Autonomous parallel parking of a car-like mobile robot by a neuro-fuzzy behavior-based controller

In this paper, the concept of sensor-based behavior is used to design a neuro-fuzzy control system for a car-like-mobile-robot. The problem addressed is the parallel parking in a rectangular constrained space with just one backward maneuver. To accomplish the autonomous fuzzy behavior control, the car-like-mobile-robot has trained to park in just 2 parking dimensions based on the training data obtained from sensor information generated offline by adopting a fifth-order polynomial as the reference trajectory. The proposed controller is an ANFIS architecture that generates turning angle as output. As long as the states (positions and orientations) of the robot are measurable at each discrete-time step during the control process, this controller can make the robot follow feasible trajectories by just knowing the initial configuration of the robot and park successfully at the prescribed goal position. The simulation results which are based on real dimensions of a typical car demonstrate the feasibility and effectiveness of the proposed controller in practical car maneuvers.