Neurofuzzy motion planners for intelligent robots

A neurofuzzy methodology is presented for motion planning in semi-autonomous mobile robots. The robotic automata considered are devices whose main feature is incremental learning from a human instructor. Fuzzy descriptions are used for the robot to acquire a repertoire of behaviors from an instructor which it may subsequently refine and recall using neural adaptive techniques. The robot is endowed with sensors providing local environmental input and a neurofuzzy internal state processing predictable aspects of its environment. Although it has no prior knowledge of the presence or the position of any obstructing objects, its motion planner allows it to make decisions in an unknown terrain. The methodology is demonstrated through a robot learning to travel from some start point to some target point without colliding with obstacles present in its path. The skills acquired are similar to those possessed by an automobile driver. The methodology has been successfully tested with a simulated robot performing a variety of navigation tasks.