Fuzzy adaptive mechanism for improving the efficiency and precision of vision-based automatic guided vehicle control

This paper proposes a vision-based automatic guided vehicle hybrid control system, which incorporated fuzzy adaptive mechanism with dynamic motion model, for improving the efficiency and precision of vehicle guidance trajectory. Vehicle is guiding along the lane center and keeps correct attitude. In this paper, vehicle's attitude is obtained from the turning angle of front wheel (/spl delta/) which is derived by a vision-based vehicle dynamic behavior acquisition subsystem (VVDBA) in our previous work. We define /spl Delta//spl delta/ and threshold value in this paper. If /spl Delta//spl delta/ is under the threshold, vehicle is guided by dynamic motion model. Otherwise, the fuzzy inference system is used to handle the vehicle's speed, so that it can be moving smoothly and avoiding exceed out the lane boundary. The hybrid strategy was simulated by MATLAB in this paper, and it would obtain robust and precise tracking capabilities along any arbitrary lane.