Path planning of an AGV based on artificial potential field and model predictive control

In order to solve the obstacle avoidance problem when the external environment changes during AGV movement in the intelligent factory, this paper proposes an AGV obstacle avoidance path planning method based on artificial potential field (APF) and model predictive control (MPC). First, the environmental potential field is constructed to determine the value of potential field received by AGV at each point in the process of movement. Secondly, the vehicle kinematics model is constructed, and the vehicle kinematics model is linearized and discretized. Then, the potential field value obtained by APF method is taken as input, and the MPC method is adopted to obtain the control sequence with the minimum target performance function index by using the error between the predicted trajectory and the expected trajectory. Finally, the effectiveness of the proposed method is verified by Simulink/Casim co-simulation. The results show that this method can carry out path planning autonomously in the intelligent factory area.