一种基于多智能体强化学习的AGV任务调度方法

By using a multi-agent reinforcement learning algorithm, the problem of scientifically and rationally allocating AGV tasks in a hospital intelligent logistics system was solved, generating a scheduling scheme with the lowest path cost, which is applicable to material handling tasks of multiple AGVs.

CN116312997BActive Publication Date: 2026-07-17ZHEJIANG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
ZHEJIANG UNIV
Filing Date
2023-01-17
Publication Date
2026-07-17

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Abstract

本发明公开了一种基于多智能体强化学习的AGV任务调度方法，属于医院智能物流系统任务调度领域。本发明包括：S1.建立与医院相关的地图模型；S2.根据建立的地图模型，求出地图中各点之间的最短路径；S3.以所有AGV任务中最短路径之和作为AGV任务调度的优化目标，构建AGV调度数学模型；S4.基于多智能体强化学习算法求解AGV调度优化模型，得到AGV的最优调度方案。该方法能够快速求解一种医院智能物流系统环境下的AGV任务调度问题，获得任务完成路径成本最低或近似最低的任务调度方案。
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