A Predictive Optimization Framework for Stability-Constrained Obstacle Avoidance and Anti-Swing Motion Planning of Overhead Cranes

In this paper, we propose an enhanced motion planning framework for overhead cranes that ensures both stability and obstacle avoidance. The Time-Elastic Band (TEB) method is widely recognized for its efficiency in real-time path planning, offering adaptive trajectory optimization and fast replanning capabilities in dynamic environments. However, it does not account for payload swing, making it unsuitable for crane operations where stability is crucial. To overcome this limitation, we integrate Model Predictive Control (MPC) with TEB, explicitly incorporating nonzero payload swing angles into the optimization process. This fusion enables the crane to achieve coordinated anti-swing control and obstacle avoidance, ensuring safe and efficient motion even in dynamic environments. Simulation results demonstrate that the proposed framework effectively reduces payload oscillations while maintaining robust obstacle avoidance, significantly enhancing the operational safety and performance of overhead cranes.