem (TSP). The TSP is modified in a way to better mimic a real-life scenario where footprints must be visited instead of simply points, which gives an interesting complexity to the problem. Considering the successes associated with these research endeavors, it can be concluded that type 1 and 2 fuzzy logic are both interesting tools that can further the abilities of intelligent systems and machine learning algorithms.