A unified sampling method for optimal feature coverage and robot placement

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing Pub Date : 2024-12-19 DOI:10.1016/j.rcim.2024.102932

Domenico Spensieri, Edvin Å blad, Raad Salman, Johan S. Carlson

引用次数: 0

Abstract

Designing a robot line includes the critical decision about the number of robots needed to carry out all the tasks in the stations and their placement. Similarly, having a robot manipulator mounted on a mobile base, such as an Automated Guided Vehicle (AGV), needs a careful choice of the base positions to minimize cycle time for the operations. In this paper, we solve both the robot placement and the AGV positioning problems by relating them to feature coverage applications, where the challenge is to place cameras (or other sensors) to inspect all points on a workpiece for metrology tasks. These similarities allow us to design an efficient divide&conquer-based algorithm which can be adapted to solve all three problems above, where finding the minimum number of positions for sensors, AGVs and robots is crucial to reduce cycle time and costs.

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优化特征覆盖和机器人位置的统一采样方法

机器人生产线的设计包括对在各工位执行所有任务所需的机器人数量及其位置进行关键决策。同样，将机器人机械手安装在移动底座（如自动导引车（AGV））上，也需要谨慎选择底座位置，以尽量缩短操作周期。在本文中，我们通过将机器人放置和 AGV 定位问题与特征覆盖应用联系起来来解决这两个问题，在特征覆盖应用中，我们面临的挑战是放置摄像头（或其他传感器）来检测工件上的所有点，以完成计量任务。这些相似之处使我们能够设计出一种基于分而治之的高效算法，该算法可用于解决上述所有三个问题，在这些问题中，找到传感器、AGV 和机器人的最小位置数对于减少周期时间和成本至关重要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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.