提出新算法REVISE，让机器人在随机干扰中更安全地规划路径。
REVISE: Robust Probabilistic Motion Planning in a Gaussian Random Field
- 基于高斯随机场建模干扰，用采样控制优化路径鲁棒性。
- 多查询下路径精度提升10倍，单查询下目标状态协方差最大特征值降2.5倍。
- 适合高自由度系统在复杂干扰环境中的安全路径规划。
本文提出鲁棒采样式协方差导向算法REVISE，用于动态系统在空间相关干扰（建模为高斯随机场）下的多查询路径规划。该方法设计了一种新型鲁棒采样式协方差导向边控制器，可安全引导机器人在状态分布间移动，并满足轨迹上的状态约束。同时，在信念路图构建中引入边重连步骤，可证明提升路图覆盖范围。相比现有最优方法，对于6自由度系统，多查询规划中路径精度（以实际与计划终点状态分布的Wasserstein距离衡量）提升10倍；单查询规划中路径成本（以目标状态协方差最大特征值衡量）降低2.5倍。代码将公开于https://acl.mit.edu/REVISE/。
原文摘要 · Abstract (English)
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/.
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