A Novel AGV Path Planning Algorithm for Large-Scale Factories

We propose a novel path planning algorithm for Automated Guided Vehicles (AGVs) in a large factory that utilizes the $A^{*}$ algorithm with a cost function incorporating congestion, wirelength, net turns, and other constraints. In addition, we employ Particle Swarm Optimization (PSO) to optimize this cost function. We applied our algorithm to the ICCAD 2024 CAD Contest-a worldwide competition attracting up to 300 teams annually-and, on average, achieved a performance improvement of 4.12 % over the first-place winner. The results indicate that our approach effectively optimizes the cost function to accommodate different requirements.