# Developing and Field Testing Path Planning for Robotic Aquaculture Water Quality Monitoring
## 3 Field Experiment of the Path Planning Techniques on the Fish Farm
### Experiment Setup  
The drones used to facilitate this study were the SwellPro SplashDrone 4, which are designed and manufactured in Shenzhen, China. They are IP67 waterproof drones meant for hobbyist photography and aquatic activities which include waterproof connectors for payload, camera and gimbal controls. Three drones were used to conduct our experiment. Attached to the underside of the drone is a housing for communication equipment and the winch release mechanism, see Figure 5. The winch holds the sensor payload, which is released into the pond upon landing on the surface of the water down to a depth of 2 m. Therefore, the system is capable of measuring the vertical DO distributions in the pond. The payload consists of a Seeed Studio XIAO nRF52840 Sense board which is used to collect DO, temperature and pressure data and send it over Bluetooth Low Energy wirelessly to an ESP32 Wi-Fi LoRa V2 board contained in the winch mechanism, where it is then forwarded  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ponds** & **HPP** & **GAM50** & **GAM100** & **GLOP** \\ \hline
50 & **0.03** & 0.94 & 1.03 & 748.79 \\ \hline
100 & **0.04** & 2.96 & 3.31 & 6091.04 \\ \hline
200 & **0.06** & 9.10 & 10.67 & 58,157.81 \\ \hline
300 & **0.09** & 22.31 & 28.76 & - \\ \hline
500 & **0.21** & 76.28 & 72.36 & - \\ \hline
700 & **0.21** & 151.22 & 162.92 & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average Run Time in Wind (ms).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ponds** & **HPP** & **GAM50** & **GAM100** & **GLOP** \\ \hline
50 & 8.69 & 9.28 & 10.69 & **7.017** \\ \hline
100 & 10.41 & 11.65 & 14.79 & **7.71** \\ \hline
200 & 13.99 & 16.99 & 24.02 & **8.95** \\ \hline
300 & **15.02** & 21.31 & 35.95 & - \\ \hline
500 & **9.10** & 32.16 & 63.57 & - \\over LoRa to an identical ESP32 board functioning as the base station. The base station receives the sensor data and uploads it to a cloud Firebase instance to automatically update an android app with the water quality status of each pond, see Figure 6 for the android app interface. Figure 7 describes the full system diagram.  
The SwellPro Software Development Kit (SDK) was used as the interface between the path planning methods and the flight controllers. The custom flight control software was developed in Python to load these routes into the drone and facilitate the communication between the drones and the base station. A laptop was connected to each of the drones'  
Figure 5: **(Top Left)** SwellPro SplashDrone 4 with orange payload visible underneath. **(Top Right)** Side view with the winch release mechanism and radio housing in black. **(Bottom Left)** Underside with the orange winch drum visible. **(Bottom Right)** Payload which contains BLE transmitter and DO, temperature and pressure sensors.  
Figure 6: Android app displaying water quality readings sent by the drone.  
handheld controllers via dual band 802.11 Wi-Fi, switching connections as necessary to send commands to each of the drones. Mission commands with the planned route coordinates and necessary takeoff and landing commands were sent over TCP/IP to each controller and were then forwarded to the respective drone over a proprietary SwellPro radio link. Once the mission plan was successfully received by the drone, no further radio communication was necessary for flight control, and they could fly out of radio range. The LoRa link to the base station is employed to upload the pond data once it was collected.