A Rough Set GA-based Hybrid Method for Robot Path Planning

In this paper, a hybrid method based on rough sets and genetic algorithms, is proposed to improve the speed of robot path planning. Decision rules are obtained using rough set theory. A series of available paths are produced by training obtained minimal decision rules. Path populations axe optimised by using genetic algorithms until the best path is obtained. Experiment results show that this hybrid method is capable of improving robot path planning speed.