Yang Xinrui, Kahouadji Mouad, Lakhal Othman, Merzouki Rochdi
Laboratory CRIStAL, University of Lille, Lille, France.
Front Robot AI. 2022 Sep 20;9:980800. doi: 10.3389/frobt.2022.980800. eCollection 2022.
This article presents an integrated concept of an aerial robot used for predictive maintenance in the construction sector. The latter can be remotely controlled, allowing the localization of cracks on wall surfaces and the adaptive deposit of the material for repairs. The use of an aerial robot is motivated by fast intervention, allowing time and cost minimizing of overhead repairs without the need for scaffolding. It is composed of a flying mobile platform positioned in stationary mode to guide a soft continuum arm that allows to reach the area of cracks with different access points. Indeed, some constructions have complex geometries that present problems for access using rigid mechanical arms. The aerial robot uses visual sensors to automatically identify and localize cracks in walls, based on deep learning convolutional neural networks. A centerline representing the structural feature of the crack is computed. The soft continuum manipulator is used to guide the continuous deposit of the putty material to fill the microscopic crack. For this purpose, an inverse kinematic model-based control of the soft arm is developed, allowing to estimate the length of the bending tubes. The latter are then used as inputs for a neural network to predict the desired input pressure to bend the actuated soft tubes. A set of experiments was carried out on cracks located on flat and oblique surfaces, to evaluate the actual performances of the predictive maintenance mechatronic robot.
本文提出了一种用于建筑行业预测性维护的空中机器人的集成概念。该机器人可以远程控制，能够定位墙面裂缝并自适应地涂抹修复材料。使用空中机器人的动机在于能够快速干预，无需搭建脚手架，从而减少高空维修的时间和成本。它由一个以固定模式定位的飞行移动平台组成，用于引导一个柔性连续体手臂，该手臂能够通过不同的接入点到达裂缝区域。实际上，一些建筑具有复杂的几何形状，使用刚性机械臂进行访问会存在问题。空中机器人基于深度学习卷积神经网络，使用视觉传感器自动识别和定位墙面裂缝。计算出代表裂缝结构特征的中心线。柔性连续体机械手用于引导腻子材料的连续涂抹以填充微观裂缝。为此，开发了一种基于逆运动学模型的柔性手臂控制方法，用于估计弯曲管的长度。然后将这些长度用作神经网络的输入，以预测弯曲驱动柔性管所需的输入压力。在平面和倾斜表面上的裂缝上进行了一系列实验，以评估预测性维护机电一体化机器人的实际性能。
