  In this paper we present a cooperative multi-robot strategy to adaptively
explore and sample environments that are unfavorable for humans. We propose a
methodology for a team of heterogeneous robots to collaborate on information
based planning for applications like sampling thermal imagery in a wildfire
affected site to assist with detecting spot fires and areas of residual fires,
fire mapping and monitoring fire progression or applications in marine domain
for coral reef monitoring and survey. We use Gabor filter based texture
classifier on aerial images from an Unmanned Aerial Vehicle (UAV) to segment
the region of interest into classes. A policy gradient based path planner is
used on the texture classified aerial image to plan a path for the Unmanned
Ground Vehicle (UGV). The UGV then uses a local planner to reach the goals set
by the global planner by avoiding obstacles. The UGV also learns the labels for
the segmented classes as drivable and non-drivable using the feedback from the
performance while reaching the planned waypoints. We evaluated the building
blocks of our approach and present the results with application of these
strategies to different domains.
