不确定场景下无人农机多机动态路径规划方法

在现代化农业中，越来越多的龙头企业或农村合作社提供一系列的农业作业专业化服务，引入多台农机进行规模化作业，不仅提高效率，而且可以实现抢种抢收，减少自然灾害的风险。目前，多台农机并行作业仍以预先计划的固定农机和静态的固定路线为主，但在实际耕种、收割等作业中，常会出现农机突发故障、农机临时增加、农机工作效率不一致等不确定场景，这些不确定性给多台农机集群控制带来巨大挑战。因此，研究不确定场景下多机动态路径规划方法具有十分重要的理论意义和实用价值。该研究以总作业时长为综合优化目标，综合各种不确定场景，针对轮式自动驾驶拖拉机，提出了改进的迭代贪婪（Improved Iterated Greedy, IIG）方法进行多机动态路径规划，解决以往传统方法在不确定情况发生后路径规划结果低效甚至失效的问题。试验表明，该方法在不确定场景下可及时、高效的动态调整路径规划方案，能够为不同数量、不同性能的农机迭代找到当前最优路径。与传统的并排作业方法相比，IIG优化的矩形农田作业路径总作业时间平均下降约35%，且随着农机性能差异越大，节省时间越多；与迭代贪婪（Iterated Greedy, IG）方法相比，IIG在一般播种作业中总掉头时间平均减少约17%。该方法在不确定场景下路径优化效果较好，且具有很好的鲁棒性及环境适应性，可为农田无人作业多机路径规划提供参考。

Dynamic path planning method for multiple unmanned agricultural machines in uncertain scenarios

Abstract: Most machinery can be hands-free and remotely operated in modern agriculture. Almost all tractors are equipped with some sort of GPS technology in recent years, indicating a step on the way to fully autonomous farms in the future. A series of multiple agricultural machinery have also been introduced to realize highly efficient plant and harvest, while reducing the risk of natural disasters for large-scale production in China. Particularly, the vehicle can travel on pre-mapped roads, even to move around the obstacle. However, the parallel operation is still widely used in current multiple machinery, indicating the fixed agricultural machinery and static fixed route in advance. Furthermore, there are often most uncertain scenarios, such as a sudden failure, temporary increase, and inconsistent work efficiency of agricultural machinery in the actual farming and harvesting. These uncertainties have also posed great challenges to the operation of multiple agricultural machinery. Therefore, it is necessary to explore the multi-machine dynamic path planning, whenever the information is accessible about the barrier, particularly when the environment tends to be unpredictable and changeable. Moreover, the future unmanned farm is highly requiring the large-scale operation of multiple agricultural machinery. In this study, a multi-machine dynamic path planning was proposed for the wheeled autonomous tractors in various uncertain scenarios using an improved iterative greedy (IIG) algorithm. The total operation time was also taken as the comprehensive optimization objective. More importantly, an attempt was made to deal with the inefficient or even invalid path planning after the occurrence of uncertain scenarios. The experimental results show that the scheme of path planning was timely and efficiently adjusted in uncertain scenarios. An optimal path was also found for the different numbers and performances of agricultural machinery during an iterative process. The total operation time of IIG optimized operation path in rectangular farmland decreased by 35%, compared with the traditional side-by-side operation. Specifically, there was a significant optimization effect, as the performance of agricultural machinery varied greatly. Additionally, the total turning time was reduced by 17% after IIG optimization, compared with the original. Consequently, the optimization algorithm presented a remarkable performance in uncertain scenarios, indicating excellent robustness and environmental adaptability. The finding can also provide a strong reference for the path planning of multiple autonomous machinery in unmanned farmland.