# Towards Autonomous Bridge Inspection: Sensor Mounting Using Aerial Manipulators
## 2 Related Work  
In the world of aerial inspections, a number of UAV-based solutions are being proposed by researchers. In [4], a technical survey for bridge inspections is given. Researchers in [5] present the project AERIAL COgnitive Integrated Multi-task Robotic System with Extended Operation Range and Safety (AERIAL-CORE) which focuses on power lines inspection, maintenance and installing bird diverters and line spacers. Most of these approaches are based in conjunction with new technologies to ensure faster and cheaper inspections. Nowadays, UAVs use high-resolution cameras for visual inspections and employ point cloud methods based on digital photogrammetry [6], Light Detection And Ranging (LiDAR)-based methods [7], digital image correlation [8], etc. There are also reports for visual compensation during aerial grasping [9], aerial grasping in strong winds [10], and the development of a fully actuated aerial manipulator for performing inspections underneath a bridge [11]. According to the experimental testing of contact-based bridge inspections, there is a need to develop a solution for mounting application sensors (such as accelerometers, strain gauges and tilt meters) on a bridge using a UAV. It is expected that a sophisticated system with the possibility of automatic sensor mounting will increase the frequency of measurements without interrupting traffic, ensure the safety of inspectors as well as reduce inspection time and overall costs. As mentioned earlier, the second UAV needs to be aware of the position of the sprayed adhesive which is applied in a blob-like pattern. For this purpose, an Red Green Blue - Depth (RGB-D) camera is used due to its favorable dimensions and weight. It provides image and depth information about the environment which proves useful for object localization and UAV navigation. Such cameras were commonly found on UAVs and unmanned ground vehicles (UGVs) present at the recent MBZIRC 2020 competition. In [12; 13], RGB-D information is used for color-based brick detection and localization for the wall-building challenge using UAVs and UGVs, respectively, while in [14] the authors use a Convolutional Neural Network (CNN)-based UAV detection and tracking method for the intruder UAV interception challenge. Furthermore, visual sensors proved useful in [15], where the authors performed a contact-based inspection of a flat surface with an aerial manipulator. The surface position and orientation was obtained by applying random sample consensus (RANSAC) on the RGB-D information. A thorough survey of 2D object detection methods from UAVs was given in [16]. In this paper, the authors present a modular framework for object detection in which a simple contour-based blob detector is implemented. The goal is to use RGB-D information to enable an autonomous inspection workflow. The blob position is obtained by segmenting the depth data at the points where the object is detected in the image, while RANSAC [17] is used to determine its orientation. After the successful detection of a blob-like pattern, it is necessary to attach the inspection sensor. The first phase of the sensor attachment is achieving contact, and the second is maintaining that contact to allow for the two adhesive components to form a bond. Generally, the contact can be achieved with or without force measurements. In [18], contact with the wall is performed and maintained. Researchers in [19] performed wall contact and aerial writing experiments. The work presented in [20] modeled and exploited the effects of the ceiling effect to perform an inspection underneath a bridge. The common denominator in the former approaches is maintaining the contact without any force feedback. Although mounting a force sensor on a UAV increases both mechanical and control complexity, an immediate benefit is the ability to maintain precise contact force regardless of the environment. In [21], the researchers used a force/torque sensor to achieve compliant control while pulling a rope and a semi-flexible bar.