Model Predictive Control of Autonomous Ground Vehicles in Warehouse Logistics

Al-Ameri, Laith

doi:10.25673/123747

Proceedings of International Conference on Applied Innovation in IT · 2026/03/31 · Vol. 14 · Issue 1 · pp. 1419–1425

Model Predictive Control of Autonomous Ground Vehicles in Warehouse Logistics

Laith S. Ismail and Zahraa A. Al-Ameri

📄 Download PDF DOI: 10.25673/123747

Abstract

As more and more autonomous ground vehicles (AGVs) are used in warehouse logistics, we need better control strategies that make sure operations are safe, efficient, and scalable in complicated indoor spaces. This paper presents a model predictive control (MPC) framework that integrates trajectory tracking, collision avoidance, and throughput optimization for automated guided vehicles (AGVs) functioning in structured warehouses. The vehicle is represented by a discrete kinematic bicycle model, and the MPC formulation includes state and input constraints, margins for avoiding obstacles, and rules for traffic in the warehouse. We did simulations and scaled hardware tests in a number of situations, such as navigating narrow aisles, handling multiple AGV intersections, dealing with pedestrian intrusion, and managing traffic jams. When compared to baseline controllers (Pure Pursuit and LQR), the results showed a 35-40% decrease in tracking error, a consistent safety margin, and up to 25% more throughput. The times it took for the solver to run showed that it was possible to use it in real time on embedded platforms. The results show that MPC is effective at automating logistics for Industry 4.0 and provide a scalable base for future multi-AGV coordination and secure data integration in smart warehouses.

Keywords

Model Predictive Control Autonomous Ground Vehicles Warehouse Logistics Collision Avoidance Industry 4.0 Real-Time Optimization.

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