# An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments
## 4 Conclusions  
This paper presents a path planning approach for resilient communication using UAV relays in MR applications. The key idea of the approach is to provide coverage to areas with poor signal propagation by relaying signals from the available cell-towers. This is performed using a two-stage framework that (a) narrows down the areas of interest that most likely require assistance, and (b) employs an energy-aware and weather-resilient path planning algorithm to relay connection to these areas. The Longley-Rice irregular terrain model was used to compute the pathloss and determine the points of interest. The optimal visiting sequence was then obtained by optimizing a traveling salesman problem, and a viewshed analysis was conducted to determine the mutual visibility region between the points of interest and the cell-towers. This information was used to create a blockage map that feeds an \(A^{*}\) algorithm to prevent the UAV from passing through no coverage areas. The results show that the proposed framework is capable of linking 1.7 more points of interest in comparison with classic methods that do not use the blockage map. The results also confirm that the framework enables the UAV to determine the optimal time to head back towards the charging station using an energy-aware decision-making process based on the throttle level throughout the path.  
Future work will consider the use of LIDAR data to provide more realistic and accurate viewshed analysis and path planning algorithms. Additionally, the frequency of transmission will be used to discriminate amongst other towers to improve the reliability of the proposed framework. The algorithm memory consumption will also be addressed in future work to uncover how the implemented algorithm affects the energy of the UAV.  
Conceptualization, M.E.D. and S.A.-R.; methodology, M.E.D. and A.P.; software M.E.D. and C.C.; validation, M.E.D. and J.A.F.-C.; formal analysis, A.P. and C.C.; investigation, M.E.D. and S.A.-R.; data curation, M.E.D. and C.C.; writing--original draft preparation, A.P. and J.A.F.-C.; supervision, S.A.-R. and A.P.; project administration, S.A.-R. and M.E.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement**: Publicly available datasets were analyzed in this study. These data can be found here: [[https://opencellid.org/downloads.php](https://opencellid.org/downloads.php)]([https://opencellid.org/downloads.php](https://opencellid.org/downloads.php)), [[http://www.gps-routes.co.uk/](http://www.gps-routes.co.uk/)]([http://www.gps-routes.co.uk/](http://www.gps-routes.co.uk/)), [[http://www.gps-routes.co.uk/routes/home.nsf/national-parks-walks/walking-in-the-lake-district](http://www.gps-routes.co.uk/routes/home.nsf/national-parks-walks/walking-in-the-lake-district)]([http://www.gps-routes.co.uk/routes/home.nsf/national-parks-walks/walking-in-the-lake-district](http://www.gps-routes.co.uk/routes/home.nsf/national-parks-walks/walking-in-the-lake-district)) (accessed on 3 March 2024).  
Figure 16: Function \(F(n)\) representing the difference between the estimated range and distance to the charging station.