# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 8 Validation
### Cloud Services Support  
This use case demonstrates interactions between a drone and cloud services. The first part of the demonstration involves mission planning, execution, and monitoring using both a simulated and a real drone. The experiment is conducted at Hans Christian Andersen Airport outside Odense in Denmark where the test setup consisting of a power line connecting two power towers is located (Figure 20).  
For experimental preparation, power tower locations were added to the graph structure as described in Section 5.1. The drone reports its location, which is visualized on the web interface, by sending HTTPS requests to the cloud. The user of the web interface plans the mission by choosing the target power tower and calculating the route to reach it as shown in Figure 21a. After the mission is planned, the user allows the drone to fetch the mission, and the drone starts executing the global plan by flying to the waypoints contained  
Figure 19: Images from real-time fault detection with an NVIDIA Jetson NX onboard computer. The two images represent different camera angles.  
Figure 20: Drone testing setup at Hans Christian Andersen Airport in Odense, Denmark.  
Figure 18: Realtime onboard fault detection by using the deep learning algorithm on Nvidia AGX NX.  
in it. The drone onboard control software is developed to follow received waypoints with an offset to avoid getting too close to the infrastructure. The drone continues reporting its location throughout the mission and the movement is visualized on the web interface allowing the user to monitor the mission's progress. Mission monitoring is visualized at the beginning of the mission execution in Figure 21b and the end in Figure 21c.  
The second part of the demonstration involves image transfer from the drone to the cloud. Two inspection scenarios could benefit from capturing and transferring images during the mission; the first scenario is where the fault detection algorithm is running on the drone's onboard computer transferring detected faults through the cloud to the user. In the second scenario, the drone captures and transfers images to the cloud where they are stored and can be used for different types of analysis or three-dimensional reconstruction. When an image is received by the cloud, a pop-up button appears at the web interface alerting the user that a fault has been detected, and the user can immediately see it. The image is then stored in the object storage and can be retrieved after the mission upon user request through the search option offered on the web interface.  
The demonstration video (Video S3) shows the interactions between the cloud services and a single drone described in this section. The communication is successfully established in both directions, from the cloud to the drone by sending mission waypoints and from the drone to the cloud by reporting the drone location as well as infrastructure images. After accomplishing the simulated flight, we proceeded to the demonstration with a real vehicle. Although the test was successful, we experienced issues related to the wind that posed flight difficulties to the drone, and it was not flying as precisely as in the simulation. To tackle the aforementioned challenge, future work regarding cloud services would consider a weather service providing a real-time weather forecast for locations contained in the mission plan. Furthermore, drones should be equipped with the onboard controller adapting the flight trajectory for wind disturbances. Another encountered issue was data loss due to the interrupted connection.