考虑移动障碍物的不确定性，实现更安全高效的导航决策。
NAMOUnc: Navigation Among Movable Obstacles with Decision Making on Uncertainty Interval
- 将观测噪声等不确定性建模为时间成本区间，动态比较清除与绕行代价。
- 在仿真与真实实验中显著提升导航成功率与效率，优于现有方法。
- 适合需应对现实不确定性的自主导航系统研发人员参考。
移动障碍物中的导航（NAMO）是机器人领域关键任务，常受观测噪声、模型近似、动作失败和部分可观测性等现实不确定性挑战。现有方法多假设理想条件，导致决策次优或存在风险。本文提出NAMOUnc框架，将不确定性整合至决策过程：首先估计不确定性，再比较清除与绕行障碍物对应的时间成本区间，从而优化成功率与时间效率，确保更安全高效的导航。通过大量仿真与真实实验验证，本方法显著优于现有NAMO框架。更多细节见：https://kai-zhang-er.github.io/namo-uncertainty/
原文摘要 · Abstract (English)
Navigation among movable obstacles (NAMO) is a critical task in robotics, often challenged by real-world uncertainties such as observation noise, model approximations, action failures, and partial observability. Existing solutions frequently assume ideal conditions, leading to suboptimal or risky decisions. This paper introduces NAMOUnc, a novel framework designed to address these uncertainties by integrating them into the decision-making process. We first estimate them and compare the corresponding time cost intervals for removing and bypassing obstacles, optimizing both the success rate and time efficiency, ensuring safer and more efficient navigation. We validate our method through extensive simulations and real-world experiments, demonstrating significant improvements over existing NAMO frameworks. More details can be found in our website: https://kai-zhang-er.github.io/namo-uncertainty/
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