Optimal Controller Design of Kalman-Filtering in Visual Navigation for Automatic Guided Vehicle
Zhiwei Liang · Journal of Tianjin University Science and Technology · 2005
In order to solve the problem of accuracy control of automatic guided vehicle(AGV) equipped with visual navigation system, the vision-based continuous state equation was turned into a discrete state equation to obtain distance deviation and angular deviation from the center axis, both of which were used as input signal of the filters. Then the U-D decomposed Kalman-filtering could effectively eliminate the white noise existing in the AGV navigation system. Recursive algorithm in time domain was adopted to provide steady state variables for the optimal controller and guarantee the computational stability to achieve better system robustness. Optimal output of the optimal control system could be obtained by status feedback. The results indicate that the robustness and noise immunity of the controller designed with the proposed method are strong. By the proposed controller, the orientation deviation of AGV ranges between ±0. 5° and the lateral deviation ranges between ±4 mm, while the swerve radius is 5 m. Consequently the controller works properly under the industrial circumstances with large electric noise and can trace the linear and curve paths effectively. It is used in intelligent transportation equipment such as automatic guided vehicle and can meet the requirement of industrial application.