Reach-avoid problems with time-varying dynamics, targets and constraints

J. Fisac, Mo Chen, C. Tomlin, S. Sastry
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引用次数: 200

Abstract

We consider a reach-avoid differential game, in which one of the players aims to steer the system into a target set without violating a set of state constraints, while the other player tries to prevent the first from succeeding; the system dynamics, target set, and state constraints may all be time-varying. The analysis of this problem plays an important role in collision avoidance, motion planning and aircraft control, among other applications. Previous methods for computing the guaranteed winning initial conditions and strategies for each player have either required augmenting the state vector to include time, or have been limited to problems with either no state constraints or entirely static targets, constraints and dynamics. To incorporate time-varying dynamics, targets and constraints without the need for state augmentation, we propose a modified Hamilton-Jacobi-Isaacs equation in the form of a double-obstacle variational inequality, and prove that the zero sublevel set of its viscosity solution characterizes the capture basin for the target under the state constraints. Through this formulation, our method can compute the capture basin and winning strategies for time-varying games at virtually no additional computational cost relative to the time-invariant case. We provide an implementation of this method based on well-known numerical schemes and show its convergence through a simple example; we include a second example in which our method substantially outperforms the state augmentation approach.
触及-避免具有时变动力学、目标和约束的问题
我们考虑一个达到-避免微分博弈,其中一个参与者的目标是在不违反一组状态约束的情况下引导系统进入目标集,而另一个参与者试图阻止第一个参与者成功;系统动力学、目标集和状态约束可能都是时变的。该问题的分析在避碰、运动规划和飞行器控制等应用中具有重要作用。以前计算每个玩家保证获胜的初始条件和策略的方法要么需要增加状态向量以包含时间,要么仅限于没有状态约束或完全静态目标、约束和动态的问题。为了在不需要状态增强的情况下结合时变动力学、目标和约束,我们提出了一种双障碍变分不等式形式的修正Hamilton-Jacobi-Isaacs方程,并证明了其粘度解的零子水平集表征了状态约束下目标的捕获池。通过这个公式,我们的方法可以计算时变博弈的捕获盆地和获胜策略,而相对于时不变的情况,几乎没有额外的计算成本。本文给出了一种基于知名数值格式的实现方法,并通过一个简单的算例说明了该方法的收敛性;我们包括第二个例子,其中我们的方法实质上优于状态增强方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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