Enhanced Covert Maneuver Planning using Offline Reinforcement Learning for Autonomous Robots in Complex Outdoor Environments

An enhanced covert navigation framework that leverages LiDAR data, height maps, cover maps, and potential threat maps, along with offline reinforcement learning, to enable autonomous robots to navigate efficiently while minimizing exposure to threats and maximizing cover utilization in complex outdoor environments.