Path Following of Autonomous Forklifts via Nonlinear Model Predictive Control

This article studies the problem of designing a path-following controller for the autonomous forklift in a warehouse. The proposed nonlinear model predictive controller tackles both the general need for efficient transportation between areas following a given path, as well as the specific requirement for autonomous forklifts to conduct precise fork positioning during pallet loading tasks to prevent damage to goods and pallets. To validate the efficacy of our approach, we conduct real-world experiments using a laboratory autonomous forklift prototype. The experimental results show that the proposed controller can achieve a good balance between accuracy and efficiency in the general path-following task, as well as achieving pose accuracy better than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ 1\text{ cm}$</tex-math></inline-formula> and angle accuracy within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.5^{\circ }$</tex-math></inline-formula> in the pallet loading task.