柔性制造系统的分层在线自动规划

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Xiaoting Dong , Guangxi Wan , Peng Zeng , Chunhe Song , Shijie Cui , Yiyang Liu
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引用次数: 0

摘要

车间机器的任务规划和行动规划对现代制造业至关重要。传统上,这两个问题是通过复杂的手工方法独立解决的。然而,个性化定制为制造系统引入了更多动态外生事件,因此不可能手动考虑所有可能的动态情况。本文的重点是在线自动规划,即根据新的动态情况自动生成新的规划。我们首先将柔性制造系统的规划问题表述为一个完全可观测的非确定性规划问题。其次,介绍了一种分层自动在线规划方法。最后,通过 ARIAC 2022 竞赛环境验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hierarchical online automated planning for a flexible manufacturing system

Task planning and action planning for workshop machines are essential for modern manufacturing. Traditionally, these two problems are solved independently with elaborate manual methods. However, personalized customization introduces more dynamic exogenous events into the manufacturing system, and it is then impossible to consider all possible dynamic scenarios manually. This paper focuses on online automated planning, generating new plans automatically in response to new dynamic situations. We first formulate the planning problem for a flexible manufacturing system as a fully observable nondeterministic planning problem. Second, a hierarchical automated online planning approach is presented. Finally, the effectiveness of the proposed approach is verified by an ARIAC 2022 competition environment.

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来源期刊
Robotics and Computer-integrated Manufacturing
Robotics and Computer-integrated Manufacturing 工程技术-工程:制造
CiteScore
24.10
自引率
13.50%
发文量
160
审稿时长
50 days
期刊介绍: The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.
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