Worker monitoring and protection in collaborative robot (cobots) industrial environments requires advanced sensing capabilities and flexible solutions to monitor the movements of the operator in close proximity of moving robots. Collaborative robotics is an active research area where Internet of Things (IoT) and novel sensing technologies are expected to play a critical role. Considering that no single technology can currently solve the problem of continuous worker monitoring, the article targets the development of an IoT multisensor data fusion (MDF) platform. It is based on an edge-cloud architecture that supports the combination and transformation of multiple sensing technologies to enable the passive and anonymous detection of workers. Multidimensional data acquisition from different IoT sources, signal preprocessing, feature extraction, data distribution, and fusion, along with machine learning (ML) and computing methods are described. The proposed IoT platform also comprises a practical solution for data fusion and analytics. It is able to perform opportunistic and real-time perception of workers by fusing and analyzing radio signals obtained from several interconnected IoT components, namely, a multiantenna WiFi installation (2.4-5 GHz), a sub-THz imaging camera (100 GHz), a network of radars (122 GHz) and infrared sensors (8-13 μm). The performance of the proposed IoT platform is validated through real use case scenarios inside a pilot industrial plant in which protective human-robot distance must be guaranteed considering latency and detection uncertainties.

中文翻译：

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用于协作机器人环境中机会无线电传感的多传感器边缘云平台


协作机器人 (cobot) 工业环境中的工人监控和保护需要先进的传感功能和灵活的解决方案来监控操作员在移动机器人附近的移动。协作机器人是一个活跃的研究领域，物联网 (IoT) 和新颖的传感技术预计将发挥关键作用。考虑到目前没有单一技术可以解决连续工人监控的问题，本文的目标是开发物联网多传感器数据融合（MDF）平台。它基于边云架构，支持多种传感技术的组合和改造，实现对工作人员的被动、匿名检测。描述了来自不同物联网源的多维数据采集、信号预处理、特征提取、数据分布和融合，以及机器学习 (ML) 和计算方法。拟议的物联网平台还包括数据融合和分析的实用解决方案。它能够通过融合和分析从多个互连的物联网组件（即多天线 WiFi 安装（2.4-5 GHz）、亚太赫兹成像相机（100 GHz））获得的无线电信号，对工作人员进行机会性和实时感知。雷达 (122 GHz) 和红外传感器 (8-13 μm) 网络。所提出的物联网平台的性能通过试点工业工厂内的真实用例场景进行了验证，其中考虑到延迟和检测不确定性，必须保证保护性人机距离。