| 不确定场景下无人农机多机动态路径规划方法 |
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| 引用本文： | 梁亚杰, 杨丽丽, 徐媛媛, 陈智博, 冯雅蓉, 吴才聪. 不确定场景下无人农机多机动态路径规划方法[J]. 农业工程学报, 2021, 37(21): 1-8. DOI: 10.11975/j.issn.1002-6819.2021.21.001 |
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| 作者姓名： | 梁亚杰 杨丽丽 徐媛媛 陈智博 冯雅蓉 吴才聪 |
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| 作者单位： | 1.中国农业大学信息与电气工程学院,北京 100083;2.农业农村部农业信息获取技术重点实验室,北京 100083 |
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| 基金项目： | 北京市科技计划项目（Z201100008020008） |
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| 摘 要： | 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,不仅提高了效率,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...
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| 关 键 词： | 农业机械 自动化 无人驾驶 多机协同作业 动态路径规划 |
| 收稿时间： | 2021-07-14 |
| 修稿时间： | 2021-11-20 |
Dynamic path planning method for multiple unmanned agricultural machines in uncertain scenarios |
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| Liang Yajie, Yang Lili, Xu Yuanyuan, Chen Zhibo, Feng Yarong, Wu Caicong. Dynamic path planning method for multiple unmanned agricultural machines in uncertain scenarios[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(21): 1-8. DOI: 10.11975/j.issn.1002-6819.2021.21.001 |
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| Authors: | Liang Yajie Yang Lili Xu Yuanyuan Chen Zhibo Feng Yarong Wu Caicong |
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| Affiliation: | 1.College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;2.Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, Beijing 100083, China |
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| Abstract: | 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. |
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| Keywords: | agricultural machinery automation driverless multi machine cooperative operation dynamic path planning |
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