Evasive Path Planning Under Surveillance Uncertainty
Evasive Path Planning Under Surveillance Uncertainty
复制标题
监视不确定性下的规避路径规划
DOI：
10.1007/s13235-019-00327-x
复制
发表时间：
2020
影响因子：
1.5
通讯作者：
Vladimirsky, Alexander
中科院分区：
文献类型：
--
作者：
Gilles, Marc Aurèle;Vladimirsky, Alexander
最优控制理论的经典设置假设完全了解过程动态以及与每个控制策略相关的成本。如果控制器只知道可能的运行成本函数的有限集合，但无法检查这些运行成本中的哪一个实际上是适当的，则问题变得更加困难。在本文中，我们解决这一挑战的一类规避路径规划问题的连续域，在逃避者需要达到一个目标，同时尽量减少他的暴露给敌人的观察员，谁又是从一个有限的一组已知的监视计划选择。我们的关键假设是，逃避者和观察者都需要提前承诺他们的（可能是概率性的）策略，并且不能根据任何新发现的关于对手当前位置的信息立即改变他们的行动。我们考虑两种类型的逃避者行为：在第一种情况下，一个完全规避风险的逃避者寻求一条最小化他的最坏情况累积可观测性的轨迹，在第二种情况下，逃避者关心的是最小化平均情况累积可观测性。后一个版本自然被解释为一个半无限的战略博弈，我们提供了一个有效的方法来近似其纳什均衡。所提出的方法借鉴了博弈论，凸优化，最优控制和多目标动态规划的方法。我们用数值例子说明我们的算法，并讨论了计算复杂性，包括多个逃避者的广义版本。
The classical setting of optimal control theory assumes full knowledge of the process dynamics and the costs associated with every control strategy. The problem becomes much harder if the controller only knows a finite set of possible running cost functions, but has no way of checking which of these running costs is actually in place. In this paper we address this challenge for a class of evasive path planning problems on a continuous domain, in which an evader needs to reach a target while minimizing his exposure to an enemy observer, who is in turn selecting from a finite set of known surveillance plans. Our key assumption is that both the evader and the observer need to commit to their (possibly probabilistic) strategies in advance and cannot immediately change their actions based on any newly discovered information about the opponent’s current position. We consider two types of evader behavior: in the first one, a completely risk-averse evader seeks a trajectory minimizing hisworst-casecumulative observability, and in the second, the evader is concerned with minimizing theaverage-casecumulative observability. The latter version is naturally interpreted as a semi-infinite strategic game, and we provide an efficient method for approximating its Nash equilibrium. The proposed approach draws on methods from game theory, convex optimization, optimal control, and multiobjective dynamic programming. We illustrate our algorithm using numerical examples and discuss the computational complexity, including for the generalized version with multiple evaders.
登录
查看更多内容
DOI：
10.1109/cdc40024.2019.9029329
发表时间：
2019
期刊：
2019 IEEE 58th Conference on Decision and Control (CDC
影响因子：
--
作者：
Cartee, Elliot;Lai, Lexiao;Song, Qianli;Vladimirsky, Alexander
通讯作者：
Vladimirsky, Alexander
影响因子：
1.9
作者：
LEWIN, J;BREAKWELL, JV
通讯作者：
BREAKWELL, JV
影响因子：
2.2
作者：
Desilles, Anna;Zidani, Hasnaa
通讯作者：
Zidani, Hasnaa
具有多重约束的连续路径规划
DOI：
10.1109/cdc.2003.1272513
发表时间：
2003
期刊：
42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475)
影响因子：
--
作者：
Ian M. Mitchell;S. Sastry
通讯作者：
S. Sastry
圆锥形监视规避
DOI：
--
发表时间：
1979
期刊：
影响因子：
--
作者：
J. Lewin;G. Olsder
通讯作者：
G. Olsder