农业车队路线:一个分散的动态问题

IF 1.4 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Marin Lujak, E. Sklar, F. Semet
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引用次数: 5

摘要

迄今为止,对农业车辆和农业移动机器人(AMRs)的研究主要集中在单个车辆(机器人)及其农业特定能力上。在日常农业任务中,很少有研究探索这种车辆车队的协调。考虑到车队的整体性能、效率和可扩展性,尤其是在高度自动化的农业车辆的背景下,这些车辆可以在不同农民和/或企业拥有的多个领域执行任务。AMR车队协调自动化对商业农业的潜在影响是巨大的。拥有庞大且异构的农业车辆车队的大型企业集团可以在大片土地上运行,而无需人工操作来实现精准农业。在本文中,我们提出了农业车队路线问题(AF-VRP),据我们所知,它不同于迄今为止研究的任何其他版本的车辆路线问题。我们关注的是这个问题的动态和分散版本,适用于涉及多个农业机械和农场所有者的环境,必须考虑公平和公平的概念。该问题结合了三个相关问题:动态分配问题、动态3指标分配问题和电容电弧布线问题。我们回顾了最先进的解决方案,并将其分类为集中式、分布式和分散式,基于强调的决策环境。最后,我们讨论了应用分布式和去中心化协调方法来解决这个问题的公开挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Agriculture fleet vehicle routing: A decentralised and dynamic problem
To date, the research on agriculture vehicles in general and Agriculture Mobile Robots (AMRs) in particular has focused on a single vehicle (robot) and its agriculture-specific capabilities. Very little work has explored the coordination of fleets of such vehicles in the daily execution of farming tasks. This is especially the case when considering overall fleet performance, its efficiency and scalability in the context of highly automated agriculture vehicles that perform tasks throughout multiple fields potentially owned by different farmers and/or enterprises. The potential impact of automating AMR fleet coordination on commercial agriculture is immense. Major conglomerates with large and heterogeneous fleets of agriculture vehicles could operate on huge land areas without human operators to effect precision farming. In this paper, we propose the Agriculture Fleet Vehicle Routing Problem (AF-VRP) which, to the best of our knowledge, differs from any other version of the Vehicle Routing Problem studied so far. We focus on the dynamic and decentralised version of this problem applicable in environments involving multiple agriculture machinery and farm owners where concepts of fairness and equity must be considered. Such a problem combines three related problems: the dynamic assignment problem, the dynamic 3-index assignment problem and the capacitated arc routing problem. We review the state-of-the-art and categorise solution approaches as centralised, distributed and decentralised, based on the underlining decision-making context. Finally, we discuss open challenges in applying distributed and decentralised coordination approaches to this problem.
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来源期刊
AI Communications
AI Communications 工程技术-计算机:人工智能
CiteScore
2.30
自引率
12.50%
发文量
34
审稿时长
4.5 months
期刊介绍: AI Communications is a journal on artificial intelligence (AI) which has a close relationship to EurAI (European Association for Artificial Intelligence, formerly ECCAI). It covers the whole AI community: Scientific institutions as well as commercial and industrial companies. AI Communications aims to enhance contacts and information exchange between AI researchers and developers, and to provide supranational information to those concerned with AI and advanced information processing. AI Communications publishes refereed articles concerning scientific and technical AI procedures, provided they are of sufficient interest to a large readership of both scientific and practical background. In addition it contains high-level background material, both at the technical level as well as the level of opinions, policies and news.
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