MPC-DS: A Safe Path Tracking Method for AGVs in Dynamic Environments With Dense Obstacles

This is the authors' abstract. We don't add key points for this paper.

In narrow environments with dynamic dense obstacles, it is difficult to find feasible paths for autonomous ground vehicles (AGVs) by using existing methods due to the strict security constraints on AGV movements. To overcome such difficulty, this work proposes an improved local path tracking algorithm based on model predictive control and a dynamic control barrier function with slack variable (MPC-DS). In this algorithm, slack variable is integrated with the control barrier function to convert the strict constraints to soft ones. To regulate the values of slack variable, a suitable penalty coefficient selected by using a series of comparative simulations is incorporated into MPC’s cost function. To test effectiveness of the proposed method, it is compared with three mainstream methods in environments with dense and sparse dynamic obstacles. Results of simulations and physical experiments show that AGV controlled by the proposed algorithm can avoid obstacles safely and efficiently in complex environments. It is worth noting that its use reduces energy consumption by 21.1% in comparison with the existing ones. To facilitate further research, our code is publicly available at https://github.com/BUCT-RobotLab417/MPC-DS