Multi-UAV Coordination for Uncertainty Suppression of Natural Disasters
Containing a wildfire requires an efficient response and persistent monitoring. A crucial aspect is the ability to search for the boundaries of the wildfire by exploring a wide area. However, even as wildfires are increasing today, the number of available monitoring systems that can provide support is decreasing, creating an operational gap and slow response in such urgent situations. Many natural and national security phenomena have a similar need for monitoring the propagating periphery of a hazardous substance or security threat.
The objective of this thesis is to estimate a propagating boundary and create a system that works in real time. It focuses on an autonomous system that suppresses uncertainty, investigating to what extent binary measurements away from the periphery can be used to predict the boundary and its spread rate. It proposes a coordination strategy with a new methodology for estimating the periphery of a propagating phenomenon using quantized observations. The method is tested in a simulation of an autonomous system with multiple unmanned aerial vehicle models that gather local measurements and share the information with a centralized ground control system. The estimate the system generates then incorporated into an allocation algorithm, weighing uncertainty and the rate of change of the uncertainty, reassign the UAV trajectory in order to suppress the uncertainty.
The complete system design, tested on the high-fidelity simulation, demonstrates that steering the vehicles towards the highest perpendicular uncertainty generates the best predictions. The results indicate that the new coordination scheme has a large beneficial impact on uncertainty suppression. By using the developed coordination algorithm and the adopted flight control system, the vehicles can follow the desired trajectory to reduce the uncertainty and the errors in predicting the periphery across a range of wind and terrain conditions.