  Autonomous navigation in offroad environments has been extensively studied in
the robotics field. However, navigation in covert situations where an
autonomous vehicle needs to remain hidden from outside observers remains an
underexplored area. In this paper, we propose a novel Deep Reinforcement
Learning (DRL) based algorithm, called CoverNav, for identifying covert and
navigable trajectories with minimal cost in offroad terrains and jungle
environments in the presence of observers. CoverNav focuses on unmanned ground
vehicles seeking shelters and taking covers while safely navigating to a
predefined destination. Our proposed DRL method computes a local cost map that
helps distinguish which path will grant the maximal covertness while
maintaining a low cost trajectory using an elevation map generated from 3D
point cloud data, the robot's pose, and directed goal information. CoverNav
helps robot agents to learn the low elevation terrain using a reward function
while penalizing it proportionately when it experiences high elevation. If an
observer is spotted, CoverNav enables the robot to select natural obstacles
(e.g., rocks, houses, disabled vehicles, trees, etc.) and use them as shelters
to hide behind. We evaluate CoverNav using the Unity simulation environment and
show that it guarantees dynamically feasible velocities in the terrain when fed
with an elevation map generated by another DRL based navigation algorithm.
Additionally, we evaluate CoverNav's effectiveness in achieving a maximum goal
distance of 12 meters and its success rate in different elevation scenarios
with and without cover objects. We observe competitive performance comparable
to state of the art (SOTA) methods without compromising accuracy.
