An Architecture for Indoor Location-Aided Services based on Collaborative Industrial Robotic Platforms

O. Arsene, C. Postelnicu, Wenbo Wang, E. Lohan, D. Năstac
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引用次数: 1

Abstract

An essential component in the intelligent wireless processing for the future industrial halls will be the data labelling with location information. The location information will facilitate not only the remote control and autonomy of the industrial robots and sensors, but it will also enable predictive control and maintenance, increased productivity, and increased workers' safety. The data labelling is typically a tedious and costly process when done manually or semi-automatically, and the fully automated data labelling has still to overcome several challenges that we describe in this paper. We propose a collaborative robotic architecture equipped with simultaneous localization and mapping as well as machine-learning-based algorithms. A scenario in an industrial setting is presented, in which data acquisition by robots, with various capabilities, can be used to enable location-based services for increased workers' safety and to offer timely tracking of mobile assets for an increased productivity. The robotic platform acquires data during the periods when the robots are not allocated to their main tasks. Besides, we demonstrate that the above mentioned robotic platform could benefit from machine learning, for example, the accurate estimation of positions and good adaption in different type of collected data sets.
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基于协同工业机器人平台的室内定位辅助服务体系结构
未来工业大厅的智能无线处理的一个重要组成部分将是带有位置信息的数据标签。位置信息不仅可以促进工业机器人和传感器的远程控制和自主,还可以实现预测性控制和维护,提高生产力,提高工人的安全性。当手动或半自动完成数据标记时,数据标记通常是一个繁琐且昂贵的过程,并且全自动数据标记仍然需要克服我们在本文中描述的几个挑战。我们提出了一种协作机器人架构,配备了同步定位和地图以及基于机器学习的算法。本文介绍了工业环境中的一个场景,其中具有各种功能的机器人数据采集可用于实现基于位置的服务,以提高工人的安全性,并提供及时的移动资产跟踪,以提高生产率。机器人平台在机器人没有被分配到其主要任务期间获取数据。此外,我们还证明了上述机器人平台可以受益于机器学习,例如,对不同类型收集的数据集的准确估计和良好的自适应。
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