A Rough-Fuzzy Controller for Autonomous Mobile Robot Navigation

This paper presents a new development of a rough-fuzzy controller for an autonomous mobile robot based on rough set and fuzzy set theory. It has been tested in different environments with the Saphira simulation software. The proposed approach provides an improvement in uncertainty reasoning by using a rough-fuzzy controller, resulting in better wall-following behavior performance as compared against other controllers. The rough-fuzziness of the input data leads to the enhanced uncertainty reasoning process by calculating the roughly approximated fuzzified value of the input, which makes the system more robust and reliable