People Detection with Quantified Fuzzy Temporal Rules

Detection of people and other moving objects is fundamental for the development of tasks by an autonomous mobile robot, and principally for human-robot interaction. In this paper we present an evolutionary algorithm to learn a pattern classifier system based on the quantified fuzzy temporal rules (QFTRs) model, for the detection of moving objects using laser range finders data. QFTRs are able to analyze the persistence of the fulfillment of a condition in a temporal reference by using fuzzy quantifiers. Experimental results with a Pioneer II robot in a typical hallway environment show an excellent classification rate in a real and complex situation with people moving in several groups in the surrounding.