Intelligent Lifting Path Planning of Tower Crane Based on Glowworm Swarm Optimization Algorithm

Aiming at the problems of unstable transportation paths and many inflection points affecting system safety planned by tower cranes in complex working environments, an improved Glowworm Swarm Optimization algorithm is proposed to carry out intelligent lifting path planning for tower cranes. Based on the working scene of the tower crane, a three-dimensional map environment model is established to simulate the complex building environment with many obstacles, and the standard Glowworm Swarm Optimization algorithm is improved according to the operating characteristics of the tower crane. The step factor of the Glowworm Swarm Optimization algorithm is adaptively designed for accurate path planning outcomes. The problem of slowing down or discontinuous crane movement brought on by the twisted path is resolved by reducing the variety of directions in which glowworms can move to the direction toward the goal point. The incorporation of a mutation strategy aimed at enhancing the stability of crane path planning. B-spline data interpolation is used to fit the curve to obtain a smooth and obstacle-avoidance path that is more suitable for the tower crane in order to prevent the crane from constantly braking. The simulation results demonstrate that the improved algorithm can locate a tower crane's ideal obstacle avoidance path in a challenging construction environment. The resulting path is smoother and has fewer inflection points, increasing the tower crane's safety and carrying capacity.