MAGV Navigation in Warehouse Environments using Probabilistic Roadmaps

Abstract

The use of automated guided vehicles (AGVs) for goods movement is growing due to the desire to have more smart automation involved with warehousing and for other mobile guided vehicles applications. Nevertheless, it can be difficult to operate dynamic and high-throughput operations with traditional single AGV (SAGV) systems. This research proposes a multi-AGV (MAGV) simulation framework, which was developed in MATLAB using the Robotics System Toolbox and a probabilistic roadmap (PRM) algorithm for path planning, to overcome the issues discussed. The proposed system provides real-time visibility of the operations of AGVs, as well as complete job scheduling and dynamic collision avoidance, to support coordinating AGV operation in warehouse environments with loading, unloading, and charging areas. The MATLAB simulation results support the performance of the MAGV system with respect to increasing task throughput, reducing idle times, and decreasing total delivery times compared to SAGV systems. In the MAGV configuration, it was shown that it demonstrated effective scalability, reducing job completion times by more than 55%. This research advanced intelligent warehouse logistics as it showed a flexible, scalable, and effective approach to multi-robot navigation and control in real-time.