Learning weighted linguistic rules to control an autonomous robot

A methodology for learning behaviors in mobile robotics has been developed. It consists of a technique to automatically generate input–output data plus a genetic fuzzy system that obtains cooperative weighted rules. The advantages of our methodology over other approaches are that the designer has to choose the values of only a few parameters, the obtained controllers are general (the quality of the controller does not depend on the environment), and the learning process takes place in simulation, but the controllers work also on the real robot with good performance. The methodology has been used to learn the wall-following behavior, and the obtained controller has been tested using a Nomad 200 robot in both simulated and real environments. C © 2009 Wiley Periodicals, Inc.