基于鲁棒控制 Lyapunov 值函数的安全回归 FaSTrack
Safe Returning FaSTrack with Robust Control Lyapunov-Value Functions
AI总结：
本文提出融合 R-CLVF 与 FaSTrack 的 SR-F 框架，使四旋翼在未知扰动下安全恢复并通过规划模型跳跃加速，10D 实验显示其较原方法提速 20% 且保持安全。
摘要：
在先验未知环境中进行实时导航仍是一项具有挑战性的任务，尤其是在发生意外（未建模）扰动时。本文提出安全回归快速安全跟踪（Safe Returning Fast and Safe Tracking，SR-F）框架，融合了两方面概念：1）鲁棒控制 Lyapunov 值函数（Robust Control Lyapunov-Value Functions，R-CLVF）；2）快速安全跟踪（Fast and Safe Tracking，FaSTrack）框架。SR-F 在离线阶段计算真实系统模型与简化规划模型之间的 R-CLVF。在线阶段，使用规划算法在简化规划空间中生成轨迹，并利用 R-CLVF 提供一个跟踪控制器，使系统指数稳定到规划模型。当意外扰动发生时，所提出的 SR-F 算法为真实系统提供恢复到规划模型的途径。我们利用该机制，在开阔环境中通过“跳跃”规划模型引入人工扰动，从而迫使导航更快。因此，该算法既能抑制意外的真实扰动，又能提高导航速度。我们使用一个 10D 四旋翼系统验证该框架，结果表明 SR-F 在经验上比原始 FaSTrack 快 20%，同时保持安全性。
英文摘要：
Real-time navigation in a priori unknown environment remains a challenging task, especially when an unexpected (unmodeled) disturbance occurs. In this paper, we propose the framework Safe Returning Fast and Safe Tracking (SR-F) that merges concepts from 1) Robust Control Lyapunov-Value Functions (R-CLVF), and 2) the Fast and Safe Tracking (FaSTrack) framework. The SR-F computes an R-CLVF offline between a model of the true system and a simplified planning model. Online, a planning algorithm is used to generate a trajectory in the simplified planning space, and the R-CLVF is used to provide a tracking controller that exponentially stabilizes to the planning model. When an unexpected disturbance occurs, the proposed SR-F algorithm provides a means for the true system to recover to the planning model. We take advantage of this mechanism to induce an artificial disturbance by ``jumping'' the planning model in open environments, forcing faster navigation. Therefore, this algorithm can both reject unexpected true disturbances and accelerate navigation speed. We validate our framework using a 10D quadrotor system and show that SR-F is empirically 20\% faster than the original FaSTrack while maintaining safety.
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