# Coverage Path Planning with Semantic Segmentation for UAV in PV Plants
## 4 Conclusions  
In this work, a method for implementing CPP in UAV for PV plant inspection was presented. The method consisted of a series of steps, one of these was the deployment of a DL-based U-net model to establish a DL service system from which to extract the limits of PV plants by extracting the boundaries of PV plants from an image. To summarize, the method was accurate, and fast without depending on the image, with low request latency and response. This experiment focused on three novel path planning methods in the PV inspection missions in order to find the best path for covering each of the three PV power plants with less energy consumption. A GUI interface was used to order the UAV's maneuvers in the inspection of the simulated PV plants. The results of each CPP method in the simulation were compared. The best CPPs was the BECD, for a range of CPP widths of 0 to 7 m. These path planning algorithms can be performed by any multirotor UAV that receives Mavlink commands, can carry a camera sensor, and transmit real-time video to GCS. Performance on the CPP tasks was measured using two different types of flying robots, a Typhoon UAV and a 3 DR Iris UAV. It was demonstrated that the Typhoon UAV (or one with similar characteristics) is better suited for large or medium-sized PV plants (such as those in deserts, plains, and hills); instead, the drone-like 3 DR Iris UAV is more suitable for small-sized PV generation (such as on roofs and rooftops, canopies, and facades). The proposed strategy allows such comparisons to be made and enables the selection of the most suitable UAV for each type of installation. The values obtained for the metrics collected from each of the tests show a correlation between covered path length, flight time, number of maneuvers as regards energy consumption, and ensuring that the CPPs implemented in UAVs to inspect photovoltaic plants could be similar when implemented in real plants. The results also help to predict the energy consumption of a given UAV when performing a plant inspection. **Author Contributions:** Conceptualization, A.e.P.-G., N.B.-M., A.J.-D. and J.B.C.-Q. ; methodology, A.e.P.-G.; software, A.e.P.-G., N.B.-M.; validation, A.e.P.-G.; formal analysis, A.e.P.-G.; investigation, A.e.P.-G., N.B.-M.; resources, A.e.P.-G., A.J.-D. and J.B.C.-Q. ; data curation, A.e.P.-G.; writing-original draft preparation, A.e.P.-G.; writing-review and editing, A.e.P.-G., N.B.-M., A.J.-D. and J.B.C.-Q. ; visualization, A.e.P.-G.; supervision, A.J.-D. and J.B.C.-Q. ; project administration, A.e.P.-G., A.J.-D. and J.B.C.-Q. ; funding acquisition, A.e.P.-G., A.J.-D. and J.B.C.-Q. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Colombia Scientific Program within the framework of the so-called Ecosistema Cientifico (Contract No. FP44842-218-2018). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The models used in the computational experiment are available at GitHub in [100]. **Acknowledgments:** The authors gratefully acknowledge the support from the Colombia Scientific Program within the framework of the call Ecosistema Cientifico (Contract No. FP44842-218-2018). The authors also want to acknowledge Universidad de Antioquia for its support through the project \"estrategia de sostenibilidad\". **Conflicts of Interest:** The authors declare no conflict of interest. **Abbreviations**  
The following abbreviations are used in this manuscript: