EnCoMP: Enhanced Covert Maneuver Planning using Offline Reinforcement
  Learning
Jumman Hossain Abu-Zaher Faridee Nirmalya Roy
Cover navigation in complex environments is a critical challenge for
autonomous robots, requiring the identification and utilization of
environmental cover while maintaining efficient navigation. We propose an
enhanced navigation system that enables robots to identify and utilize natural
and artificial environmental features as cover, thereby minimizing exposure to
potential threats. Our perception pipeline leverages LiDAR data to generate
high-fidelity cover maps and potential threat maps, providing a comprehensive
understanding of the surrounding environment. We train an offline reinforcement
learning model using a diverse dataset collected from real-world environments,
learning a robust policy that evaluates the quality of candidate actions based
on their ability to maximize cover utilization, minimize exposure to threats,
and reach the goal efficiently. Extensive real-world experiments demonstrate
the superiority of our approach in terms of success rate, cover utilization,
exposure minimization, and navigation efficiency compared to state-of-the-art
methods.