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.

提出了一种增强型导航系统，使机器人能够识别和利用自然和人工的环境特征作为掩护，从而减少对潜在威胁的暴露，并以高效的方式进行导航。通过使用 LiDAR 数据生成高保真度的掩护地图和潜在威胁地图，我们的感知管道提供了对周围环境的全面理解。通过使用从真实环境中收集的多样化数据集训练离线强化学习模型，学习一个评估候选动作质量的强大策略，该策略基于其最大化掩护利用、最小化对威胁的暴露以及有效到达目标的能力。广泛的真实世界实验证明了我们的方法在成功率、掩护利用、暴露最小化和导航效率方面相对于最先进方法的优越性。