非线性混合系统最优动力学规划的快速探索随机可达集树

A. Wu, Sadra Sadraddini, Russ Tedrake
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引用次数: 17

摘要

我们介绍了R3T,一种基于可达性的快速探索随机树(RRT)算法的变体,适用于非线性和混合系统的(最优)动力学规划。我们开发了使用多面体近似可达集的工具,并使用它们执行基于采样的规划。该方法在混合系统中具有独特的优势:可达集中的不同动态模式可以用多个多面体显式表示。我们证明了在温和的假设下,R3T在动力学系统中是概率完全的,并且通过重新布线证明了R3T是渐近最优的。此外,R3T为非线性系统的可达性分析提供了一种形式化的验证方法。通过对非线性、混合和接触丰富的机器人系统的案例研究,证明了R3T的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
R3T: Rapidly-exploring Random Reachable Set Tree for Optimal Kinodynamic Planning of Nonlinear Hybrid Systems
We introduce R3T, a reachability-based variant of the rapidly-exploring random tree (RRT) algorithm that is suitable for (optimal) kinodynamic planning in nonlinear and hybrid systems. We developed tools to approximate reachable sets using polytopes and perform sampling-based planning with them. This method has a unique advantage in hybrid systems: different dynamic modes in the reachable set can be explicitly represented using multiple polytopes. We prove that under mild assumptions, R3T is probabilistically complete in kinodynamic systems, and asymptotically optimal through rewiring. Moreover, R3T provides a formal verification method for reachability analysis of nonlinear systems. The advantages of R3T are demonstrated with case studies on nonlinear, hybrid, and contact-rich robotic systems.
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