# Adaptive Path Planning for UAV-Based Pollution Sampling
## 6 Conclusions  
This research focused on adaptive path planning for UAV-based pollution sampling in a restricted environment, inspired by the LEADER project. The UAV model autonomously traversed virtual regions with potential pollution, measuring the pollutant intensity and distribution. This problem served as the basis for establishing the model-based Adaptive Path Planner (APP), which includes both a Global Path Planner (GPP) and a Local Path Planner (LPP).  
Using a series of Model-in-the-Loop simulations, the planner was optimized, calibrated, and validated. The verification study showcased key strengths, such as adaptability, while also identifying certain limitations, including high result variability, particularly in the LPP component. An updated version of the APP was also successfully deployed on a real small fixed-wing UAV and validated via a Hardware-in-the-Loop test.  
Separating the APP into two distinct algorithms enabled precise tuning of each to meet its specific role. The GPP is responsible for planning an optimized global path, while the LPP quickly generates a feasible \"good enough\" path when needed. This fallback path does not need to be energy-optimal, serving instead as a practical solution within time constraints.  
The APP, including both the GPP and LPP, was successfully validated across two real-world-inspired measurement mission scenarios. The APP algorithms demonstrated success rates varying between 83.33% and 100%, depending on the scenario, generally delivering near-optimal solutions. While some paths were suboptimal, this is not critical since the APP is intended as a guidance tool for human operators for mission planning. The APP system generates energy-efficient, collision-free paths for UAVs, rivaling human-created paths in quality. It excels in scenarios involving short distances, low altitude changes, and minimal wind interference. However, its performance diminishes as wind speeds approach the UAV's velocity or when path complexity and length increase, occasionally failing to match the efficiency of human planning. Hence, to deploy the APP in a fully autonomous setting, further algorithm enhancements, as outlined in the Section 5, would be necessary.  
Conceptualization, M.K., P.P., and W.P.; methodology, M.K. and P.P.; software, M.K.; validation, M.K.; formal analysis, M.K. and P.P.; investigation, M.K.; resources, P.P. and W.P.; data curation, M.K.; writing--original draft preparation, M.K.; writing--review and editing, P.P. and W.P.; visualization, M.K.; supervision, P.P. and W.P.; project administration, P.P.; funding acquisition, P.P. All authors have read and agreed to the published version of the manuscript.  
The research presented in the paper was conducted and funded by the project NOR/POLNOR/LEPolUAV/0066/2019-00. Most of the research was realized as part of the first author's PhD thesis within \"Simulations in Engineering\" (SymIn) project (POWR.03.05.00-00.2098/17-00). This publication was funded by statutory funds of the Faculty of Mechanical Engineering, Silesian University of Technology.  
Not applicable.  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.