Optimizing the design of a fuzzy path planner for car-like autonomous robots

This paper presents methods and tools to design a fuzzy path planner for autonomous non-holonomic vehicles by means of supervised learning. The method combines heuristic knowledge and geometric considerations to obtain a continuous-curvature short path that can be executed efficiently by the path tracking controller of the mobile robot. Furthermore, the method minimizes the computer requirements to implement the fuzzy planner. The proposed design method can be easily carried out by means of the Xfuzzy 3.0 environment developed by some of the authors. The resulting planning strategies have been proven successfully in the Romeo 4R autonomous vehicle fully designed and built at the Escuela Superior de Ingenieros, University of Seville.