Autonomous robots that are capable of operating safely in the presence of imperfect model knowledge or external disturbances are vital in safety-critical applications. In this paper, we present a planner-agnostic framework to design and certify safe tubes around desired trajectories that the robot is always guaranteed to remain inside of. By leveraging recent results in contraction analysis and $\mathcal{L}_1$-adaptive control we synthesize an architecture that induces safe tubes for nonlinear systems with state and time-varying uncertainties. We demonstrate with a few illustrative examples how contraction theory-based $\mathcal{L}_1$-adaptive control can be used in conjunction with traditional motion planning algorithms to obtain provably safe trajectories.

中文翻译：

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安全反馈运动规划：一种收缩理论和 $\mathcal{L}_1$-基于自适应控制的方法

在存在不完善的模型知识或外部干扰的情况下能够安全运行的自主机器人在安全关键应用中至关重要。在本文中，我们提出了一个与规划者无关的框架来设计和认证围绕所需轨迹的安全管，机器人始终保证保持在其中。通过利用收缩分析和 $\mathcal{L}_1$ 自适应控制的最新结果，我们合成了一种架构，该架构为具有状态和时变不确定性的非线性系统引入安全管。我们通过一些说明性示例展示了基于收缩理论的 $\mathcal{L}_1$ 自适应控制如何与传统的运动规划算法结合使用，以获得可证明的安全轨迹。