基于视觉的无人机垂直结构自主检测

Ayush Gupta, Amit Shukla, Amit Kumar, Ashok Kumar Shivratri
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引用次数: 0

摘要

烟囱、火炬堆、储罐、冷却塔、电线杆和通信塔等高层建筑成为任何行业的重要组成部分,在日常生活中非常常见。这些垂直结构需要适当和频繁的检查,以确保行业安全运行并保持盈利。工业结构的检测分几个阶段进行,基于视觉的检测是检测和定位表面缺陷和异常的最原始、最古老、最简单的方法。现有的基于视觉的传统检测方法不安全、耗时,并且对公司来说是额外的经济负担,而现有的机器人检测方法由于复杂的动力学、结构和重量而效率低下、速度缓慢且令人筋疲力尽。在这项研究工作中,我们提出了一种利用水旋翼机对垂直结构进行完全自主视觉检测的方法。在机器人操作系统(ROS)和可视化工具凉亭的帮助下,配备了摄像头和非接触式传感器的无人机(UAV)进行了模拟。为了检验开发的算法,在露台上准备了一个简单的黑色圆柱形垂直结构。在这里,无人机首先使用经典的计算机视觉算法检测和定位图像帧中的垂直结构,并将提取一些所需的特征。从图像帧中得到的特征信息将被输入启发式调整的控制算法,用于无人机在垂直结构周围的导航和定位。为此,开发了用于定位和轨迹跟踪的偏移控制和旋转宽度/半径控制算法。由于图像帧定位和定位,不存在对GPS的依赖,也可以在没有GPS的环境下工作。仿真结果令人满意,计算机视觉算法和控制算法的总体性能令人满意。
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Vision-Based Autonomous Inspection of Vertical Structures Using Unmanned Aerial Vehicle (UAV)
High-rise structures like a chimney, flare stacks, storage tanks, cooling towers, electric line poles, and communication towers become a vital part of any industry and are very common in day-to-day life. These vertical structures required a proper and frequent inspection to run the industries safely and stay profitable. The inspection of the industrial structure is done in several stages, and vision-based inspection is the very initial, oldest, and simplest method to detect and locate the surface defects and anomalies. The existing traditional methods of vision-based inspections are unsafe, time-consuming, and are an extra financial burden on a company and the existing robotics inspections methods are ineffective, slow, and exhausting due to complex dynamics, structure, and weights. In this research work, we are proposing a fully autonomous visual inspection approach to inspect the vertical structure using aquadcopter. The unmanned aerial vehicle (UAV) equipped with cameras and non-contact sensors is simulated with the help of robot operating systems (ROS) and a visualization tool gazebo. To examine the developed algorithms, a simple black-colored cylindrical vertical structure is prepared in the gazebo. Here, the UAV first detects and locates the vertical structure in the image frame using a classical computer vision algorithm and will extract some desired features. The feature information coming out from the image frame will be fed into the heuristically tuned control algorithms for navigating and positioning the UAV around the vertical structure. For this work, offset control and width/radius of rotation control algorithms have been developed for positioning and trajectory tracking. Due to the image frame localization and positioning, the GPS dependency is not there, and it can operate in GPS denied the environment also. The simulation results are quite satisfying, and the overall performance of the computer vision algorithms and control algorithms is satisfactory.
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