Uncontrolled emissions of gases from industrial accidents and disasters
result in huge loss of life and property. Such extreme events require a quick
and reliable survey of the site for effective rescue strategy planning. To
achieve these goals, a network of unmanned aerial vehicles can be deployed that
survey the affected region and identify safe and danger zones. Although single
UAV-based systems for gas sensing applications are well-studied in literature,
research on the deployment of a UAV network for such applications, which is
more robust and fault tolerant, is still in infancy. The objective of this
project is to design a system that can be deployed in emergency situations to
provide a quick survey and identification of safe and dangerous zones in a
given region that contains a toxic plume without making any assumptions about
plume location. We focus on an end-to-end solution and formulate a two-phase
strategy that can not only guarantee detection/acquisition of plume but also
its characterization with high spatial resolution. To guarantee coverage of the
region with a certain spatial resolution, we set up a vehicle routing problem.
To overcome the limitations imposed by limited range of sensors and drone
resources, we estimate the concentration map by using Gaussian kernel
extrapolation. Finally, we evaluate the suggested framework in simulations. Our
results suggest that this two-phase strategy not only gives better error
performance but is also more efficient in terms of mission time. Moreover, the
comparison between 2-phase random search and 2-phase uniform coverage suggest
that the latter is better for single drone systems whereas for multiple drones
the former gives reasonable performance at low computational cost