# Aerial Robotic Solution for Detailed Inspection of Viaducts
## 5 Conclusions  
This work demonstrates that it is possible to perform an inspection of a viaduct with aerial robots as an alternative to current methodologies, saving time and cost and improving the safety and quality of data obtained. The design and development of the AERO-CAM and AeroX inspection platforms was quite successful, since together they cover the sensor needs of a viaduct: either looking for possible defects or analyzing existing ones. In addition, the provision of autonomous capabilities to the platforms, especially to the AERO-CAM, greatly facilitates the work and provides more flexibility than conventional methods. These capabilities also reduce the number of specialized people needed to operate these platforms, thus improving their safety and speed of use. Thanks to the choice of a LIDAR-type sensor for the autonomous capabilities of the AERO-CAM, the platform is fully versatile to operate in variable lighting situations, either due to weather conditions or to possible shadows and lighting changes that may be caused by the viaducts themselves. In addition, the system does not rely on a total station for the flight of the robotic platforms. Likewise, the camera installed in its gimbal can be configured to adapt to the different level of detail required or even be replaced by another one without having to redesign the platform. As future developments, although the localization presented in this article provides good and sufficient results in terms of accuracy and calculation speed, the AERO-CAM does not consider taking off autonomously from areas where its LIDAR cannot see the structure, as the global localization would fail. This means that, until the system is able to locate itself, the pilot has to fly the UAV to the viaduct. Therefore, other strategies can be developed to complement the localization system to overcome these edge cases. On the other hand, another line of work is the inclusion of a detect and avoid system to provide the AERO-CAM with more advanced intelligent capabilities when executing missions. Currently, the system relies on the fact that the mission designed by the inspector  
Figure 12: Algodonales sequence 2 inspection result. (**a**) shows the 3D model with the followed route (green) and the location of the acquired pictures (blue planes). (**b**,**c**) show two examples of the acquired pictures. is free of obstacles and the waypoints keep a safe distance from the viaduct, but it could make use of the LIDAR readings to detect potential hazards on the route and re-plan it in real time. Conceptualization, R.C., M.A.T., F.J.P.-G., A.V. and A.O. ; Investigation, R.C., J.P., M.A.T. and F.J.P.-G.; Methodology, R.C. ; Software, R.C. and J.P.; Validation, R.C. and J.P.; Project administration, M.A.T., F.J.P.-G., A.V. and A.O. ; writing--original draft preparation, R.C. ; writing--review and editing, R.C., J.P., M.A.T., F.J.P.-G., A.V. and A.O. All authors have read and agreed to the published version of the manuscript. This work was funded by the RESIST (H2020-2019-769066) and PILOTING (H2020-2020-871542) projects funded by the European Commission. Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available yet due to ongoing private work. Authors would like to thank [PERSON] for his support in the development of the global localization algorithm, [PERSON] for his review of this article and the interesting discussions, and the GRVC from the University of Seville for collaborating with their total station. In addition, the authors would like to thank Ferrovial for allowing and providing access to infrastructure for the experimentation phase. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: