Multi-AGV task scheduling and dynamic map path planning based on task pre-allocation

Abstract

Multi-AGV (Automated Guided Vehicle) systems operating in complex warehouse environments equipped with movable containers encounter several challenges, including high system no-load rate, low task response efficiency, and imbalanced path utilization. To address these issues, we propose an integrated optimization approach for task scheduling and path planning. First, a task segmentation strategy is introduced to decompose complex tasks into long-distance transport and precision in-racking sub-tasks which are then allocated to heterogeneous AGV types for execution. Second, a task pre-allocation algorithm is designed to enable AGVs to participate in the subsequent task assignment prior to the completion of their current tasks, thereby reducing the system no-load rate. Third, a dynamic map path planning mechanism is developed, which incorporates a temporary path traffic control module and a localized Floyd update algorithm to achieve real-time path adjustment and effective path utilization optimization. Comparative experiments were conducted in a multi-zone warehouse simulation environment. The results demonstrate that the proposed approach can reduce the system no-load rate, mitigate path congestion, and enhance overall operational performance.