论文标题
增强重要性采样的安全性：强大MPPI的性能范围
Safety in Augmented Importance Sampling: Performance Bounds for Robust MPPI
论文作者
论文摘要
这项工作探讨了在安全受限的模型预测控制问题中增强重要性采样的性质。当在受约束的环境中运行时，基于抽样的模型预测控制和运动计划通常会使用惩罚功能或昂贵的基于优化的控制障碍算法来保持正向采样的可行性。相比之下，所提出的算法在提出的重要性采样中利用离散的嵌入式屏障状态，以对采样时的标称状态进行反馈。我们将证明，通过无碰撞轨迹的度量，在增强重要性采样中离散嵌入式屏障状态的安全性方法更有效，在计算上可以可行，并且每个样品的执行，并且在杂物导航任务上具有更好的安全性能，并具有极端未模块化的干扰。此外，我们将利用增强重要性采样和安全控制的理论特性来推导系统的自由能。
This work explores the nature of augmented importance sampling in safety-constrained model predictive control problems. When operating in a constrained environment, sampling based model predictive control and motion planning typically utilizes penalty functions or expensive optimization based control barrier algorithms to maintain feasibility of forward sampling. In contrast the presented algorithm utilizes discrete embedded barrier states in augmented importance sampling to apply feedback with respect to a nominal state when sampling. We will demonstrate that this approach of safety of discrete embedded barrier states in augmented importance sampling is more sample efficient by metric of collision free trajectories, is computationally feasible to perform per sample, and results in better safety performance on a cluttered navigation task with extreme un-modeled disturbances. In addition, we will utilize the theoretical properties of augmented importance sampling and safety control to derive a new bound on the free energy of the system.
