ROSI: A Robotic System for Harsh Outdoor Industrial Inspection - System Design and Applications
ROSI: A Robotic System for Harsh Outdoor Industrial Inspection - System Design and Applications
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ROSI：用于严酷户外工业检测的机器人系统 - 系统设计与应用
DOI：
10.1007/s10846-021-01459-2
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发表时间：
2021
影响因子：
3.3
通讯作者：
F. Lizarralde
中科院分区：
文献类型：
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作者：
Filipe A. S. Rocha;G. Garcia;Raphael F. S. Pereira;Henrique D. Faria;T. Silva;R. Andrade;Evelyn S. Barbosa;André Almeida;Emanuel Cruz;Wagner Andrade;W. Serrantola;Luiz Moura;Héctor Azpúrua;Andre Franca;G. Pessin;G. Freitas;Ramon R. Costa;F. Lizarralde
带式输送机在不同行业的干散货运输中是必不可少的。这种结构需要永久性的检查，传统上是由人工操作员根据认知进行检查。为了改善工作条件和工艺标准化，我们提出了一种由移动平台、机械臂和传感器组成的地面机器人对输送机结构进行检测的新方法。根据现场经验，我们介绍了ROSI，这是一种新的机器人设备，专为在恶劣的户外环境中长期操作而设计。这款移动机器人拥有混合移动系统，使用轮子在长距离行驶的同时减少能源消耗，并在穿越障碍时使用带履带的鳍状肢来提高机动性。机械无源开关可以使轨道的牵引力解耦，在不增加机械复杂性的情况下减少部件磨损和能源消耗。针对机器人辅助操作，控制策略有助于(I)考虑系统整体模型来控制移动平台和机器人操作手，(Ii)调整振动检查时接触输送机结构的接触力，以及(Iii)在自动调整鳍状物的同时爬楼梯。机器学习算法通过处理视觉、热和声音数据作为检测功能来检测传送带的污垢堆积、滚筒故障和轴承故障。算法的训练和验证使用了从淡水河谷运行的传送带收集的数据集，检测准确率高于90%。在采矿现场的现场测试结果表明，机器人在执行所有必需的检查任务的同时，能够承受恶劣的操作条件，这表明ROSI是带式输送机检查和其他一般工业操作的颠覆性解决方案。
Belt Conveyors are essential for transporting dry bulk material in different industries. Such structures require permanent inspections, traditionally executed by human operators based on cognition. To improve working conditions and process standardization, we propose a novel procedure to inspect conveyor structures with a ground robot composed by a mobile platform, a robotic arm, and a sensor-set. Based on field experience, we introduce ROSI, a new robotic device designed for long-term operations in harsh outdoor environments. The mobile robot has a hybrid locomotion system, using wheels to reduce energy consumption while covering long distances, and also flippers with tracks to improve mobility during obstacle negotiation. A mechanical passive switch allows decoupling tracks’ traction, reducing components wear and energy consumption without raising mechanical complexity. Aiming the robot-assisted operation, control strategies help to (i) command both the mobile platform and a robotic manipulator considering the system whole-body model, (ii) adjust the contact force for touching the conveyor structure during vibration inspection, and (iii) climb stairs while automatically adjusting the flippers. Machine Learning algorithms detect conveyors’ dirt build-ups, roller failures, and bearing faults by processing visual, thermal and sound data as inspection functionalities. The algorithms training and validation use a dataset collected from running conveyors at Vale, presenting detection accuracy superior to 90%. Field test results in a mining site demonstrate the robot capabilities to stand for the harsh operating conditions while executing all the required inspection tasks, stating ROSI as a disruptive solution for Belt Conveyor inspections and other general industrial operations.