Route planning for volcanic ash and radioactivity in-situ drone measurements using a genetic algorithm and kriging

Kurzfassung

This study presents a methodology for optimizing route planning for Unmanned Aerial Systems (UAS) conducting in-situ measurements of volcanic ash and radioactivity. Utilizing a genetic algorithm combined with Kriging, this approach aims to enhance the optimization of measurement routes for improved in-situ data collection in contaminated airspaces. The genetic algorithm optimizes routes to minimize the mean absolute error of Kriging estimates against reference data while adhering to mission constraints. An ICON-ART atmospheric model system provides the propagation information for the contamination scenario serving as the basis for route optimization. Results demonstrate the effectiveness of the approach by flexible but shorter measurement routes comparing against a standard coverage pattern. This study contributes to the advancement of UAS path planning for environmental monitoring and disaster response.