Control and path prediction of an Automate Guided Vehicle

Purpose: In this paper a new architecture and control strategy of an AGV is proposed. It is organized as follows. The system architecture is explained in section 2. Section 3 deals with the kinematics model of the AGV path, prediction and control. Section 4 describes the experiments. The conclusion and recommendation are given in section 5. Design/methodology/approach: It is a three wheels vehicle. The front wheel is used for driving and steering the AGV and the two rear wheels are free. The steering and driving are DC motor. Two encoders are individually attached on the two rear wheels in order to measure the vehicle displacement and then calculate its real time position and orientation. The choice of positioning the encoders on the free wheels provides to the vehicle an accurate measurement of its progression. A programmable logic control (PLC) is used for motion control. Findings: In this paper, An Automate Guided Vehicle (AGV) is presented. The developed algorithm is based on memorised path and kinematics determination of the movement. The vehicle position and deviation are calculated from rear wheels rotation measurement. The steering and driving command are determined from this deviation. Localization of AGV by Kaman filtering algorithm is presented. Control of AGV motion is implemented by using PID control scheme. Displacement axis and steering axis are separated to implement the motion control. We proposed the localization system for estimation of AGV. Position and orientation are estimated by Kalman filtering in state-space model. Position and orientation of AGV are measured and used for simulation for localization system. We conclude that the vehicle can reach from the initial position moved along with generated path with accurate location. A Schneider PLC is used to implement this control. The tests reveal a smooth movement and convenient deviation. Practical implications: The first prototype working, the next research steps will be development of a correction system to correct none detected errors. It will also be necessary to develop the fleet management strategy and software. Originality/value: Future work is planed to increase the accuracy of the system by equip more sensors for observation technique. Treatment of dynamic model and machine vision application of automated vehicle are also planed to the next step.