Motion planning for obstacle avoidance in overhead cranes via artificial potential fields and model predictive control

Abstract

This paper proposes a novel trajectory tracking combined with an obstacle avoidance control method for crane systems in industrial environments. First, the reference motion trajectory of the trolley carrying the load, along with the time-varying cable length, is designed using a model predictive controller (MPC). The highlight of this study is that the artificial potential field (APF)-based obstacle avoidance method is applied to crane systems for the first time. Subsequently, a sliding mode controller (SMC) is developed to track the designed reference trajectory. The global stability as well as the convergence of the states in the closed-loop system are rigorously proven using Lyapunov stability theory. Finally, simulation results on MATLAB/Simulink demonstrate the superior effectiveness of the proposed method compared with traditional obstacle avoidance methods.