College of Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
IRL 2958 Georgia Tech-CNRS, 2 Rue Marconi, 57070 Metz, France.
Sensors (Basel). 2022 Apr 22;22(9):3235. doi: 10.3390/s22093235.
The inspection and maintenance of large-scale industrial structures are major challenges that require time-efficient and reliable solutions to ensure the healthy condition of structures during operation. Autonomous robots may provide a promising solution for this purpose. In particular, they could lead to faster and more reliable inspection and maintenance without direct intervention from human operators. In this paper, we present a custom magnetic crawler system, and sensor suit and sensing modalities to enable such robotic operation. We first describe a localization framework based on a mesh created from a point cloud coupled with Inertial Measurement Unit (IMU) and Ultra-Wide Band (UWB) readings. Next, we introduce a mapping framework that relies on a 3D laser, and explicitly state how autonomous navigation and obstacle avoidance can be developed. Lastly, we present how ultrasonic guided waves (UGWs) are integrated into the system to provide accurate robot localization and structural feature mapping by relying on acoustic reflections in combination with the other systems. It is envisioned that long-range inspection capabilities that are not yet available in current industrial mobile platforms could emerge from the designed robotic system.
大型工业结构的检测和维护是重大挑战,需要高效、可靠的解决方案,以确保结构在运行过程中的健康状况。自主机器人可能为此提供了有前途的解决方案。特别是,它们可以实现更快、更可靠的检测和维护,而无需操作人员的直接干预。在本文中,我们提出了一种定制的磁性履带系统,以及传感器套件和传感模式,以实现这种机器人操作。我们首先描述了一种基于点云与惯性测量单元(IMU)和超宽带(UWB)读数相结合创建的网格的定位框架。接下来,我们介绍了一种依赖于 3D 激光的映射框架,并明确说明了如何开发自主导航和避障功能。最后,我们介绍了如何将超声波导波(UGW)集成到系统中,通过结合其他系统的声反射,实现机器人的精确定位和结构特征映射。可以预见,设计的机器人系统将产生当前工业移动平台尚不可用的远程检测能力。