Optimality and robustness in path-planning under initial uncertainty
Optimality and robustness in path-planning under initial uncertainty
复制标题
初始不确定性下路径规划的最优性和鲁棒性
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
A. Vladimirsky
中科院分区：
文献类型：
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作者：
Dongping Qi;Adam Dhillon;A. Vladimirsky
经典的确定性最优控制问题假设被控过程的全部信息。一般部分可观测过程的控制理论是强大的，但这些方法在计算上是昂贵的，并且通常解决随机动力学和连续（直接不可观测）随机扰动的问题。在本文中，我们专注于路径规划问题之间的确定性，但与初始的不确定性的目标或运行成本的部分域。这种不确定性后来在某个时间T$被消除，目标是选择最佳轨迹，直到那时。我们解决这一挑战的三种不同的模型的信息采集：固定的$T$，离散分布和指数分布的随机$T$。我们开发了适用于多个最优性概念的模型和数值方法：基于平均情况下的性能，最坏情况下的性能，最坏情况下的平均约束，平均性能与不良结果的概率约束，风险敏感性和分布鲁棒性。我们说明我们的方法使用的例子，追求随机目标确定在一个（可能是随机的）稍后的时间$T$。
Classical deterministic optimal control problems assume full information about the controlled process. The theory of control for general partially-observable processes is powerful, but the methods are computationally expensive and typically address the problems with stochastic dynamics and continuous (directly unobserved) stochastic perturbations. In this paper we focus on path planning problems which are in between -- deterministic, but with an initial uncertainty on either the target or the running cost on parts of the domain. That uncertainty is later removed at some time $T$, and the goal is to choose the optimal trajectory until then. We address this challenge for three different models of information acquisition: with fixed $T$, discretely distributed and exponentially distributed random $T$. We develop models and numerical methods suitable for multiple notions of optimality: based on the average-case performance, the worst-case performance, the average constrained by the worst, the average performance with probabilistic constraints on the bad outcomes, risk-sensitivity, and distributional-robustness. We illustrate our approach using examples of pursuing random targets identified at a (possibly random) later time $T$.
具有概率约束的最佳停止
影响因子：
1.9
作者：
Palmer, Aaron Zeff;Vladimirsky, Alexander
通讯作者：
Vladimirsky, Alexander
交通信号不确定性下的最佳驾驶
影响因子：
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作者：
Gaspard, Mallory E.;Vladimirsky, Alexander
通讯作者：
Vladimirsky, Alexander