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A human driver can successfully execute different vehicle navigation tasks, such as: forward garaging, reverse garaging, parallel parking, diagonal parking, following a given trajectory, stopping at a given point, avoiding one or more obstacles, and the like. These processes are either too difficult to model accurately or too complex to model at all, while experienced drivers are able to successfully execute the tasks without any knowledge of the vehicle’s mathematical model, even when they switch vehicles. Existing knowledge about how such problems are solved and the ability to express this knowledge, are benefits which can be put to good use when designing controllers for wheeled mobile robot (WMR) navigation tasks. It is well known that fuzzy logic is the most effective tool for solving a problem for which there is a human solution (Zadeh, 2001), because it offers a mathematical apparatus in the background of an inaccurate, qualitative description, which projects input variables into output variables through several stages. However, describing the tasks executed by a driver in spoken language is not a simple process. When we drive a car, we rely on our driving skills and prefer to use our hands and feet, rather than our brain. It is safe to assume that compiling rules based on driver skills will not be an easy task. In view of the above, this chapter presents a new approach to the modeling of driver skills, based on a fuzzy model and an original virtual fuzzy-magnet concept. Driver experience is used to solve simple tasks (turn left or right, slow down or accelerate), while the entire problem is solved by specifying an appropriate number of fictitious fuzzy magnets and defining their tasks. Each fictitious fuzzymagnet is defined by its position and a set (or subset) of fuzzy rules which determine its action. For example, to solve a target navigation problem in open space requires only one fuzzy magnet which attracts the mobile robot to the target, while a more complex problem, such as bidirectional garaging of a mobile robot, requires two fuzzy magnets: one immediately in front of the garage and the other inside the garage. The first point is used to approach the garage and while the vehicle is entering the garage, it serves as a reference point for proper orientation, similar to human driving skills. The second point is both a target and a reference point. Compared to other algorithms which address this type of problem, the proposed algorithms are very simple; they are not based on the WMR model. The proposed fuzzy controllers are of the Takagi-Sugeno (T-S) type; they are manually 12

Fictitious Fuzzy-Magnet Concept in Solving Mobile–Robot Target Navigation, Obstacle Avoidance and Garaging Problems