Behavior based mobile robot navigation by AI techniques: behavior selection and resolving behavior conflicts using Alpha level fuzzy inference system

Abstract

paper, problems associated with mobile robot navigation are investigated and a new methodology is established to resolve the problems. The issue investigated is the behavior rule selection when more than one action of the same type is present during mobile robot navigation and the methodology proposed is the Alpha level fuzzy logic system. In this investigation an action selection or decision-making procedure incorporating Alpha level fuzzy logic is established and used for selecting an appropriate action during mobile robot navigation. This methodology also presents the mathematical aspects of resolving conflicts when more than one context rule of the same kind is in action. In the present approach, the operational strategies of the human expert driver are transferred via fuzzy logic to the robot navigation in the form of a set of simple conditional statements composed of linguistic variables. These linguistic variables are well defined by Alpha level fuzzy sets with user defined membership functions. The main advantage of the Alpha level fuzzy logic navigation strategy is the ability to extract heuristic rule from human experience and to obviate the need for an analytical model of the process. Simulation and Real world experiment results are presented based on the real life situation.