This paper presents Robust samplE-based coVarIance StEering (REVISE), a multi-query algorithm that generates robust belief roadmaps for dynamic systems navigating through spatially dependent disturbances modeled as a Gaussian random field. Our proposed method develops a novel robust sample-based covariance steering edge controller to safely steer a robot between state distributions, satisfying state constraints along the trajectory. Our proposed approach also incorporates an edge rewiring step into the belief roadmap construction process, which provably improves the coverage of the belief roadmap. When compared to state-of-the-art methods, REVISE improves median plan accuracy (as measured by Wasserstein distance between the actual and planned final state distribution) by 10x in multi-query planning and reduces median plan cost (as measured by the largest eigenvalue of the planned state covariance at the goal) by 2.5x in single-query planning for a 6DoF system. We will release our code at https://acl.mit.edu/REVISE/.

翻译：本文提出鲁棒采样协方差引导算法（REVISE），这是一种多查询算法，可为在空间相关扰动（建模为高斯随机场）中导航的动态系统生成鲁棒置信度路线图。我们提出的方法开发了一种新颖的鲁棒采样协方差引导边缘控制器，用于在状态分布之间安全地引导机器人，同时满足沿轨迹的状态约束。该方法还将边缘重布线步骤纳入置信度路线图构建过程，可证明地提高了置信度路线图的覆盖范围。与最先进的方法相比，REVISE在6自由度系统的多查询规划中，将中位数规划精度（通过实际与规划最终状态分布之间的Wasserstein距离衡量）提高了10倍；在单查询规划中，将中位数规划成本（通过目标处规划状态协方差的最大特征值衡量）降低了2.5倍。我们的代码将在 https://acl.mit.edu/REVISE/ 发布。