EnCoMP：基于离线强化学习和自适应威胁感知可见性估计的增强型隐蔽机动规划
EnCoMP: Enhanced Covert Maneuver Planning with Adaptive Threat-Aware Visibility Estimation using Offline Reinforcement Learning
AI总结：
EnCoMP提出一种结合离线强化学习与自适应威胁感知可见性估计的导航框架，利用多地图表示和CQL策略，在真实机器人上实现95%成功率、85%掩体利用率及10.5%威胁暴露，显著提升隐蔽导航性能。
摘要：
在复杂环境中运行的自主机器人面临着一个关键挑战，即识别和利用环境中的掩体进行隐蔽导航，以最大限度地减少对潜在威胁的暴露。我们提出了EnCoMP，一个增强型导航框架，它集成了离线强化学习和我们新颖的自适应威胁感知可见性估计（ATAVE）算法，使机器人能够在多样化的户外环境中隐蔽且高效地导航。ATAVE是一种动态概率威胁建模技术，我们设计它用于实时持续评估和缓解潜在威胁，通过适应不断变化的环境和威胁条件来增强机器人隐蔽导航的能力。此外，我们的方法从LiDAR点云生成高保真多地图表示，包括掩体图、潜在威胁图、高度图和目标图，提供了对环境的全面理解。这些多地图提供了详细的环境洞察，有助于战略性导航决策。目标图编码了到目标位置的相对距离和方向，引导机器人的导航。我们在从真实世界环境收集的大规模数据集上训练了一个保守Q学习（CQL）模型，学习了一个最大化掩体利用、最小化威胁暴露并保持高效导航的稳健策略。我们在物理Jackal机器人上展示了我们方法的能力，在多样地形上进行了广泛的实验。这些实验表明，EnCoMP相比最先进的方法具有优越性能，实现了95%的成功率、85%的掩体利用率和将威胁暴露降低至10.5%，同时在导航效率和鲁棒性方面显著优于基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
