在复杂仓储环境下，自动导引运输车（automated guided vehicle, AGV）搬运过程中的全局路径规划问题备受关注，其中能耗最小化是一个关键目标。传统蚁群算法路径规划未充分考虑能耗，为此，提出改进蚁群算法以实现AGV搬运路径最低能耗规划。首先介绍蚁群算法原理基础，然后构建单AGV综合加速、减速、转弯的全过程运动能耗数学模型为算法改进提供依据。基于此模型提出三项改进策略：一是趋向函数改进信息素更新机制，提升收敛速度与精度；二是引入能耗因素优化路径选择并用代码模型实现，增强能耗效率关注度；三是逆向消除冗余拐点策略简化路径、降低能耗。最后仿真对比原ACO实验验证改进算法有效性。结果表明：相比传统算法，其在复杂仓储环境下规划的搬运路径能耗更低且更简洁高效，对提升AGV搬运效率和降低能耗意义重大、应用价值显著。

In the complex storage environment, the global path planning in the process of automated guided vehicle(AGV) handling has attracted much attention, and the minimization of energy consumption is a key goal. The path planning of the traditional ant colony algorithm does not fully consider the energy consumption. To this end, the improved ant colony algorithm was proposed to realize the minimum energy consumption planning of AGV transportation path. Firstly, the principle and basis of ant colony algorithm were introduced, and then a mathematical model of the whole process of motion energy consumption of single AGV comprehensive acceleration, deceleration and turning was constructed to provide a basis for algorithm improvement. Based on this model, three improvement strategies were proposed: first, the trend function improves the pheromone update mechanism to improve the convergence speed and accuracy. The second was to introduce energy consumption factors to optimize path selection and use code models to enhance the attention of energy efficiency. The third was to reverse eliminate redundant inflection points, simplify the path, and reduce energy consumption. Finally, the simulation was compared with the original ACO experiment to verify the effectiveness of the improved algorithm. The results show that compared with the traditional algorithm, the energy consumption of the transportation path planned in the complex storage environment is lower, more concise and efficient, which is of great significance for improving the handling efficiency and reducing the energy consumption of AGV, and has significant application value.