Authors

Abstract

With the rapid development of agricultural machinery intelligence and informatization, agricultural robots are becoming the protagonist, promoting standardized production in agriculture, improving efficiency, and reducing labor costs. However, how to quickly plan an efficient and safe path for agricultural transport robots is currently a hot topic in path planning research. In this study, the path optimization problem of agricultural robots handling agricultural products (such as Edible Fungi) in and out of warehouses, which served as the study object, was solved. First, the number of agricultural handling robots was initialized based on the scanning method, and the geometric center of sub-path nodes was set as the virtual node. Secondly, the optimal path of the virtual node was solved using the improved ant colony algorithm embedded with a genetic operator, and the optimal result of sub-paths was acquired. Thirdly, the optimal solution meeting constraint conditions was obtained with the launch cost, transportation cost, and time cost of agricultural robots as objective functions. Lastly, the effectiveness of the optimization model and the improved ant colony algorithm was verified through the instance analysis. This study is of certain significance to the ex-warehousing path optimization of agricultural robots under the sustainable development concept of agricultural automation.

Abstract in Chinese

随着农业机械智能化和信息化的飞速发展，农业机器人正在成为主角，推动农业实现标准化生产，提高效率，减少人工成本。然而农业搬运机器人如何快速规划出一条高效、安全的路径是目前路径规划研究的热点问题。本文以农业机器人仓库运输路径优化为研究对象，解决农业机器人搬运农产品(例如食用菌)进出仓库场景下的路径优化问题。首先，基于扫描法初始化农业搬运机器人初始数量，并将子路径节点的几何中心设置为虚拟节点；其次，采用嵌入遗传算子的改进蚁群算法求解连接虚拟节点的最优路径，求解子路径的最优结果。第三，以农业机器人启动成本、运输成本和时间成本为目标函数，最终得到满足约束条件的最优解。最后，通过实例分析验证优化模型和改进蚁群算法的有效性，对农业自动化可持续发展理念下农业机器人出库路径的优化研究具有一定的意义。