A Novel Integrated Path Planning Algorithm for Warehouse AGVs

We propose an integrated path planning method for multiple automated guided vehicles performing logistics delivery within a real-world warehouse environment considering obstacles. By applying it on each vehicle, this proposed enables the vehicle have capabilities autonomous planning. The consists of three parts, K-means algorithm based task points clustering, genetic ordering, and probabilistic road map best search. Vehicle conflict resolution is depending implementing construction realistic with simulations result validate that clustering ordering are necessary planning, both time Automated (AGVs) running can be dramatically reduced.