在仓储物流领域中，自动导向搬运车系统（AGVS）具有可靠性程度高、机动灵活等特点，但其工作环境复杂度的增加也提升了路径规划的难度。针对复杂环境下AGVS路径规划的低效且易冲突问题，文中提出了一种基于分层分布式框架的改进AGVS路径规划算法。首先，为提高算法在路径规划过程中的搜索效率，对传统A ^*算法的评价函数进行改进，并将双向Floyd算法与改进A ^*算法融合，以增加路径平滑度，最终得到AGVS全局最优路径；然后，建立AGVS运动学模型，将全局最优路径中关键节点作为临时目标点，通过调整机器人初始位姿、优化评价函数，在各临时目标点间使用DWA算法完成AGVS局部路径规划；最后，引入AGVS协同规划策略，通过对AGVS分配任务优先级实现AGVS间运动的统一调度，降低移动机器人之间发生冲突的概率，以提高AGVS路径规划算法的鲁棒性。Matlab仿真实验结果表明：提出的改进算法在简单环境和复杂环境下均可以生成AGVS无碰撞路径，在复杂环境下，文中改进算法规划的AGVS路径长度相比传统A ^*算法缩短2.26%；AGVS运动过程中，机器人线速度和角速度始终保持在0.6~1.2 m/s和-0.4~0.4 rad/s区域内，符合机器人运动学特性。

In the field of warehousing and logistics, automatic guided truck system (AGVS) has the merits of high reliability and flexibility, but with the increase of the complexity of its working environment, the difficulty of path planning also increases. Aiming at the problem of low efficiency and easy conflict in AGVS path planning in complex environment, this paper proposed an improved AGVS path planning algorithm based on hierarchical distributed framework. Firstly, in order to improve the search efficiency of the algorithm in the path planning process, the evaluation function of the traditional A ^* algorithm was improved and fused with the bidirectional Floyd algorithm to increase the path smoothness, and the global optimal AGVS path is finally obtained. Secondly, the AGVS kinematics modeling was established, and the key nodes in the global optimal path were taken as temporary target points. By adjusting the initial poses of the robot and optimizing the evaluation function, the AGVS local path planning was completed appying the DWA algorithm to the temporary target points. Finally, AGVS collaborative planning strategy was introduced to achieve unified scheduling of inter-AGVS motion by assigning task priorities to AGVS, reducing the probability of conflicts between mobile machines, improving the robustness of AGVS path planning algorithm. Matlab simulation results show that the proposed improved algorithm can generate collision-free paths in both simple and complex environments. In complex environments, AGVS path length planned by the improved algorithm is shortened by 2.26% compared with that planned by the traditional A ^* algorithm. In the process of AGVS motion, the angular velocity and the linear velocity of the mobile robot are always maintained within -0.4~0.4 rad/s, and 0.6~1.2 m/s, which conforms to the kinematic characteristics of the mobile robot.