Priority Maps for Surveillance and Intervention of Wildfires and other Spreading Processes
This addresses the need for efficient UAV path planning in wildfire management, though it appears incremental as it builds on existing optimization methods for spreading processes.
The paper tackles the problem of creating priority maps for monitoring or intervening in dynamic spreading processes like wildfires, proposing an optimization framework that yields scalable algorithms and demonstrating results on examples with up to 1000 nodes.
Unmanned Aerial Vehicle (UAV) path planning algorithms often assume a knowledge reward function or priority map, indicating the most important areas to visit. In this paper we propose a method to create priority maps for monitoring or intervention of dynamic spreading processes such as wildfires. The presented optimization framework utilizes the properties of positive systems, in particular the separable structure of value (cost-to-go) functions, to provide scalable algorithms for surveillance and intervention. We present results obtained for a 16 and 1000 node example and convey how the priority map responds to changes in the dynamics of the system. The larger example of 1000 nodes, representing a fictional landscape, shows how the method can integrate bushfire spreading dynamics, landscape and wind conditions. Finally, we give an example of combining the proposed method with a travelling salesman problem for UAV path planning for wildfire intervention.