Device-Free Human Sensing and Localization in Collaborative Human-Robot Workspaces: A Case Study
Device-Free Human Sensing and Localization in Collaborative Human-Robot Workspaces: A Case Study
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DOI：
10.1109/jsen.2015.2500121
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发表时间：
2016-03-01
影响因子：
4.3
通讯作者：
Giussani, Matteo
中科院分区：
文献类型：
--
作者：
Savazzi, Stefano;Rampa, Vittorio;Giussani, Matteo
现代机器人制造业正在促进以人机协作任务为特征的混合生产系统的实现。用于工人保护的安全技术需要先进的传感能力和灵活的解决方案，能够监控移动机器人附近操作员的移动。本文提出了使用无线无设备定位（DFL）的方法和架构，以检测和跟踪人类工人在一个合作的人-机器人工业工作空间。DFL系统由多组大规模交互的小型、低成本、嵌入式射频（RF）收发器组成，这些收发器执行接收功率测量。这些设备固定在工厂的固定外围位置，并提供工人的定位，工人特别是既不携带无线有源设备（无设备），也不携带特定的跟踪传感器（无传感器传感）。操作员的运动，事实上，估计通过跟踪由人体引起的无线电场的扰动，同时考虑移动机器人的非平稳干扰的效果。提出的定位和检测算法是基于跳变线性马尔可夫系统和交互式多模型方法，其定位精度已被验证的工业测试工厂的机器人单元内进行的实验。建议的DFL系统已实施采用IEEE 802.15.4射频器件工作在2.4 GHz，并集成到一个软件安全架构。最后，设计了一个软件工具集来预测DFL精度，验证实验测量结果，并支持与预安装的工业传感器集成，以提高增强系统的精度。
Modern robot manufacturing is fostering the implementation of hybrid production systems characterized by human-robot cooperative tasks. Safety technologies for workers protection require advanced sensing capabilities and flexible solutions that are able to monitor the movements of the operator in proximity of moving robots. This paper proposes the use of wireless device-free localization (DFL) methods and architectures to detect and track a human worker in a cooperative human-robot industrial workspace. The DFL system is composed of groups of massively interacting small, low-cost, embedded radio-frequency (RF) transceivers that perform received power measurements. These devices are anchored in fixed peripheral locations of the plant and provide the localization of the worker, who peculiarly carries neither wireless active devices (device-free) nor specific tracking sensors (sensor-free sensing). Operator motion is, in fact, estimated by tracking the perturbations of the radio field induced by the human body, considering the effect of concurrently moving robot as non-stationary interference. The proposed localization and detection algorithm is based on the jump linear Markovian system and the interactive multiple model method, and its positioning accuracy has been validated by experiments performed inside a robotic cell of an industrial test plant. The proposed DFL system has been implemented by employing IEEE 802.15.4 RF devices operating at 2.4 GHz and integrated into a software safety architecture. Finally, a software toolset has been designed to predict DFL accuracy, to verify experimental measurements, and also to support the integration with preinstalled industrial sensors to increase the accuracy of the augmented system.