# Towards Autonomous Bridge Inspection: Sensor Mounting Using Aerial Manipulators
## 8 Conclusions  
This paper presents a step towards autonomous bridge inspection by investigating the possibility of mounting various inspection sensors using an aerial manipulator. Currently, inspectors use specialized trucks with cranes and baskets in order to access the area underneath the bridge. This inevitably leads to road closure which poses an inconvenience for both inspectors and traffic. To alleviate this problem, the aforementioned aerial manipulators can be used to access difficult-to-reach areas of the bridge. As mounting sensors require forming a bond between the wall and sensor, we envision using a two-component adhesive with a short cure time. Since the aerial manipulator has to achieve and maintain contact with the sensor mount point, short cure times are desirable because of te limited flight time of these platforms. Nevertheless, current flight times of outdoor multirotors reach up to 30 min, which ensures enough time for the two adhesive components to form the bond.  
Although preliminary, the results of this paper seem promising. The visual detection was extensively tested and reliably tracks the blob position. The adaptive impedance controller is capable of maintaining the required force. Even though there are some oscillations and settling times in the force response, in practical use, it does not make much difference since the curing time of the adhesive is at least several minutes. The trajectory planner was augmented to plan in the force space which allows for setting the force reference step change before the contact. The simulation results show the high repeatability of the overall system which gives us the confidence to perform experiments in a real-world environment.  
Our first step in future work was to perform experiments in a controlled laboratory environment. The outdoor environment poses a different set of challenges including the lower accuracy positioning system and unpredictable disturbances, i.e., wind gusts. Since these factors will inevitably reflect on the overall end-effector accuracy, we are looking into augmenting the manipulator to be able to compensate for lateral movements, as well as roll and yaw angles. To further increase the system's accuracy, the developed visual  
Figure 12: (**Left**): box and whiskers plot of the dot product between the blob plane normal vector \(\mathbf{r}_{t}\) and the end-effector orientation vector \(\mathbf{r}_{oe}\); and (**Right**): box and whiskers plot of the distance from the target point after contact.  
tracker will be used to improve feedback around the tracked blob on the bridge wall in real-world experiments.  
All authors contributed equally to this work. Conceptualization, M.O. and I.D.; methodology, A.I., L.M. and M.C.; software, A.I., L.M. and M.C.; investigation, A.I.; writing--original draft preparation, A.I.; writing--review and editing, L.M., I.D. and M.O.; supervision, I.D. and M.O.; project administration, I.D. and M.O.; funding acquisition I.D. and M.O. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the European Commission Horizon 2020 Programme through the project under G.A. number 820434, named Energy-Aware BIM Cloud Platform in a Cost-Effective Building Renovation Context--ENCORE. Furthermore, this research was part of the scientific project Autonomous System for Assessment and Prediction of Infrastructure Integrity (ASAP), financed by the European Union through the European Regional Development Fund--The Competitiveness and Cohesion Operational Programme (KK.01.1.1.04.0041).  
Not applicable.  
The authors declare no conflict of interest.