EnCoMP: Enhanced Covert Maneuver Planning with Adaptive Target-Aware Visibility Estimation using Offline Reinforcement Learning

Autonomous robots operating in complex environments face the critical challenge of identifying and utilizing environmental cover for covert navigation to minimize exposure to potentially harmful targets. We propose EnCoMP, an enhanced navigation framework that integrates offline reinforcement learning and our novel Adaptive Target-Aware Visibility Estimation (ATAVE) algorithm to enable robots to navigate covertly and efficiently in diverse outdoor settings. ATAVE is a dynamic probabilistic target modeling technique that we designed to continuously assess and mitigate potential targets in real-time, enhancing the robot’s ability to navigate covertly by adapting to evolving environmental and target conditions. Moreover, our approach generates high-fidelity multi-map representations, including cover maps, potential target 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 target 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 target exposure to $10.5 \%$, while significantly outperforming baselines in navigation efficiency and robustness.