Trajectory Planning for Bridge Cranes Based on RRT* Algorithm and Segmented Time Optimization

To enhance the operational efficiency and safety of a three-dimensional (3-D) bridge crane in complex environments, a trajectory planning method combining the optimal Rapidly-exploring Random Tree Star (RRT*) global obstacle avoidance and segmented time polynomial optimization is proposed. The RRT* algorithm is employed to quickly generate obstacle-avoidance paths, and segmented time polynomials are used to fit the path points. By utilizing quadratic programming (QP) within the feasible constraint domain, the trajectory is optimized to ensure continuity and smoothness. Additionally, the complexity of system coupling is simplified through differential flatness techniques. Simulation results demonstrate that the proposed method generates trajectories capable of controlling the maximum load swing angle within 1.94°, with the system stabilizing at the target position within 78 s. The method also adapts to changes in load mass and obstacle positions. Compared to existing methods, the proposed algorithm exhibits significant advantages in positioning accuracy, trajectory smoothness, and real-time obstacle avoidance capabilities, providing an effective solution for intelligent crane control in complex industrial scenarios.