  Autonomous robots operating in complex environments face the critical
challenge of identifying and utilizing environmental cover for covert
navigation to minimize exposure to potential threats. We propose EnCoMP, an
enhanced navigation framework that integrates offline reinforcement learning
and our novel Adaptive Threat-Aware Visibility Estimation (ATAVE) algorithm to
enable robots to navigate covertly and efficiently in diverse outdoor settings.
ATAVE is a dynamic probabilistic threat modeling technique that we designed to
continuously assess and mitigate potential threats in real-time, enhancing the
robot's ability to navigate covertly by adapting to evolving environmental and
threat conditions. Moreover, our approach generates high-fidelity multi-map
representations, including cover maps, potential threat maps, height maps, and
goal maps from LiDAR point clouds, providing a comprehensive understanding of
the environment. These multi-maps offer detailed environmental insights,
helping in strategic navigation decisions. The goal map encodes the relative
distance and direction to the target location, guiding the robot's navigation.
We train a Conservative Q-Learning (CQL) model on a large-scale dataset
collected from real-world environments, learning a robust policy that maximizes
cover utilization, minimizes threat exposure, and maintains efficient
navigation. We demonstrate our method's capabilities on a physical Jackal
robot, showing extensive experiments across diverse terrains. These experiments
demonstrate EnCoMP's superior performance compared to state-of-the-art methods,
achieving a 95% success rate, 85% cover utilization, and reducing threat
exposure to 10.5%, while significantly outperforming baselines in navigation
efficiency and robustness.
