We focus on the problem of long-range dynamic replanning for off-road autonomous vehicles, where a robot plans paths through a previously unobserved environment while continuously receiving noisy local observations. An effective approach for planning under sensing uncertainty is determinization, where one converts a stochastic world into a deterministic one and plans under this simplification. This makes the planning problem tractable, but the cost of following the planned path in the real world may be different than in the determinized world. This causes collisions if the determinized world optimistically ignores obstacles, or causes unnecessarily long routes if the determinized world pessimistically imagines more obstacles. We aim to be robust to uncertainty over potential worlds while still achieving the efficiency benefits of determinization. We evaluate algorithms for dynamic replanning on a large real-world dataset of challenging long-range planning problems from the DARPA RACER program. Our method, Dynamic Replanning via Evaluating and Aggregating Multiple Samples (DREAMS), outperforms other determinization-based approaches in terms of combined traversal time and collision cost. https://sites.google.com/cs.washington.edu/dreams/

翻译：我们聚焦于越野自动驾驶车辆的长距离动态重规划问题，其中机器人在持续接收含噪声局部观测的同时，需在未曾观测过的环境中规划路径。一种在感知不确定性下进行规划的有效方法是确定化——即将随机世界转化为确定性世界并在此简化条件下进行规划。这种方法使规划问题变得可解，但实际世界中沿规划路径行驶的成本可能与确定化世界存在偏差。若确定化世界过于乐观地忽略障碍物，将导致碰撞；若过于悲观地臆想更多障碍物，则会生成非必要长路线。我们的目标是在保持确定化方法效率优势的同时，增强对潜在世界不确定性的鲁棒性。我们利用DARPA RACER项目中具有挑战性的大规模长距离规划问题真实数据集，对动态重规划算法进行评估。本文提出的方法——通过评估与聚合多样本实现动态重规划（DREAMS），在综合行驶时间与碰撞成本指标上优于其他基于确定化的方法。https://sites.google.com/cs.washington.edu/dreams/