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 completion 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.